Method and device for beamforming in wireless communication system

Abstract

An operation method of a first node in a wireless communication system according to an embodiment of the present disclosure may comprise the steps of: transmitting, to a second node, terminal capability information including information about a sensor included in the first node; transmitting, to the second node, first location information of the first node when the first node enters a specific area; receiving, from the second node, a correction table for the sensor; obtaining an offset value for the sensor on the basis of correction information and a preconfigured calibration offset table; obtaining a first measurement value through the sensor; obtaining a final prediction value for the sensor on the basis of the offset value for the sensor and the first measurement value; and performing at least one of beam prediction and beam tracking on the basis of the final prediction value.

Description

Beamforming method and device in a wireless communication system

[0001] The present disclosure relates to a wireless communication system. Specifically, the present disclosure relates to a beamforming method and device in a wireless communication system.

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

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

[0004] In order to solve the above-described problems, the technical challenge of the present disclosure is to provide a beamforming method and device in a wireless communication system that can minimize the influence of external environmental factors that cause errors in beam operation and sensor measurement.

[0005] In order to solve the above-described problem, the technical task of the present disclosure is to provide a beamforming method and device in a wireless communication system, which can minimize error covariance and thereby increase the stability and accuracy of beam tracking when a terminal operates a prediction algorithm such as an extended Kalman filter (EKF).

[0006] The technical problems to be achieved in the present disclosure are not limited to the technical problems mentioned above, and other technical problems not mentioned will be clearly understood by a person having ordinary skill in the technical field to which the present disclosure belongs from the description below.

[0007] In a wireless communication system according to one embodiment of the present disclosure, a method for operating a first node includes the steps of: transmitting terminal capability information including information about a sensor included in the first node to a second node; transmitting first location information of the first node to the second node when the first node enters a specific area; receiving a calibration table about the sensor from the second node; obtaining an offset value about the sensor based on the calibration information and a preset calibration offset table; obtaining a first measurement value through the sensor; obtaining a final prediction value about the sensor based on the offset value about the sensor and the first measurement value; and performing at least one of beam prediction and beam tracking based on the final prediction value.

[0008] The above correction table can be generated based on factors related to the type of the sensor and the measurement accuracy of the sensor.

[0009] The step of receiving a correction table for the sensor from the second node may include a step of receiving, from the second node, an RRC (Radio resource control) message, an RRC reconfiguration message, or DCI (downlink control information) including the correction table through a third node.

[0010] The step of obtaining the offset value for the sensor based on the above correction table and the preset correction offset table may include the step of selecting one of a plurality of correction values ​​included in the correction table and the step of obtaining an offset value related to the selected correction value among the plurality of offset values ​​included in the correction offset table as the offset value for the sensor.

[0011] The step of obtaining a final predicted value for the sensor based on the offset value and the first measurement value for the sensor may be performed based on a Kalman filter or an extended Kalman filter.

[0012] The method may further include a step of obtaining a prior prediction value for the sensor and a step of calculating a Kalman gain based on the prior prediction value for the sensor.

[0013] The step of obtaining a final prediction value for the sensor based on the offset value and the first measurement value for the sensor may include the step of obtaining the final prediction value for the sensor based on the offset value, the first measurement value, and the Kalman gain for the sensor.

[0014] When the first node leaves the specific area, a step of transmitting second location information of the first node to the second node, a step of receiving a correction information utilization off message including information regarding use of the correction information from the second node,

[0015] The method may further include a step of obtaining a second measurement value through the sensor and a step of performing at least one of the beam prediction or the beam tracking based on the second measurement value.

[0016] A first node operating in a communication system according to one embodiment of the present disclosure may include one or more transceivers, one or more processors controlling the one or more transceivers, and a memory including one or more instructions to be executed by the one or more processors, wherein the one or more instructions may include: a step of transmitting terminal capability information including information about a sensor included in the first node to a second node; a step of transmitting first location information of the first node to the second node when the first node enters a specific area; a step of receiving a correction table about the sensor from the second node; a step of obtaining an offset value about the sensor based on the correction information and a preset calibration offset table; a step of obtaining a first measurement value through the sensor; a step of obtaining a final prediction value about the sensor based on the offset value about the sensor and the first measurement value; and a step of performing at least one of beam prediction or beam tracking based on the final prediction value.

[0017] In a wireless communication system according to one embodiment of the present disclosure, a method for operating a first node may include the steps of: receiving terminal capability information including information about a sensor included in the second node from a second node; transmitting first location information of the second node from the second node; determining whether the second node has entered a specific area based on the first location information; selecting a correction table for the sensor from among a plurality of correction tables based on the terminal capability information when it is determined that the second node has entered the specific area; and transmitting the correction table for the sensor to the second node.

[0018] The above correction table can be generated based on factors related to the type of the sensor and the measurement accuracy of the sensor.

[0019] The step of transmitting the correction table regarding the sensor to the second node may include the step of transmitting, to the second node, an RRC (Radio resource control) message, an RRC reconfiguration message, or DCI (downlink control information) including the correction table through a third node.

[0020] The method may include a step of receiving second location information of the second node from the second node, a step of determining whether the second node has left the specific area based on the second location information, and a step of transmitting a correction information utilization off message including information regarding use of the correction table to the second node when the second node has left the specific area.

[0021] A first node operating in a communication system according to one embodiment of the present disclosure may include one or more transceivers, one or more processors controlling the one or more transceivers, and a memory including one or more instructions to be executed by the one or more processors, wherein the one or more instructions may include: receiving terminal capability information including information about a sensor included in the second node from a second node; transmitting first location information of the second node from the second node; determining whether the second node has entered a specific area based on the first location information; selecting a correction table for the sensor from among a plurality of correction tables based on the terminal capability information when it is determined that the second node has entered the specific area; and transmitting the correction table for the sensor to the second node.

[0022] In one embodiment of the present disclosure, a first device including one or more memories and one or more processors functionally connected to the one or more memories, wherein the one or more processors are operable to cause the first device to transmit terminal capability information including information about a sensor included in the first device to a second device, transmit first location information of the first device to the second device when the first device enters a specific area, receive a calibration table about the sensor from the second device, obtain an offset value about the sensor based on the calibration information and a preset calibration offset table, obtain a first measurement value through the sensor, obtain a final prediction value about the sensor based on the offset value about the sensor and the first measurement value, and perform at least one of beam prediction or beam tracking based on the final prediction value.

[0023] One or more non-transitory computer-readable media storing one or more commands according to one embodiment of the present disclosure, the instructions being operable to transmit terminal capability information including information about a sensor included in a first device to a second device, transmit first location information of the first device to the second device when the first device enters a specific area, receive a calibration table about the sensor from the second device, obtain an offset value about the sensor based on the calibration information and a preset calibration offset table, obtain a first measurement value through the sensor, obtain a final prediction value about the sensor based on the offset value about the sensor and the first measurement value, and perform at least one of beam prediction or beam tracking based on the final prediction value.

[0024] According to the present disclosure, a terminal can minimize an error in a sensor measurement value based on compensation information transmitted by a base station, thereby enabling rapid beam tracking in the THz band.

[0025] According to the present disclosure, it is possible to implement an ultra-massive MIMO technology that compensates for the physical attenuation characteristics of the THz band and utilizes beam spatial gain.

[0026] The accompanying drawings are intended to aid in understanding the present disclosure and, together with detailed descriptions, may provide embodiments of the present disclosure. However, the technical features of the present disclosure are not limited to specific drawings, and the features disclosed in each drawing may be combined with each other to form new embodiments. Reference numerals in each drawing may indicate structural elements.

[0027] Figure 1 is a drawing showing an example of a communication system applicable to this specification.

[0028] Figure 2 is a drawing showing an example of a wireless device applicable to this specification.

[0029] FIG. 3 is a diagram illustrating a method for processing a transmission signal applicable to the present specification.

[0030] FIG. 4 is a drawing showing another example of a wireless device applicable to this specification.

[0031] FIG. 5 is a drawing showing an example of a mobile device applicable to this specification.

[0032] Figure 6 is a diagram showing physical channels applicable to this specification and a signal transmission method using them.

[0033] Figure 7 is a diagram showing the structure of a wireless frame applicable to this specification.

[0034] Figure 8 is a drawing showing a slot structure applicable to this specification.

[0035] FIG. 9 is a diagram showing an example of a communication structure that can be provided in a 6G system applicable to this specification.

[0036] Figure 10 shows an example of a perceptron structure.

[0037] Figure 11 shows an example of a multilayer perceptron structure.

[0038] Figure 12 shows an example of a deep neural network.

[0039] Figure 13 shows an example of a convolutional neural network.

[0040] Figure 14 is a diagram showing an example of a filter operation in a convolutional neural network.

[0041] Figure 15 shows an example of a neural network structure in which a recurrent loop exists.

[0042] Figure 16 shows an example of the operating structure of a recurrent neural network.

[0043] Figure 17 is a diagram showing an electromagnetic spectrum applicable to this specification.

[0044] Fig. 18 is a drawing showing a THz communication method applicable to this specification.

[0045] FIG. 19 is a diagram illustrating a THz wireless communication transceiver applicable to the present specification.

[0046] Fig. 20 is a drawing showing a THz signal generation method applicable to the present specification.

[0047] Figure 21 is a drawing showing a wireless communication transceiver applicable to this specification.

[0048] Figure 22 is a drawing showing a transmitter structure applicable to this specification.

[0049] Fig. 23 is a drawing showing a modulator structure applicable to this specification.

[0050] Figure 24 is a flowchart explaining the operation method of the Kalman filter.

[0051] Figures 25 and 26 are conceptual diagrams for explaining the influence of environmental factors in a wireless communication system.

[0052] Figure 27 is a flowchart illustrating the initial connection procedure of a terminal in a wireless communication system.

[0053] Figures 28 to 31 are conceptual diagrams illustrating examples of sensors used for beam prediction-based technology operation.

[0054] FIG. 32 is a flowchart of an initial connection method according to one embodiment of the present disclosure.

[0055] FIG. 33 is a flowchart between a terminal and a core network according to one embodiment of the present disclosure.

[0056] Figures 34 to 40 are conceptual diagrams of a correction table according to one embodiment of the present disclosure.

[0057] Figure 41 is a conceptual diagram of a correction offset table according to one embodiment of the present disclosure.

[0058] FIG. 42 is a flowchart between a terminal and a core network according to another embodiment of the present disclosure.

[0059] Figure 43 is a conceptual diagram illustrating the effect of a method according to one embodiment of the present invention.

[0060] Figure 44 is a conceptual diagram illustrating the effect of a method according to one embodiment of the present invention when a terminal operates a geomagnetic sensor.

[0061] Figure 45 is a flowchart of a signal transmission and reception method according to one embodiment of the present disclosure.

[0062] Figure 46 is a flowchart of a signal transmission and reception method according to another embodiment of the present disclosure.

[0063] The following embodiments combine the components and features of this specification in a predetermined form. Each component or feature may be considered optional unless explicitly stated otherwise. Each component or feature may be implemented without being combined with other components or features. Furthermore, some components and / or features may be combined to form embodiments of this specification. The order of operations described in the embodiments of this specification may be changed. Some components or features of one embodiment may be included in another embodiment or may be replaced with corresponding components or features of another embodiment.

[0064] In the description of the drawings, procedures or steps that may obscure the gist of the present specification are not described, and procedures or steps that can be understood by a person skilled in the art are also not described.

[0065] Throughout the specification, when a part is said to "comprising" (or including) a certain component, this does not mean that other components are excluded, but rather that other components can be included, unless specifically stated otherwise. In addition, terms such as "...part," "...unit," and "module" described in the specification mean a unit that processes at least one function or operation, which may be implemented by hardware, software, or a combination of hardware and software. In addition, the words "a" or "an," "one," "the," and similar related words may be used in the context of describing this specification (especially in the context of the claims below) to include both the singular and the plural, unless otherwise indicated herein or clearly contradicted by context.

[0066] The embodiments of this specification have been described with a focus on the data transmission and reception relationship between a base station and a mobile station. Here, the base station is understood as a terminal node of a network that directly communicates with the mobile station. Certain operations described herein as being performed by the base station may, in some cases, be performed by an upper node of the base station.

[0067] That is, in a network consisting of multiple network nodes including a base station, various operations performed for communication with a mobile station may be performed by the base station or other network nodes other than the base station. In this case, the term 'base station' may be replaced by terms such as fixed station, Node B, eNB (eNode B), gNB (gNode B), ng-eNB, advanced base station (ABS), or access point.

[0068] Additionally, in the embodiments of the present specification, the term terminal may be replaced with terms such as user equipment (UE), mobile station (MS), subscriber station (SS), mobile subscriber station (MSS), mobile terminal, or advanced mobile station (AMS).

[0069] Additionally, a transmitter refers to a fixed and / or mobile node that provides data or voice services, and a receiver refers to a fixed and / or mobile node that receives data or voice services. Therefore, for uplink, a mobile station can be the transmitter, and a base station can be the receiver. Similarly, for downlink, a mobile station can be the receiver, and a base station can be the transmitter.

[0070] Embodiments of the present specification may be supported by standard documents disclosed in at least one of wireless access systems, such as IEEE 802.xx system, 3rd Generation Partnership Project (3GPP) system, 3GPP Long Term Evolution (LTE) system, 3GPP 5G (5th generation) NR (New Radio) system and 3GPP2 system, and in particular, embodiments of the present specification may be supported by 3GPP TS (technical specification) 38.211, 3GPP TS 38.212, 3GPP TS 38.213, 3GPP TS 38.321 and 3GPP TS 38.331 documents.

[0071] Furthermore, the embodiments of this specification may be applied to other wireless access systems and are not limited to the aforementioned systems. For example, they may also be applicable to systems implemented after the 3GPP 5G NR system, and are not limited to a specific system.

[0072] That is, obvious steps or parts not described in the embodiments of this specification may be explained by reference to the above documents. In addition, all terms disclosed in this specification may be explained by the above standard documents.

[0073] Hereinafter, preferred embodiments according to the present specification will be described in detail with reference to the accompanying drawings. The detailed description set forth below, together with the accompanying drawings, is intended to illustrate exemplary embodiments of the present specification and is not intended to represent the only embodiments in which the technical components of the present specification may be implemented.

[0074] Additionally, specific terms used in the embodiments of this specification are provided to aid in understanding of this specification, and the use of these specific terms may be changed to other forms without departing from the technical spirit of this specification.

[0075] The following technology can be applied to various wireless access systems such as CDMA (code division multiple access), FDMA (frequency division multiple access), TDMA (time division multiple access), OFDMA (orthogonal frequency division multiple access), and SC-FDMA (single carrier frequency division multiple access).

[0076] In order to make the following description clear, the following description is based on a 3GPP communication system (e.g., LTE, NR, etc.), but the technical idea of ​​the present invention is not limited thereto. LTE may refer to technology after 3GPP TS 36.xxx Release 8. Specifically, LTE technology after 3GPP TS 36.xxx Release 10 may be referred to as LTE-A, and LTE technology after 3GPP TS 36.xxx Release 13 may be referred to as LTE-A pro. 3GPP NR may refer to technology after TS 38.xxx Release 15. 3GPP 6G may refer to technology after TS Release 17 and / or Release 18. "xxx" refers to a standard document detail number. LTE / NR / 6G may be collectively referred to as a 3GPP system.

[0077] For background information, terms, abbreviations, etc. used in this specification, reference may be made to standard documents published prior to the invention of the present invention. For example, reference may be made to the 36.xxx and 38.xxx standard documents.

[0078] Communication systems applicable to this specification

[0079] Although not limited thereto, the various descriptions, functions, procedures, proposals, methods and / or operational flowcharts disclosed herein may be applied to various fields requiring wireless communication / connectivity (e.g., 5G) between devices.

[0080] Hereinafter, more specific examples will be provided with reference to the drawings. In the drawings / descriptions below, the same drawing reference numerals may represent identical or corresponding hardware blocks, software blocks, or functional blocks, unless otherwise described.

[0081] FIG. 1 is a diagram illustrating an example of a communication system applicable to the present specification. Referring to FIG. 1, a communication system (100) applicable to the present specification includes a wireless device, a base station, and a network. Here, a wireless device refers to a device that performs communication using a wireless access technology (e.g., 5G NR, LTE) and may be referred to as a communication / wireless / 5G device. Although not limited thereto, the wireless device may include a robot (100a), a vehicle (100b-1, 100b-2), an XR (extended reality) device (100c), a hand-held device (100d), a home appliance (100e), an IoT (Internet of Things) device (100f), and an AI (artificial intelligence) device / server (100g). For example, the vehicle may include a vehicle equipped with a wireless communication function, an autonomous vehicle, a vehicle capable of performing vehicle-to-vehicle communication, etc. Here, the vehicles (100b-1, 100b-2) may include unmanned aerial vehicles (UAVs) (e.g., drones). The XR devices (100c) include augmented reality (AR) / virtual reality (VR) / mixed reality (MR) devices, and may be implemented in the form of head-mounted devices (HMDs), head-up displays (HUDs) installed in vehicles, televisions, smartphones, computers, wearable devices, home appliances, digital signage, vehicles, robots, etc. The portable devices (100d) may include smartphones, smart pads, wearable devices (e.g., smartwatches, smart glasses), computers (e.g., laptops, etc.), etc. The home appliances (100e) may include TVs, refrigerators, washing machines, etc. The IoT devices (100f) may include sensors, smart meters, etc.For example, the base station (120) and the network (130) may also be implemented as wireless devices, and a specific wireless device (120a) may act as a base station / network node to other wireless devices.

[0082] Wireless devices (100a to 100f) can be connected to a network (130) via a base station (120). AI technology can be applied to the wireless devices (100a to 100f), and the wireless devices (100a to 100f) can be connected to an AI server (100g) via a network (130). The network (130) can be configured using a 3G network, a 4G (e.g., LTE) network, a 5G (e.g., NR) network, etc. The wireless devices (100a to 100f) can communicate with each other via the base station (120) / network (130), but can also communicate directly (e.g., sidelink communication) without going through the base station (120) / network (130). For example, vehicles (100b-1, 100b-2) can communicate directly (e.g., V2V (vehicle to vehicle) / V2X (vehicle to everything) communication). In addition, IoT devices (100f) (e.g., sensors) can communicate directly with other IoT devices (e.g., sensors) or other wireless devices (100a to 100f).

[0083] Wireless communication / connection (150a, 150b, 150c) can be established between wireless devices (100a to 100f) / base stations (120), and base stations (120) / base stations (120). Here, the wireless communication / connection can be established through various wireless access technologies (e.g., 5G NR) such as uplink / downlink communication (150a), sidelink communication (150b) (or D2D communication), and communication between base stations (150c) (e.g., relay, IAB (integrated access backhaul)). Through the wireless communication / connection (150a, 150b, 150c), the wireless device and base station / wireless device, and base stations and base stations can transmit / receive wireless signals to / from each other. For example, the wireless communication / connection (150a, 150b, 150c) can transmit / receive signals through various physical channels. To this end, at least some of the various configuration information setting processes for transmitting / receiving wireless signals, various signal processing processes (e.g., channel encoding / decoding, modulation / demodulation, resource mapping / demapping, etc.), and resource allocation processes may be performed based on various proposals of this specification.

[0084] Communication systems applicable to this specification

[0085] FIG. 2 is a diagram illustrating an example of a wireless device applicable to this specification.

[0086] Referring to FIG. 2, the first wireless device (200a) and the second wireless device (200b) can transmit and receive wireless signals via various wireless access technologies (e.g., LTE, NR). Here, {the first wireless device (200a), the second wireless device (200b)} can correspond to {the wireless device (100x), the base station (120)} and / or {the wireless device (100x), the wireless device (100x)} of FIG. 1.

[0087] A first wireless device (200a) includes one or more processors (202a) and one or more memories (204a), and may further include one or more transceivers (206a) and / or one or more antennas (208a). The processor (202a) controls the memories (204a) and / or the transceivers (206a), and may be configured to implement the descriptions, functions, procedures, proposals, methods, and / or operational flowcharts disclosed herein. For example, the processor (202a) may process information in the memory (204a) to generate first information / signals, and then transmit a wireless signal including the first information / signals via the transceivers (206a). In addition, the processor (202a) may receive a wireless signal including second information / signals via the transceivers (206a), and then store information obtained from signal processing of the second information / signals in the memory (204a). The memory (204a) may be connected to the processor (202a) and may store various information related to the operation of the processor (202a). For example, the memory (204a) may perform some or all of the processes controlled by the processor (202a), or may store software code including instructions for performing the descriptions, functions, procedures, proposals, methods, and / or operational flowcharts disclosed herein. Here, the processor (202a) and the memory (204a) may be part of a communication modem / circuit / chip designed to implement wireless communication technology (e.g., LTE, NR). The transceiver (206a) may be connected to the processor (202a) and may transmit and / or receive wireless signals via one or more antennas (208a). The transceiver (206a) may include a transmitter and / or a receiver. The transceiver (206a) may be used interchangeably with an RF (radio frequency) unit. In this specification, wireless device may also mean a communication modem / circuit / chip.

[0088] The second wireless device (200b) includes one or more processors (202b), one or more memories (204b), and may further include one or more transceivers (206b) and / or one or more antennas (208b). The processor (202b) controls the memories (204b) and / or the transceivers (206b), and may be configured to implement the descriptions, functions, procedures, proposals, methods, and / or operational flowcharts disclosed herein. For example, the processor (202b) may process information in the memory (204b) to generate third information / signals, and then transmit a wireless signal including the third information / signals via the transceivers (206b). In addition, the processor (202b) may receive a wireless signal including fourth information / signals via the transceivers (206b), and then store information obtained from signal processing of the fourth information / signals in the memory (204b). The memory (204b) may be connected to the processor (202b) and may store various information related to the operation of the processor (202b). For example, the memory (204b) may perform some or all of the processes controlled by the processor (202b), or may store software code including instructions for performing the descriptions, functions, procedures, proposals, methods, and / or operational flowcharts disclosed herein. Here, the processor (202b) and the memory (204b) may be part of a communication modem / circuit / chip designed to implement wireless communication technology (e.g., LTE, NR). The transceiver (206b) may be connected to the processor (202b) and may transmit and / or receive wireless signals via one or more antennas (208b). The transceiver (206b) may include a transmitter and / or a receiver. The transceiver (206b) may be used interchangeably with an RF unit. In this specification, wireless device may also mean a communication modem / circuit / chip.

[0089] Hereinafter, hardware elements of the wireless device (200a, 200b) will be described in more detail. Although not limited thereto, one or more protocol layers may be implemented by one or more processors (202a, 202b). For example, one or more processors (202a, 202b) may implement one or more layers (e.g., functional layers such as physical (PHY), media access control (MAC), radio link control (RLC), packet data convergence protocol (PDCP), radio resource control (RRC), and service data adaptation protocol (SDAP)). One or more processors (202a, 202b) may generate one or more Protocol Data Units (PDUs) and / or one or more Service Data Units (SDUs) according to the descriptions, functions, procedures, proposals, methods, and / or operational flowcharts disclosed herein. One or more processors (202a, 202b) may generate messages, control information, data or information according to the descriptions, functions, procedures, proposals, methods and / or operational flowcharts disclosed herein. One or more processors (202a, 202b) may generate signals (e.g., baseband signals) including PDUs, SDUs, messages, control information, data or information according to the functions, procedures, proposals and / or methods disclosed herein and provide the signals to one or more transceivers (206a, 206b). One or more processors (202a, 202b) may receive signals (e.g., baseband signals) from one or more transceivers (206a, 206b) and obtain PDUs, SDUs, messages, control information, data or information according to the descriptions, functions, procedures, proposals, methods and / or operational flowcharts disclosed herein.

[0090] One or more processors (202a, 202b) may be referred to as a controller, a microcontroller, a microprocessor, or a microcomputer. One or more processors (202a, 202b) may be implemented by hardware, firmware, software, or a combination thereof. For example, one or more application specific integrated circuits (ASICs), one or more digital signal processors (DSPs), one or more digital signal processing devices (DSPDs), one or more programmable logic devices (PLDs), or one or more field programmable gate arrays (FPGAs) may be included in one or more processors (202a, 202b). The descriptions, functions, procedures, proposals, methods, and / or operational flowcharts disclosed herein may be implemented using firmware or software, and the firmware or software may be implemented to include modules, procedures, functions, etc. The descriptions, functions, procedures, suggestions, methods and / or operation flowcharts disclosed in this specification may be implemented using firmware or software configured to perform one or more processors (202a, 202b) or stored in one or more memories (204a, 204b) and executed by one or more processors (202a, 202b). The descriptions, functions, procedures, suggestions, methods and / or operation flowcharts disclosed in this specification may be implemented using firmware or software in the form of codes, instructions and / or sets of instructions.

[0091] One or more memories (204a, 204b) may be coupled to one or more processors (202a, 202b) and may store various forms of data, signals, messages, information, programs, codes, instructions, and / or commands. The one or more memories (204a, 204b) may be configured as read only memory (ROM), random access memory (RAM), erasable programmable read only memory (EPROM), flash memory, hard drives, registers, cache memory, computer readable storage media, and / or combinations thereof. The one or more memories (204a, 204b) may be located internally and / or externally to the one or more processors (202a, 202b). Additionally, the one or more memories (204a, 204b) may be coupled to the one or more processors (202a, 202b) via various technologies, such as wired or wireless connections.

[0092] One or more transceivers (206a, 206b) can transmit user data, control information, wireless signals / channels, etc., as mentioned in the methods and / or flowcharts of this specification, to one or more other devices. One or more transceivers (206a, 206b) can receive user data, control information, wireless signals / channels, etc., as mentioned in the descriptions, functions, procedures, proposals, methods and / or flowcharts of this specification, from one or more other devices. For example, one or more transceivers (206a, 206b) can be coupled to one or more processors (202a, 202b) and can transmit and receive wireless signals. For example, one or more processors (202a, 202b) can control one or more transceivers (206a, 206b) to transmit user data, control information, or wireless signals to one or more other devices. Additionally, one or more processors (202a, 202b) may control one or more transceivers (206a, 206b) to receive user data, control information, or wireless signals from one or more other devices. Additionally, one or more transceivers (206a, 206b) may be coupled to one or more antennas (208a, 208b), and one or more transceivers (206a, 206b) may be configured to transmit and receive user data, control information, wireless signals / channels, or the like, as referred to in the descriptions, functions, procedures, proposals, methods, and / or operational flowcharts disclosed herein, via one or more antennas (208a, 208b). In the present specification, one or more antennas may be multiple physical antennas or multiple logical antennas (e.g., antenna ports). One or more transceivers (206a, 206b) can convert received user data, control information, wireless signals / channels, etc. from RF band signals to baseband signals in order to process the received user data, control information, wireless signals / channels, etc. using one or more processors (202a, 202b).One or more transceivers (206a, 206b) may convert user data, control information, wireless signals / channels, etc. processed by one or more processors (202a, 202b) from baseband signals to RF band signals. For this purpose, one or more transceivers (206a, 206b) may include an (analog) oscillator and / or filter.

[0093] FIG. 3 is a diagram illustrating a method for processing a transmission signal applied to the present specification. For example, the transmission signal may be processed by a signal processing circuit. At this time, the signal processing circuit (300) may include a scrambler (310), a modulator (320), a layer mapper (330), a precoder (340), a resource mapper (350), and a signal generator (360). At this time, as an example, the operations / functions of FIG. 3 may be performed in the processors (202a, 202b) and / or the transceivers (206a, 206b) of FIG. 2. Furthermore, as an example, the hardware elements of FIG. 3 may be implemented in the processors (202a, 202b) and / or the transceivers (206a, 206b) of FIG. 2. For example, blocks 310 to 350 may be implemented in the processor (202a, 202b) of FIG. 2, and block 360 may be implemented in the transceiver (206a, 206b) of FIG. 2, and are not limited to the above-described embodiments.

[0094] The codeword can be converted into a wireless signal through the signal processing circuit (300) of FIG. 3. Here, the codeword is an encoded bit sequence of an information block. The information block may include a transport block (e.g., a UL-SCH transport block, a DL-SCH transport block). The wireless signal may be transmitted through various physical channels (e.g., a PUSCH, a PDSCH) of FIG. 6. Specifically, the codeword can be converted into a bit sequence scrambled by a scrambler (310). The scramble sequence used for scrambling is generated based on an initialization value, and the initialization value may include ID information of the wireless device, etc. The scrambled bit sequence can be modulated into a modulation symbol sequence by a modulator (320). The modulation scheme may include pi / 2-binary phase shift keying (pi / 2-BPSK), m-phase shift keying (m-PSK), m-quadrature amplitude modulation (m-QAM), etc.

[0095] A complex modulation symbol sequence can be mapped to one or more transmission layers by a layer mapper (330). The modulation symbols of each transmission layer can be mapped to the corresponding antenna port(s) by a precoder (340) (precoding). The output z of the precoder (340) can be obtained by multiplying the output y of the layer mapper (330) by an N*M precoding matrix W. Here, N is the number of antenna ports, and M is the number of transmission layers. Here, the precoder (340) can perform precoding after performing transform precoding (e.g., discrete Fourier transform (DFT) transform) on the complex modulation symbols. In addition, the precoder (340) can perform precoding without performing transform precoding.

[0096] The resource mapper (350) can map modulation symbols of each antenna port to time-frequency resources. The time-frequency resources can include multiple symbols (e.g., CP-OFDMA symbols, DFT-s-OFDMA symbols) in the time domain and multiple subcarriers in the frequency domain. The signal generator (360) generates a wireless signal from the mapped modulation symbols, and the generated wireless signal can be transmitted to another device through each antenna. To this end, the signal generator (360) can include an inverse fast Fourier transform (IFFT) module, a cyclic prefix (CP) inserter, a digital-to-analog converter (DAC), a frequency uplink converter, and the like.

[0097] The signal processing process for receiving signals in a wireless device can be configured in reverse order of the signal processing process (310-360) of FIG. 3. For example, a wireless device (e.g., 200a, 200b of FIG. 2) can receive wireless signals from the outside through an antenna port / transceiver. The received wireless signals can be converted into baseband signals through a signal restorer. For this purpose, the signal restorer can include a frequency downlink converter, an analog-to-digital converter (ADC), a CP remover, and a fast Fourier transform (FFT) module. Thereafter, the baseband signal can be restored to a codeword through a resource demapper process, a postcoding process, a demodulation process, and a descrambling process. The codewords can be restored to the original information blocks through decoding. Accordingly, a signal processing circuit (not shown) for a received signal may include a signal restorer, a resource de-mapper, a postcoder, a demodulator, a de-scrambler, and a decoder.

[0098] Wireless device structure applicable to this specification

[0099] FIG. 4 is a diagram illustrating another example of a wireless device to which the present specification applies.

[0100] Referring to FIG. 4, the wireless device (400) corresponds to the wireless devices (200a, 200b) of FIG. 2 and may be composed of various elements, components, units, and / or modules. For example, the wireless device (400) may include a communication unit (410), a control unit (420), a memory unit (430), and additional elements (440). The communication unit may include a communication circuit (412) and a transceiver(s) (414). For example, the communication circuit (412) may include one or more processors (202a, 202b) and / or one or more memories (204a, 204b) of FIG. 2. For example, the transceiver(s) (414) may include one or more transceivers (206a, 206b) and / or one or more antennas (208a, 208b) of FIG. 2. The control unit (420) is electrically connected to the communication unit (410), the memory unit (430), and the additional elements (440) and controls the overall operation of the wireless device. For example, the control unit (420) may control the electrical / mechanical operation of the wireless device based on the program / code / command / information stored in the memory unit (430). In addition, the control unit (420) may transmit information stored in the memory unit (430) to an external device (e.g., another communication device) via a wireless / wired interface through the communication unit (410), or store information received from an external device (e.g., another communication device) via a wireless / wired interface in the memory unit (430).

[0101] The additional element (440) may be configured in various ways depending on the type of the wireless device. For example, the additional element (440) may include at least one of a power unit / battery, an input / output unit, a driving unit, and a computing unit. Although not limited thereto, the wireless device (400) may be implemented in the form of a robot (Fig. 1, 100a), a vehicle (Fig. 1, 100b-1, 100b-2), an XR device (Fig. 1, 100c), a portable device (Fig. 1, 100d), a home appliance (Fig. 1, 100e), an IoT device (Fig. 1, 100f), a digital broadcasting terminal, a hologram device, a public safety device, an MTC device, a medical device, a fintech device (or a financial device), a security device, a climate / environmental device, an AI server / device (Fig. 1, 140), a base station (Fig. 1, 120), a network node, etc. Wireless devices may be mobile or stationary depending on the use / service.

[0102] In FIG. 4, various elements, components, units / parts, and / or modules within the wireless device (400) may be entirely interconnected via a wired interface, or at least some may be wirelessly connected via a communication unit (410). For example, within the wireless device (400), the control unit (420) and the communication unit (410) may be wired, and the control unit (420) and a first unit (e.g., 430, 440) may be wirelessly connected via the communication unit (410). In addition, each element, component, unit / part, and / or module within the wireless device (400) may further include one or more elements. For example, the control unit (420) may be composed of a set of one or more processors. For example, the control unit (420) may be composed of a set of a communication control processor, an application processor, an electronic control unit (ECU), a graphics processing processor, a memory control processor, etc. As another example, the memory unit (430) may be composed of RAM, DRAM (dynamic RAM), ROM, flash memory, volatile memory, non-volatile memory, and / or a combination thereof.

[0103] Mobile devices to which this specification applies

[0104] FIG. 5 is a drawing illustrating an example of a mobile device to which the present specification applies.

[0105] Figure 5 illustrates an example of a mobile device applicable to the present specification. The mobile device may include a smartphone, a smart pad, a wearable device (e.g., a smart watch, smart glasses), or a portable computer (e.g., a laptop, etc.). The mobile device may be referred to as a mobile station (MS), a user terminal (UT), a mobile subscriber station (MSS), a subscriber station (SS), an advanced mobile station (AMS), or a wireless terminal (WT).

[0106] Referring to FIG. 5, the portable device (500) may include an antenna unit (508), a communication unit (510), a control unit (520), a memory unit (530), a power supply unit (540a), an interface unit (540b), and an input / output unit (540c). The antenna unit (508) may be configured as a part of the communication unit (510). Blocks 510 to 530 / 540a to 540c correspond to blocks 410 to 430 / 440 of FIG. 4, respectively.

[0107] The communication unit (510) can transmit and receive signals (e.g., data, control signals, etc.) with other wireless devices and base stations. The control unit (520) can control components of the portable device (500) to perform various operations. The control unit (520) can include an AP (application processor). The memory unit (530) can store data / parameters / programs / codes / commands required for operating the portable device (500). In addition, the memory unit (530) can store input / output data / information, etc. The power supply unit (540a) supplies power to the portable device (500) and can include a wired / wireless charging circuit, a battery, etc. The interface unit (540b) can support connection between the portable device (500) and other external devices. The interface unit (540b) can include various ports (e.g., audio input / output ports, video input / output ports) for connection with external devices. The input / output unit (540c) can input or output video information / signals, audio information / signals, data, and / or information input from a user. The input / output unit (540c) may include a camera, a microphone, a user input unit, a display unit (540d), a speaker, and / or a haptic module.

[0108] For example, in the case of data communication, the input / output unit (540c) obtains information / signals (e.g., touch, text, voice, image, video) input by the user, and the obtained information / signals can be stored in the memory unit (530). The communication unit (510) can convert the information / signals stored in the memory into wireless signals, and transmit the converted wireless signals directly to other wireless devices or to a base station. In addition, the communication unit (510) can receive wireless signals from other wireless devices or base stations, and then restore the received wireless signals to the original information / signals. The restored information / signals can be stored in the memory unit (530) and then output in various forms (e.g., text, voice, image, video, haptic) through the input / output unit (540c).

[0109] Physical channels and general signal transmission

[0110] In a wireless access system, a terminal can receive information from a base station via the downlink (DL) and transmit it to the base station via the uplink (UL). The information transmitted and received between the base station and the terminal includes general data and various control information, and various physical channels exist depending on the type and purpose of the information being transmitted and received.

[0111] FIG. 6 is a diagram illustrating physical channels applicable to this specification and a signal transmission method using them.

[0112] When a terminal is powered on again from a powered-off state or newly enters a cell, it performs an initial cell search operation, such as synchronizing with the base station, in step S611. To this end, the terminal receives a primary synchronization channel (P-SCH) and a secondary synchronization channel (S-SCH) from the base station to synchronize with the base station and obtain information such as the cell ID.

[0113] After that, the terminal can obtain broadcast information within the cell by receiving a physical broadcast channel (PBCH) signal from the base station. Meanwhile, the terminal can check the downlink channel status by receiving a downlink reference signal (DL RS) in the initial cell search phase. After completing the initial cell search, the terminal can obtain more specific system information by receiving a physical downlink control channel (PDCCH) and a physical downlink shared channel (PDSCH) based on the physical downlink control channel information in step S612.

[0114] Thereafter, the terminal may perform a random access procedure such as steps S613 to S616 to complete connection to the base station. To this end, the terminal may transmit a preamble through a physical random access channel (PRACH) (S613) and receive a random access response (RAR) for the preamble through a physical downlink control channel and a physical downlink shared channel corresponding thereto (S614). The terminal may transmit a physical uplink shared channel (PUSCH) using scheduling information in the RAR (S615) and perform a contention resolution procedure such as receiving a physical downlink control channel signal and a physical downlink shared channel signal corresponding thereto (S616).

[0115] A terminal that has performed the procedure described above can then perform reception of a physical downlink control channel signal and / or a physical downlink shared channel signal (S617) and transmission of a physical uplink shared channel (PUSCH) signal and / or a physical uplink control channel (PUCCH) signal (S618) as a general uplink / downlink signal transmission procedure.

[0116] Control information transmitted from a terminal to a base station is collectively referred to as uplink control information (UCI). UCI includes hybrid automatic repeat and request acknowledgment / negative ACK (HARQ-ACK / NACK), scheduling request (SR), channel quality indication (CQI), precoding matrix indication (PMI), rank indication (RI), and beam indication (BI) information. UCI is generally transmitted periodically through PUCCH, but depending on the embodiment (e.g., when control information and traffic data must be transmitted simultaneously), it may be transmitted through PUSCH. In addition, the terminal may transmit UCI aperiodically through PUSCH upon request / instruction from the network.

[0117] Figure 7 is a diagram illustrating the structure of a wireless frame applicable to this specification.

[0118] Uplink and downlink transmissions based on the NR system can be based on frames such as those in FIG. 7. At this time, one radio frame has a length of 10 ms and can be defined as two 5 ms half-frames (HF). One half-frame can be defined as five 1 ms subframes (SF). One subframe is divided into one or more slots, and the number of slots within a subframe can depend on the subcarrier spacing (SCS). At this time, each slot can contain 12 or 14 OFDM (A) symbols depending on the cyclic prefix (CP). When a normal CP is used, each slot can contain 14 symbols. When an extended CP is used, each slot can contain 12 symbols. Here, the symbol may include an OFDM symbol (or CP-OFDM symbol), an SC-FDMA symbol (or DFT-s-OFDM symbol).

[0119] Table 1 shows the number of symbols per slot, the number of slots per frame, and the number of slots per subframe according to SCS when a general CP is used, and Table 2 shows the number of symbols per slot, the number of slots per frame, and the number of slots per subframe according to SCS when an extended CP is used.

[0120]

[0121]

[0122] In Table 1 and Table 2 above, N slot symb represents the number of symbols in the slot, and N frame,μ slot represents the number of slots in the frame, and N subframe,μ slot can indicate the number of slots within a subframe.

[0123] Additionally, in a system to which the present specification is applicable, OFDM(A) numerologies (e.g., SCS, CP length, etc.) may be set differently between multiple cells merged into a single terminal. Accordingly, the (absolute time) interval of a time resource (e.g., SF, slot, or TTI) (conveniently referred to as TU (time unit)) consisting of the same number of symbols may be set differently between the merged cells.

[0124] NR can support multiple numerologies (or subcarrier spacing (SCS)) to support various 5G services. For example, a 15 kHz SCS supports wide areas in traditional cellular bands; a 30 kHz / 60 kHz SCS supports dense urban areas, lower latency, and wider carrier bandwidth; and a 60 kHz or higher SCS can support bandwidths greater than 24.25 GHz to overcome phase noise.

[0125] The NR frequency band is defined by two types of frequency ranges (FR1 and FR2). FR1 and FR2 can be configured as shown in the table below. FR2 can also refer to millimeter wave (mmW).

[0126]

[0127] In addition, as an example, the numerology described above may be set differently in a communication system to which the present specification is applicable. For example, a terahertz wave (THz) band may be used as a frequency band higher than the FR2 described above. In the THz band, the SCS may be set to be larger than in the NR system, and the number of slots may also be set differently, and is not limited to the above-described embodiment. The THz band will be described later.

[0128] Figure 8 is a drawing illustrating a slot structure applicable to this specification.

[0129] A single slot contains multiple symbols in the time domain. For example, a slot contains seven symbols in a regular CP, but a slot may contain six symbols in an extended CP. A carrier contains multiple subcarriers in the frequency domain. A Resource Block (RB) can be defined as multiple (e.g., 12) consecutive subcarriers in the frequency domain.

[0130] Additionally, a Bandwidth Part (BWP) is defined as multiple consecutive (P)RBs in the frequency domain, and can correspond to one numerology (e.g., SCS, CP length, etc.).

[0131] A carrier can contain up to N (e.g., 5) BWPs. Data communication is performed through an activated BWP, and only one BWP can be activated per terminal. Each element in the resource grid is referred to as a Resource Element (RE), to which a single complex symbol can be mapped.

[0132] 6G communication system

[0133] The 6G (wireless communication) system aims to achieve (i) very high data rates per device, (ii) a very large number of connected devices, (iii) global connectivity, (iv) very low latency, (v) low energy consumption for battery-free IoT devices, (vi) ultra-reliable connectivity, and (vii) connected intelligence with machine learning capabilities. The vision of the 6G system can be divided into four aspects: "intelligent connectivity," "deep connectivity," "holographic connectivity," and "ubiquitous connectivity," and the 6G system can satisfy the requirements as shown in Table 4 below. In other words, Table 4 is a table showing the requirements of the 6G system.

[0134]

[0135] At this time, the 6G system may have key factors such as enhanced mobile broadband (eMBB), ultra-reliable low latency communications (URLLC), massive machine type communications (mMTC), AI integrated communication, tactile internet, high throughput, high network capacity, high energy efficiency, low backhaul and access network congestion, and enhanced data security.

[0136] FIG. 9 is a diagram illustrating an example of a communication structure that can be provided in a 6G system applicable to this specification.

[0137] Referring to Figure 9, 6G systems are expected to have 50 times higher simultaneous wireless connectivity than 5G wireless systems. URLLC, a key feature of 5G, is expected to become a more prominent technology in 6G communications by providing end-to-end latency of less than 1 ms. Furthermore, 6G systems will have significantly better volumetric spectral efficiency than the commonly used area spectral efficiency. 6G systems can offer extremely long battery life and advanced battery technologies for energy harvesting, eliminating the need for separate charging for mobile devices in 6G systems. Furthermore, new network characteristics in 6G may include:

[0138] - Satellite integrated network: 6G is expected to integrate with satellites to provide a global mobile network. The integration of terrestrial, satellite, and airborne networks into a single wireless communications system could be crucial for 6G.

[0139] Connected Intelligence: Unlike previous generations of wireless communication systems, 6G is revolutionary, upgrading the wireless evolution from "connected objects" to "connected intelligence." AI can be applied at every stage of the communication process (or at every signal processing step, as described below).

[0140] - 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.

[0141] - Ubiquitous super 3-dimension connectivity: Access to networks and core network functions from drones and very low Earth orbit satellites will create super 3-dimension connectivity in 6G ubiquitous.

[0142] Some general requirements for the new network characteristics of 6G, such as the above, may be as follows:

[0143] - Small cell networks: The concept of small cell networks was introduced to improve received signal quality in cellular systems by increasing throughput, energy efficiency, and spectral efficiency. Consequently, small cell networks are essential for 5G and beyond-5G (5GB) communication systems. Accordingly, 6G communication systems also adopt the characteristics of small cell networks.

[0144] Ultra-dense heterogeneous networks: Ultra-dense heterogeneous networks will be another key feature of 6G communication systems. Multi-tier networks comprised of heterogeneous networks improve overall QoS and reduce costs.

[0145] High-capacity backhaul: Backhaul connections are characterized by high-capacity backhaul networks to support high-volume traffic. High-speed fiber optics and free-space optics (FSO) systems may be potential solutions to this problem.

[0146] - Radar technology integrated with mobile technology: High-precision localization (or location-based services) through communications is a key feature of 6G wireless communication systems. Therefore, radar systems will be integrated with 6G networks.

[0147] - Softwarization and virtualization: Softwarization and virtualization are two critical features that form the foundation of the design process for 5GB networks to ensure flexibility, reconfigurability, and programmability. Furthermore, billions of devices can be shared on a shared physical infrastructure.

[0148] Core implementation technology of 6G systems

[0149] - Artificial Intelligence (AI)

[0150] The most crucial and newly introduced technology for 6G systems is AI. 4G systems did not involve AI. 5G systems will support partial or very limited AI. However, 6G systems will fully support AI for automation. Advances in machine learning will create more intelligent networks for real-time communications in 6G. Incorporating AI into communications can streamline and improve real-time data transmission. AI can use numerous analyses to determine how complex target tasks should be performed. In other words, AI can increase efficiency and reduce processing delays.

[0151] Time-consuming tasks such as handover, network selection, and resource scheduling can be performed instantly using AI. AI can also play a crucial role in machine-to-machine (M2M), machine-to-human, and human-to-machine communications. Furthermore, AI can facilitate rapid communication in brain-computer interfaces (BCIs). AI-based communication systems can be supported by metamaterials, intelligent structures, intelligent networks, intelligent devices, intelligent cognitive radios, self-sustaining wireless networks, and machine learning.

[0152] Recent attempts to integrate AI into wireless communication systems have focused on the application layer and network layer, particularly deep learning in wireless resource management and allocation. However, this research is increasingly evolving to the MAC layer and physical layer, with attempts to combine deep learning with wireless transmission, particularly in the physical layer. AI-based physical layer transmission refers to applying AI-based signal processing and communication mechanisms, rather than traditional communication frameworks, in the fundamental signal processing and communication mechanisms. For example, this may include deep learning-based channel coding and decoding, deep learning-based signal estimation and detection, deep learning-based multiple input multiple output (MIMO) mechanisms, and AI-based resource scheduling and allocation.

[0153] Machine learning can be used for channel estimation and channel tracking, as well as for power allocation and interference cancellation at the physical layer of the downlink (DL). Machine learning can also be used for antenna selection, power control, and symbol detection in MIMO systems.

[0154] However, the application of DNN for transmission at the physical layer may have the following problems.

[0155] Deep learning-based AI algorithms require a large amount of training data to optimize training parameters. However, due to limitations in obtaining training data from specific channel environments, a large amount of training data is used offline. This means that static training on training data in specific channel environments can lead to conflicts with the dynamic characteristics and diversity of the wireless channel.

[0156] Furthermore, current deep learning primarily targets real-world signals. However, signals at the physical layer of wireless communications are complex signals. Further research is needed on neural networks capable of detecting complex domain signals to match the characteristics of wireless communication signals.

[0157] Below, we will look at machine learning in more detail.

[0158] Machine learning refers to a series of operations that train machines to perform tasks that humans can or cannot perform. Machine learning requires data and a learning model. In machine learning, data learning methods can be broadly categorized into three types: supervised learning, unsupervised learning, and reinforcement learning.

[0159] Neural network training aims to minimize output errors. It involves repeatedly inputting training data into a neural network, calculating the neural network output and target error for the training data, and backpropagating the neural network error from the output layer to the input layer to update the weights of each node in the neural network to reduce the error.

[0160] Supervised learning uses labeled training data, while unsupervised learning may not have labeled training data. For example, in the case of supervised learning for data classification, the training data may be data in which each training data category is labeled. Labeled training data is input to a neural network, and the error can be calculated by comparing the output (categories) of the neural network with the training data labels. The calculated error is backpropagated through the neural network in the backward direction (i.e., from the output layer to the input layer), and the connection weights of each node in each layer of the neural network can be updated according to the backpropagation. The amount of change in the connection weights of each updated node can be determined by the learning rate. The neural network's calculation of the input data and the backpropagation of the error can constitute a learning cycle (epoch). The learning rate can be applied differently depending on the number of iterations of the neural network's learning cycle. For example, in the early stages of training a neural network, a high learning rate can be used to quickly allow the network to reach a certain level of performance, thereby improving efficiency. In the later stages of training, a low learning rate can be used to improve accuracy.

[0161] Learning methods may vary depending on the characteristics of the data. For example, if the goal is to accurately predict data transmitted by a transmitter in a communication system, supervised learning is preferable to unsupervised learning or reinforcement learning.

[0162] The learning model corresponds to the human brain, and the most basic linear model can be thought of, but the machine learning paradigm that uses highly complex neural network structures, such as artificial neural networks, as learning models is called deep learning.

[0163] The neural network cores used in learning methods are largely divided into deep neural networks (DNN), convolutional deep neural networks (CNN), and recurrent Boltzmann machines (RNN), and these learning models can be applied.

[0164] An artificial neural network is an example of a network of multiple perceptrons.

[0165] Figure 10 shows an example of a perceptron structure.

[0166] Referring to Fig. 10, when an input vector x=(x1,x2,...,xd) is input, the entire process of multiplying each component by a weight (W1,W2,...,Wd), adding up all the results, and then applying the activation function σ() is called a perceptron. A large-scale artificial neural network structure can extend the simplified perceptron structure illustrated in Fig. 10 to apply the input vector to perceptrons of different dimensions. For convenience of explanation, input values ​​or output values ​​are called nodes.

[0167] Meanwhile, the perceptron structure illustrated in Fig. 10 can be explained as consisting of a total of three layers based on input and output values. An artificial neural network in which there are H perceptrons of (d+1) dimensions between the 1st layer and the 2nd layer, and K perceptrons of (H+1) dimensions between the 2nd layer and the 3rd layer can be expressed as in Fig. 4. Fig. 11 shows an example of a multilayer perceptron structure.

[0168] The layer where the input vector is located is called the input layer, the layer where the final output value is located is called the output layer, and all layers located between the input layer and the output layer are called hidden layers. The example in Fig. 4 discloses three layers, but when counting the number of layers in an actual artificial neural network, the input layer is excluded, so it can be viewed as a total of two layers. An artificial neural network is composed of perceptrons, which are basic blocks, connected in two dimensions.

[0169] The aforementioned input, hidden, and output layers can be applied jointly not only to multilayer perceptrons but also to various artificial neural network structures, such as CNNs and RNNs, which will be described later. The greater the number of hidden layers, the deeper the artificial neural network. The machine learning paradigm that uses sufficiently deep artificial neural networks as learning models is called deep learning. Furthermore, the artificial neural network used for deep learning is called a deep neural network (DNN).

[0170] The deep neural network illustrated in Figure 12 is a multilayer perceptron consisting of eight hidden layers and eight output layers. The multilayer perceptron structure is referred to as a fully connected neural network. In a fully connected neural network, there is no connection between nodes located in the same layer, and there is a connection only between nodes located in adjacent layers. DNN has a fully connected neural network structure and is composed of a combination of multiple hidden layers and activation functions, and can be usefully applied to identify correlation characteristics between inputs and outputs. Here, the correlation characteristic can mean the joint probability of inputs and outputs.

[0171] Meanwhile, depending on how multiple perceptrons are connected to each other, various artificial neural network structures different from the aforementioned DNN can be formed.

[0172] In DNN, nodes located within a single layer are arranged in a one-dimensional vertical direction. However, Fig. 13 can assume a case where nodes are arranged two-dimensionally, with w nodes in width and h nodes in height (convolutional neural network structure of Fig. 6). In this case, since a weight is added to each connection in the connection process from one input node to the hidden layer, a total of h×w weights must be considered. Since there are h×w nodes in the input layer, a total of h2w2 weights are required between two adjacent layers.

[0173] The convolutional neural network of Fig. 13 has a problem in that the number of weights increases exponentially according to the number of connections. Therefore, instead of considering the connections of all modes between adjacent layers, it assumes that there are small filters, and performs weighted sum and activation function operations on the overlapping portions of the filters, as in Fig. 7.

[0174] Each filter has a weight corresponding to its size, and weight learning can be performed to extract and output a specific feature on the image as a factor. In Fig. 14, a 3×3 filter is applied to the upper left 3×3 region of the input layer, and the output value resulting from performing weighted sum and activation function operations on the corresponding node is stored in z22.

[0175] The above filter performs weighted sum and activation function operations while moving horizontally and vertically at a certain interval while scanning the input layer, and places the output value at the current filter location. This operation method is similar to the convolution operation for images in the field of computer vision, so a deep neural network with this structure is called a convolutional neural network (CNN), and the hidden layer generated as a result of the convolution operation is called a convolutional layer. In addition, a neural network with multiple convolutional layers is called a deep convolutional neural network (DCNN).

[0176] In the convolutional layer, the number of weights can be reduced by calculating a weighted sum that includes only the nodes located in the area covered by the filter, starting from the node where the current filter is located. This allows a single filter to focus on features within a local area. Accordingly, CNNs can be effectively applied to image data processing where physical distance in a two-dimensional area is an important criterion for judgment. Meanwhile, CNNs can apply multiple filters immediately before the convolutional layer, and can generate multiple output results through the convolution operation of each filter.

[0177] Meanwhile, depending on the data properties, there may be data for which sequence characteristics are important. Considering the length variability and chronological relationship of such sequence data, a structure that applies a method of inputting one element of the data sequence at each timestep and inputting the output vector (hidden vector) of the hidden layer output at a specific timestep together with the immediately following element in the sequence is called a recurrent neural network structure.

[0178] Figure 15 shows an example of a neural network structure in which a recurrent loop exists.

[0179] Referring to Figure 15, a recurrent neural network (RNN) is a structure that inputs elements (x1(t), x2(t), ,..., xd(t)) of a data sequence at a time point t into a fully connected neural network, and then inputs the hidden vectors (z1(t-1), z2(t-1),..., zH(t-1)) of the immediately preceding time point t-1 together and applies a weighted sum and activation function. The reason for transmitting the hidden vector to the next time point in this way is because the information in the input vectors of the preceding time points is considered to be accumulated in the hidden vector of the current time point.

[0180] Figure 16 shows an example of the operating structure of a recurrent neural network.

[0181] Referring to Figure 16, the recurrent neural network operates in a predetermined order of time for the input data sequence.

[0182] When the input vector (x1(t), x2(t), ,..., xd(t)) at time point 1 is input to the recurrent neural network, the hidden vector (z1(1), z2(1),..., zH(1)) is input together with the input vector (x1(2), x2(2),..., xd(2)) at time point 2, and the vector (z1(2), z2(2),..., zH(2)) of the hidden layer is determined through a weighted sum and an activation function. This process is repeatedly performed until time points 2, 3, ,,, T.

[0183] Meanwhile, when multiple hidden layers are placed within a recurrent neural network, it is called a deep recurrent neural network (DRNN). Recurrent neural networks are designed to be useful for processing sequence data (e.g., natural language processing).

[0184] It is a neural network core used in a learning manner, and includes various deep learning techniques such as DNN, CNN, RNN, Restricted Boltzmann Machine (RBM), Deep Belief Network (DBN), and Deep Q-Network, and can be applied to fields such as computer vision, speech recognition, natural language processing, and speech / signal processing.

[0185] Recent attempts to integrate AI into wireless communication systems have focused on the application layer, network layer, and especially deep learning in wireless resource management and allocation. However, this research is increasingly evolving to the MAC layer and physical layer, with attempts to combine deep learning with wireless transmission, particularly at the physical layer. AI-based physical layer transmission refers to the application of AI-based signal processing and communication mechanisms, rather than traditional communication frameworks, in the fundamental signal processing and communication mechanisms. For example, this may include deep learning-based channel coding and decoding, deep learning-based signal estimation and detection, deep learning-based MIMO mechanisms, and AI-based resource scheduling and allocation.

[0186] THz (Terahertz) communication

[0187] THz communications can be applied in 6G systems. For example, data transmission rates can be increased by increasing bandwidth. This can be achieved by using sub-THz communications with wide bandwidths and applying advanced massive MIMO technology.

[0188] Figure 17 is a diagram illustrating the electromagnetic spectrum applicable to the present specification. For example, referring to Figure 17, THz waves, also known as sub-millimeter radiation, generally represent a frequency band between 0.1 THz and 10 THz with a corresponding wavelength ranging from 0.03 mm to 3 mm. The 100 GHz to 300 GHz band (Sub-THz band) is considered a major portion of the THz band for cellular communications. Adding the Sub-THz band to the mmWave band will increase the capacity of 6G cellular communications. Among the defined THz bands, 300 GHz to 3 THz is in the far infrared (IR) frequency band. Although the 300 GHz to 3 THz band is part of the optical band, it is at the boundary of the optical band and immediately follows the RF band. Therefore, this 300 GHz to 3 THz band exhibits similarities to RF.

[0189] Key characteristics of THz communications include (i) the widely available bandwidth to support very high data rates and (ii) the high path loss that occurs at high frequencies (requiring highly directional antennas). The narrow beamwidths generated by highly directional antennas reduce interference. The small wavelength of THz signals allows for a significantly larger number of antenna elements to be integrated into devices and base stations operating in this band. This enables the use of advanced adaptive array technologies to overcome range limitations.

[0190] optical wireless technology

[0191] Optical wireless communication (OWC) technology is planned for 6G communications, in addition to RF-based communications for all possible device-to-access networks. These networks connect to network-to-backhaul / fronthaul networks. OWC technology has already been used in 4G communication systems, but it will be used more widely to meet the demands of 6G communication systems. OWC technologies such as light fidelity, visible light communication, optical camera communication, and wideband-based free space optical (FSO) communication are already well-known. Optical wireless communication can provide very high data rates, low latency, and secure communications. Light detection and ranging (LiDAR) can also be used for ultra-high-resolution 3D mapping in 6G communications based on wideband technology.

[0192] FSO backhaul network

[0193] The transmitter and receiver characteristics of an FSO system are similar to those of a fiber-optic network. Therefore, data transmission in an FSO system is similar to that of a fiber-optic system. Therefore, FSO can be a promising technology for providing backhaul connectivity in 6G systems, in conjunction with fiber-optic networks. Using FSO, ultra-long-distance communications are possible, even over distances exceeding 10,000 km. FSO supports high-capacity backhaul connectivity for remote and non-remote areas, such as the ocean, space, underwater, and isolated islands. FSO also supports cellular base station connections.

[0194] Massive MIMO technology

[0195] One of the key technologies for improving spectral efficiency is the application of MIMO technology. As MIMO technology improves, spectral efficiency also improves. Therefore, massive MIMO technology will be crucial in 6G systems. Because MIMO technology utilizes multiple paths, multiplexing technology must be considered to ensure that data signals can be transmitted along more than one path, as well as beam generation and operation technologies suitable for the THz band.

[0196] Blockchain

[0197] Blockchain will become a crucial technology for managing massive amounts of data in future communication systems. Blockchain is a form of distributed ledger technology. A distributed ledger is a database distributed across numerous nodes or computing devices. Each node replicates and stores an identical copy of the ledger. Blockchains are managed by a peer-to-peer (P2P) network and can exist without being managed by a central authority or server. Data on a blockchain is collected and organized into blocks. Blocks are linked together and protected using cryptography. Blockchain perfectly complements large-scale IoT with its inherently enhanced interoperability, security, privacy, reliability, and scalability. Therefore, blockchain technology offers several features, such as interoperability between devices, traceability of large amounts of data, autonomous interaction with other IoT systems, and the massive connectivity stability of 6G communication systems.

[0198] 3D networking

[0199] 6G systems integrate terrestrial and airborne networks to support vertically expanded user communications. 3D BS will be provided via low-orbit satellites and UAVs. Adding a new dimension in altitude and associated degrees of freedom, 3D connections differ significantly from existing 2D networks.

[0200] Quantum communication

[0201] Unsupervised reinforcement learning holds promise in the context of 6G networks. Supervised learning approaches cannot label the massive amounts of data generated by 6G networks. Unsupervised learning does not require labeling. Therefore, this technology can be used to autonomously build representations of complex networks. Combining reinforcement learning and unsupervised learning allows for truly autonomous network operation.

[0202] drone

[0203] Unmanned aerial vehicles (UAVs), or drones, will be a key element in 6G wireless communications. In most cases, high-speed wireless connections will be provided using UAV technology. Base stations are installed on UAVs to provide cellular connectivity. UAVs offer specific capabilities not found in fixed base station infrastructure, such as easy deployment, robust line-of-sight links, and controlled mobility. During emergencies such as natural disasters, deploying terrestrial communications infrastructure is not economically feasible, and sometimes, volatile environments make it impossible to provide services. UAVs can easily handle these situations. UAVs will become a new paradigm in wireless communications. This technology facilitates three fundamental requirements for wireless networks: enhanced mobile broadband (eMBB), URLLC, and mMTC. UAVs can also support various purposes, such as enhancing network connectivity, fire detection, disaster emergency services, security and surveillance, pollution monitoring, parking monitoring, and accident monitoring. Therefore, UAV technology is recognized as one of the most important technologies for 6G communications.

[0204] cell-free communication

[0205] Tight integration of multiple frequencies and heterogeneous communication technologies is crucial in 6G systems. As a result, users will be able to seamlessly move from one network to another without requiring any manual configuration on their devices. The best network will be automatically selected from available communication technologies. This will break the limitations of the cell concept in wireless communications. Currently, user movement from one cell to another in dense networks results in excessive handovers, resulting in handover failures, handover delays, data loss, and a ping-pong effect. 6G cell-free communications will overcome all of these challenges and provide better QoS. Cell-free communications will be achieved through multi-connectivity and multi-tier hybrid technologies, as well as heterogeneous radios on devices.

[0206] Wireless Information and Energy Transfer (WIET)

[0207] WIET uses the same fields and waves as wireless communication systems. Specifically, sensors and smartphones will be charged using wireless power transfer during communication. WIET is a promising technology for extending the life of battery-powered wireless systems. Therefore, battery-less devices will be supported by 6G communications.

[0208] Integration of sensing and communication

[0209] Autonomous wireless networks are capable of continuously sensing dynamically changing environmental conditions and exchanging information between different nodes. In 6G, sensing will be tightly integrated with communications to support autonomous systems.

[0210] Integration of Access Backhaul Networks

[0211] In 6G, the density of access networks will be enormous. Each access network will be connected to backhaul connections, such as fiber optics and FSO networks. To cope with the enormous number of access networks, there will be tight integration between access and backhaul networks.

[0212] Holographic beamforming

[0213] Beamforming is a signal processing procedure that adjusts an antenna array to transmit a wireless signal in a specific direction. It is a subset of smart antennas or advanced antenna systems. Beamforming technology offers several advantages, including high signal-to-noise ratio, interference avoidance and rejection, and high network efficiency. Holographic beamforming (HBF) is a novel beamforming method that differs significantly from MIMO systems because it uses software-defined antennas. HBF will be a highly effective approach for efficient and flexible signal transmission and reception in multi-antenna communication devices in 6G.

[0214] Big data analysis

[0215] Big data analytics is a complex process for analyzing diverse, large-scale data sets, or "big data." This process uncovers hidden data, unknown correlations, and customer trends, ensuring complete data management. Big data is collected from various sources, such as video, social networks, images, and sensors. This technology is widely used to process massive amounts of data in 6G systems.

[0216] large intelligent surface (LIS)

[0217] THz band signals have strong linearity, which can create many shadow areas due to obstacles. LIS technology, which enables expanded communication coverage, enhanced communication stability, and additional value-added services by installing LIS near these shadow areas, is becoming increasingly important. LIS is an artificial surface made of electromagnetic materials that can alter the propagation of incoming and outgoing radio waves. While LIS can be viewed as an extension of Massive MIMO, it differs from Massive MIMO in its array structure and operating mechanism. Furthermore, LIS operates as a reconfigurable reflector with passive elements, passively reflecting signals without using active RF chains, which offers the advantage of low power consumption. Furthermore, because each passive reflector in LIS must independently adjust the phase shift of the incoming signal, this can be advantageous for wireless communication channels. By appropriately adjusting the phase shift via the LIS controller, the reflected signal can be collected at the target receiver to boost the received signal power.

[0218] Terahertz (THz) wireless communications

[0219] Fig. 18 is a diagram illustrating a THz communication method applicable to this specification.

[0220] Referring to Fig. 18, THz wireless communication is a wireless communication using THz waves with a frequency of approximately 0.1 to 10 THz (1 THz = 1012 Hz), and may refer to terahertz (THz) band wireless communication using a very high carrier frequency of 100 GHz or higher. THz waves are located between the RF (Radio Frequency) / millimeter (mm) and infrared bands, and (i) compared to visible light / infrared rays, they penetrate non-metallic / non-polarizable materials well, and compared to RF / millimeter waves, they have a shorter wavelength, so they have high straightness and can focus beams.

[0221] In addition, since the photon energy of THz waves is only a few meV, it has the characteristic of being harmless to the human body. The frequency band expected to be used for THz wireless communication may be the D-band (110 GHz to 170 GHz) or H-band (220 GHz to 325 GHz) bands where propagation loss due to absorption of molecules in the air is small. In addition to 3GPP, standardization discussions for THz wireless communication are being centered around the IEEE 802.15 THz WG (working group), and standard documents issued by the IEEE 802.15 TG (task group) (e.g., TG3d, TG3e) may specify or supplement the contents described in this specification. THz wireless communication can be applied to wireless cognition, sensing, imaging, wireless communication, THz navigation, etc.

[0222] Specifically, referring to FIG. 18, THz wireless communication scenarios can be categorized into macro networks, micro networks, and nanoscale networks. In macro networks, THz wireless communication can be applied to vehicle-to-vehicle (V2V) connections and backhaul / fronthaul connections. In micro networks, THz wireless communication can be applied to fixed point-to-point or multi-point connections, such as indoor small cells, wireless connections in data centers, and near-field communication, such as kiosk downloading. Table 5 below shows examples of technologies that can be utilized in THz waves.

[0223]

[0224] FIG. 19 is a diagram illustrating a THz wireless communication transceiver applicable to the present specification.

[0225] Referring to Figure 19, THz wireless communication can be classified based on the method for THz generation and reception. THz generation methods can be classified into optical or electronic device-based technologies.

[0226] Here, there are several ways to generate THz using electronic components, including a method using a semiconductor component such as a resonant tunneling diode (RTD), a method using a local oscillator and a multiplier, a monolithic microwave integrated circuit (MMIC) method using a compound semiconductor high electron mobility transistor (HEMT)-based integrated circuit, and a method using a Si-CMOS-based integrated circuit. In the case of Fig. 19, a multiplier (doubler, tripler, multiplier) is applied to increase the frequency, and it passes through a subharmonic mixer and is radiated by an antenna. Since the THz band forms a high frequency, a multiplier is essential. Here, the multiplier is a circuit that has an output frequency that is N times that of the input, and matches it to the desired harmonic frequency and filters out all remaining frequencies. In addition, beamforming can be implemented by applying an array antenna or the like to the antenna of Fig. 19. In Figure 19, IF represents intermediate frequency, tripler and multipler represent multipliers, PA represents a power amplifier, LNA represents a low noise amplifier, and PLL represents a phase-locked loop.

[0227] FIG. 20 is a diagram illustrating a THz signal generation method applicable to the present specification. FIG. 21 is a diagram illustrating a wireless communication transceiver applicable to the present specification.

[0228] Referring to FIGS. 20 and 21, optical device-based THz wireless communication technology refers to a method of generating and modulating a THz signal using an optical device. The optical device-based THz signal generation technology is a technology that generates an ultra-high-speed optical signal using a laser and an optical modulator, and converts it into a THz signal using an ultra-high-speed photodetector. Compared to a technology that uses only electronic devices, this technology makes it easy to increase the frequency, enables high-power signal generation, and obtains a flat response characteristic in a wide frequency band. In order to generate a THz signal based on an optical device, a laser diode, a wideband optical modulator, and an ultra-high-speed photodetector are required, as illustrated in FIG. 20. In the case of FIG. 20, the light signals of two lasers with different wavelengths are combined to generate a THz signal corresponding to the wavelength difference between the lasers. In Fig. 20, an optical coupler refers to a semiconductor device that transmits an electrical signal using optical waves to provide electrical isolation and coupling between circuits or systems, and a uni-travelling carrier photo-detector (UTC-PD) is a type of photodetector that uses electrons as active carriers and reduces the travel time of electrons by bandgap grading. The UTC-PD is capable of detecting light at 150 GHz or higher. In Fig. 20, an erbium-doped fiber amplifier (EDFA) represents an erbium-doped fiber amplifier, a photodetector (PD) represents a semiconductor device that can convert an optical signal into an electrical signal, an OSA represents an optical sub-assembly that modularizes various optical communication functions (e.g., photoelectric conversion, electro-optical conversion, etc.) into a single component, and a DSO represents a digital storage oscilloscope.

[0229] Fig. 22 is a diagram illustrating a transmitter structure applicable to the present specification. In addition, Fig. 23 is a diagram illustrating a modulator structure applicable to the present specification.

[0230] Referring to FIGS. 22 and 23, a signal phase, etc. can generally be changed by passing an optical source of a laser through an optical wave guide. At this time, data is loaded by changing the electrical characteristics through a microwave contact, etc. Therefore, the optical modulator output is formed as a modulated waveform. An opto-electrical (O / E) converter can generate a THz pulse according to an optical rectification operation by a nonlinear crystal, an opto-electrical conversion by a photoconductive antenna, an emission from a bunch of relativistic electrons, etc. A terahertz pulse (THz pulse) generated in the above manner can have a length in units of femtoseconds to picoseconds. An optical / electronic converter (O / E converter) performs down conversion by utilizing the non-linearity of the device.

[0231] Considering the THz spectrum usage, it is likely that THz systems will use multiple contiguous gigahertz bands for fixed or mobile service purposes. Based on the outdoor scenario criteria, the available bandwidth can be classified based on the oxygen attenuation of 10^2 dB / km in the spectrum up to 1 THz. Accordingly, a framework in which the available bandwidth is composed of multiple band chunks can be considered. As an example of the above framework, if the THz pulse length for one carrier is set to 50 ps, ​​the bandwidth (BW) becomes approximately 20 GHz.

[0232] Effective down-conversion from the infrared band to the terahertz band (THz band) depends on how to utilize the nonlinearity of the optical / electrical converter (O / E converter). In other words, to down-convert to the desired terahertz band (THz band), it is necessary to design an O / E converter with the most ideal non-linearity for transferring to the corresponding terahertz band (THz band). If an O / E converter that is not suitable for the target frequency band is used, errors are likely to occur in the amplitude and phase of the corresponding pulse.

[0233] In a single-carrier system, a terahertz transmission and reception system can be implemented using a single optical-to-electrical converter. Depending on the channel environment, in a multi-carrier system, the number of optical-to-electrical converters may be equal to the number of carriers. This phenomenon will be particularly noticeable in a multi-carrier system that utilizes multiple broadbands according to the aforementioned spectrum usage plan. In this regard, a frame structure for the multi-carrier system may be considered. A signal down-converted using an optical-to-electrical converter may be transmitted in a specific resource region (e.g., a specific frame). The frequency domain of the specific resource region may include a plurality of chunks. Each chunk may be composed of at least one component carrier (CC).

[0234] Here, the wireless communication technology implemented in the wireless devices (200a, 200b) of the present specification may include not only LTE, NR, and 6G, but also Narrowband Internet of Things for low-power communication. At this time, for example, NB-IoT technology may be an example of LPWAN (Low Power Wide Area Network) technology, and may be implemented with standards such as LTE Cat NB1 and / or LTE Cat NB2, and is not limited to the above-described names. Additionally or alternatively, the wireless communication technology implemented in the wireless devices (XXX, YYY) of the present specification may perform communication based on LTE-M technology. At this time, for example, LTE-M technology may be an example of LPWAN technology, and may be called by various names such as eMTC (enhanced Machine Type Communication). For example, LTE-M technology can be implemented by at least one of various standards such as 1) LTE CAT 0, 2) LTE Cat M1, 3) LTE Cat M2, 4) LTE non-BL (non-Bandwidth Limited), 5) LTE-MTC, 6) LTE Machine Type Communication, and / or 7) LTE M, and is not limited to the above-described names. Additionally or alternatively, the wireless communication technology implemented in the wireless device (200a, 200b) of the present specification can include at least one of ZigBee, Bluetooth, and Low Power Wide Area Network (LPWAN) considering low-power communication, and is not limited to the above-described names. For example, ZigBee technology can create personal area networks (PAN) related to small / low-power digital communication based on various standards such as IEEE 802.15.4, and can be called by various names.

[0235] The THz and mmWave bands can have large path loss characteristics in the atmosphere due to their short wavelengths. To overcome this, technologies utilizing beams with large gain in a specific direction are being operated in the THz and mmWave bands. The wavelengths in the THz band are shorter than those in the mmWave band, so by massively combining antenna elements with smaller aperture sizes, it exhibits characteristics such as large gain, long-distance transmission, and sharp pencil beam-like beam width characteristics (i.e., reduced HPBW (Half Power Beam Width)). By utilizing these beam characteristics, massive MIMO (multi-input, multi-output) antenna technology can be effectively utilized in the THz band. Furthermore, sophisticated beam alignment and tracking technology may be required to operate massive MIMO antenna technology.

[0236] For operation in the THz or mmWave bands, the use of beam technology may be essential to overcome the problem of large attenuation at frequencies with short wavelengths. In the case of channel measurement-based technology operated for beam tracking, the receiver may allocate an additional reference signal to be used for beam alignment between the transmitter and receiver, and perform measurement and estimation processes to track the number of beams used. In this case, resource inefficiency due to additional resource usage and the problem of measurement and estimation times increasing proportionally to the number of beams may arise.

[0237] Additionally, when the terminal operates a prediction-based algorithm such as the Extended Kalman Filter (EKF), the terminal can repeatedly perform a parameter prediction process to minimize error covariance, a process to acquire measured values ​​at the current point in time through sensors, and a covariance update process. Here, the terminal can perform a parameter prediction process to minimize error covariance based on information measured by sensors prior to the current point in time.

[0238] However, if the terminal performs these processes without considering the influence of external environmental factors, there is a risk that the estimated value error may increase when the external environment changes, and the increased error covariance value may affect the terminal's selection of the next beam. Therefore, a stable beam operation technology that can minimize the influence of external environmental changes is required.

[0239] Prediction-based beam operation techniques typically involve utilizing inertial sensors or channel-based techniques, which may utilize EKF or other prediction algorithms. When utilizing these algorithms, modeling the channel or operating environment for prediction, selecting appropriate parameters, and optimizing the selected model for system operation can be crucial.

[0240] Modeling parameters utilized for beamforming and / or beam tracking include factors such as angle, speed, direction, and distance, and a gyro sensor, acceleration sensor, geomagnetic sensor, GPS, etc. can be utilized to measure these data.

[0241] In EKF prediction-based technology, a spatial state model can be generated based on the modeling parameters described above, and the pre-observation error covariance and Kalman gain can be obtained based on the spatial state model and covariance noise (process noise). Furthermore, after the observation is complete, the final predicted value can be obtained using the Kalman gain, error covariance, and pre-predicted value.

[0242] During this process, system environmental noise, which is environmental influences that can cause sensor measurement errors, may be present. Examples of system environmental noise include, but are not limited to, wind speed, temperature, rainfall, fading, or ambient reflectors.

[0243] Measurement errors that may be caused by such system environment noise may cause an increase in error covariance and an increase in error in predicted values, and ultimately may cause system problems such as an increase in beam tracking errors or beam link failure.

[0244] In order to prevent measurement errors that may occur due to such system environment noise, a system that operates an EKF or similar prediction algorithm can define factors related to the system environment noise as covariance noise (process noise) and manage statistical data by classifying the degree of influence by system environment noise by factor. In addition, a system that operates an EKF or similar prediction algorithm can generate correction data and transmit the correction data to a terminal. The terminal can receive the correction data from a system that operates an EKF or similar prediction algorithm. The terminal can use the correction data as correction data information when operating a prediction algorithm such as an EKF. In this case, the terminal can perform prediction results with high accuracy and beam tracking with high reliability.

[0245] As mentioned above, the THz band is likely to be an environment where sharp beams with narrow beamwidths operate. Therefore, more sophisticated beam control and tracking technologies may be required in the THz band.

[0246] Meanwhile, the Kalman Filter (or Extended Kalman Filter) estimates the current value based on the value estimated at the previous time, and can iterate through a process of continuously updating and optimizing the state distribution to minimize the error between the estimated value and the actual value. First, the Kalman Filter's operation method is explained as follows.

[0247] Figure 24 is a flowchart explaining the operation method of the Kalman filter.

[0248] In FIG. 24, S2410 and S2420 may be prediction steps, and S2430 to S2450 may be measurement or update steps.

[0249] The Kalman filter can obtain a pre-prediction value (S2410). The pre-prediction value may be an expected value of an input value to be input after the current state. Here, the previous data may be an estimated value generated by the Kalman filter based on the input value provided immediately before the current point in time. The Kalman filter can obtain the pre-prediction value based on the following mathematical expression (1).

[0250]

[0251] In mathematical expression 1, can be a priori predicted value, can be a space state equation, may be previous data, can be a control matrix, can be an external control vector. Here, can have a distribution form that reflects the error variance of the error covariance matrix.

[0252] The Kalman filter can update the prior error covariance (S2420). The Kalman filter can update the prior error covariance based on the prior prediction value obtained in S2410. The Kalman filter can update the prior error covariance based on the following mathematical expression 2.

[0253]

[0254] In mathematical expression 2, can be the prior error covariance for which the update is performed, can be the prior covariance matrix of process noise, The uncertainty can be modeled mainly as a Gaussian with limited variance and zero mean.

[0255] The Kalman filter can obtain a measured value and a Kalman gain (S2430). The Kalman filter can obtain a measured value based on a sensor or other device in the current operating environment. Since errors may occur in the measured value, the Kalman filter may need to determine a value using an appropriate weight between the pre-prediction value obtained in S2410 and the measured value. Accordingly, the Kalman filter can obtain a Kalman gain and obtain a post-prediction value after the measurement based on the Kalman gain. The Kalman filter can obtain the Kalman gain based on Equation 3.

[0256]

[0257] In mathematical expression 3, can only benefit the sword, can be an observation model matrix, may be measurement noise.

[0258] The Kalman filter can obtain a posterior prediction value (S2440). The Kalman filter can obtain a posterior prediction value based on the prior prediction value obtained in S2410, the measurement value obtained in S2430, and the Kalman gain. The Kalman filter can obtain a posterior prediction value based on the following mathematical expression 4.

[0259]

[0260] In mathematical equation 4, can be a posterior prediction value, can be a measured value. Referring to Equation 4, it can be seen that the Kalman filter operates in such a way that the measured value is more reliable when the Kalman gain increases, and the predicted value is more reliable when the Kalman gain decreases.

[0261] The Kalman filter can update the posterior error covariance (S2450). The Kalman filter can update the posterior error covariance based on the updated prior error covariance obtained in S2420 and the Kalman gain obtained in S2430. The Kalman filter can update the posterior error covariance based on the following mathematical expression (5).

[0262]

[0263] In mathematical expression 5, may be a posterior error covariance after the update is performed. Meanwhile, the updated posterior error covariance may be utilized as a prior covariance in the next state (e.g., t+1), and steps S2410 to S2450 may be repeated based on this.

[0264] For the extended Kalman filter, steps S4110 to S2450 can be performed in the same manner, except that nonlinear modeling is performed before step S2410, and linearization for the nonlinear modeling is performed based on the Jacobian matrix.

[0265] Even if the error due to processor noise averages out to 0 over time, instantaneous accurate control is important in beam control, and if accurate control is not achieved by immediately reflecting instantaneous measurement errors due to external environmental factors, beam misalignment may occur, which may result in beam failure and link failure.

[0266] Therefore, a method is required to minimize the influence of noise and the error in the predicted value by utilizing optimized statistical data, reflecting an instantaneous compensation method for noise factors that cause errors in a specific environment.

[0267] In the THz band, the size will further increase due to the increased number of antennas, which will obviously be utilized as an ultra-massive MIMO technology that compensates for the physical attenuation characteristics in the THz band and utilizes the beam spatial gain, and for this, more sophisticated spatial beamforming and beam tracking technologies may be required.

[0268] The present disclosure proposes a technology that can be used to further minimize errors in the operation of Kalman filters, extended Kalman filters, or data estimation technologies applied to prediction-based beamforming technology utilizing sensors, etc.

[0269] Figures 25 and 26 are conceptual diagrams for explaining the influence of environmental factors in a wireless communication system.

[0270] Figures 25 and 26 are diagrams illustrating the relationship between the increasing influence of environmental factors and the increase in error of a Kalman filter (or extended Kalman filter). The increasing influence of environmental factors may indicate an increase in system operational noise due to the environmental factors.

[0271] As described above in FIG. 24, the Kalman filter can 1) obtain a prior prediction value, 2) perform an update on the prior error covariance, 3) obtain a measurement value and Kalman gain, 4) obtain a posterior prediction value, and 5) perform an update on the posterior error covariance.

[0272] When the influence of environmental factors increases, the prior error covariance obtained by the Kalman filter in 2) may increase, and accordingly, the Kalman gain obtained by the Kalman filter in 3) may increase. The increase in the Kalman gain may increase the size of the posterior prediction value obtained by the Kalman filter in 4). In other words, the Kalman filter may have the effect of placing more weight on the measurement value obtained in 3) than on the prior prediction value obtained in 1), which may cause an increase in the error of the posterior measurement value.

[0273] That is, when environmental factors increase, and when an increase in measurement error due to environmental factors (or external factors) occurs simultaneously due to the limitations of modeling process noise, the possibility of divergence of the posterior prediction value obtained by the Kalman filter in 5) may increase.

[0274] Figure 27 is a flowchart illustrating the initial connection procedure of a terminal in a wireless communication system.

[0275] Referring to Figure 27, the base station can broadcast an SSB (S2710). The terminal can perform an initial beam search (S2720). The terminal can receive the SSB broadcast by the base station through multiple reception beams and determine an initial optimal reception beam and panel. The terminal can perform frame boundary synchronization, master information block (MIB) decoding, and system information block (SIB) decoding.

[0276] The terminal may transmit a PRACH (Physical Random Access Channel) to the base station (S2730). In step S2720, the terminal may transmit a RACH (Random Access Channel) preamble to the base station based on the results of frame boundary synchronization, MIB decoding, or SIB decoding performed.

[0277] The base station can receive a PRACH from the terminal (S2730). The base station can transmit a random access request (RAR) message to the terminal (S2740). The terminal can receive the RAR message from the base station (S2740). The terminal can transmit an RRC (Radio resource control) request message to the base station (S2750). The base station can receive the RRC request message from the terminal (S2750). The base station can transmit an RRC setup message to the terminal (S2760). The terminal can receive the RRC setup message from the base station (S2760). Through this process, the state of the terminal can be changed to an RRC connected state.

[0278] A NAS (Non-Access Stratum) registration and authentication procedure may be performed between a terminal and a core network (S2770). A base station may request terminal capability information from a terminal (S2780). The terminal may receive a request for terminal capability information from the base station (S2780). The terminal may transmit the terminal capability information to a core network (S2790). The terminal may transmit the terminal capability information to the core network through the base station. The terminal capability information may include UE-EUTRA (Evolved Universal Terrestrial Radio Access) capability information, UE-NR capability information, and UE-MRDC (Multi-RAT Dual Connectivity) capability information. The UE-NR capability information may include a MIMO-ParameterPerBand field, and the MIMO-ParameterPerBand field may include parameters regarding beam-related information.

[0279] Beam-related capability information may include beamCorrespondenceWithoutUL-BeamSweeping , periodicBeamReport , aperiodicBeamReport , sp-BeamReportPUCCH , sp-BeamReportPUSCH , maxNumberRxBeam , maxNumberRxTxBeamSwitchDL , maxNumberNonGroupBeamReporting , groupBeamReporting , uplinkBeamManangement , beamReportTiming , beamManagementSSB-CSI-RS , beamSwitchTiming , BeamManagementSSB-CSI-RS , maxNumberPeriodicCSI-PerBWP-ForBeamReport , maxNumberAperiodicCSI-PerBWP-ForBeamReport , and maxNumberSemiPersistentCSI-PerBWP-ForBeamReport .

[0280] That is, it can be seen that beam-related capability information consists of information related to beam reporting and information related to SSB and CSI-RS management, and does not yet include information related to the utilization of sensors used for beam prediction-based technology operation.

[0281] A prediction-based beam tracking technology using a sensor according to one embodiment of the present disclosure requires sensors that can be used in various positioning techniques, a positioning algorithm utilizing the sensors, and a beam tracking technology using accurate positioning information.

[0282] For example, the sensors used in the prediction-based beam tracking technology using sensors according to one embodiment of the present disclosure are as follows.

[0283] Figures 28 to 31 are conceptual diagrams illustrating examples of sensors used for beam prediction-based technology operation.

[0284] Figure 28 is a drawing for explaining the operating principle of the gyro sensor.

[0285] Referring to Figure 28, a gyro sensor may be a sensor that detects angular velocity. When an object with a certain velocity rotates, a Coriolis force may be generated perpendicular to the direction of the velocity, and the gyro sensor can measure the Coriolis force. The Coriolis force can be expressed as in mathematical equation 6.

[0286]

[0287] In mathematical expression 6, may be the Coriolis force.

[0288] Changes in the speed or mass of a moving object may occur due to environmental changes (e.g., changes in wind direction or speed), which may cause errors in the angular velocity calculated by the gyro sensor.

[0289] Figure 29 is a conceptual diagram explaining the operating principle of an acceleration sensor.

[0290] Referring to Fig. 29, an acceleration sensor may be a sensor that measures how much force an object is receiving based on the Earth's gravitational acceleration. The acceleration sensor can measure the magnitude of the gravitational acceleration acting on the sensor by dividing it into three vectors along the X, Y, and Z axes.

[0291] Acceleration sensors can be capacitive or piezoelectric. Capacitive sensors can sense changes in the position of a test mass (proof mass) due to acceleration, leading to movement of electrodes and a change in capacitance. Piezoresistive sensors can sense changes in the test mass due to acceleration, resulting in changes in tensile stress resistance and a corresponding change in voltage.

[0292] These acceleration sensors may cause measurement errors due to additional motion acceleration generated by movement during measurement, and may cause measurement errors due to changes in wind direction and speed.

[0293] Figures 30 and 31 are conceptual diagrams for explaining the operation of a geomagnetic sensor.

[0294] Referring to FIGS. 30 and 31, the geomagnetic sensor may be a sensor that measures geomagnetism along each axis to measure the angle of inclination. The geomagnetic sensor may be a sensor that utilizes physical phenomena called the magnetoresistor effect (MR effect) and the Hall effect.

[0295] These geomagnetic sensors may be affected by the surrounding magnetic field signals, which may result in measurement errors. In addition, factors such as power lines, steel structures, or tunnels around the geomagnetic sensor may increase measurement errors due to the nature of the magnetic field measurement principle. In addition, measurement errors may increase due to sunspot explosions, which may affect other magnetic field signals.

[0296] FIG. 32 is a flowchart of an initial connection method according to one embodiment of the present disclosure.

[0297] Figure 32 illustrates the process of transmitting terminal capability information to the core network when the terminal initially connects to the communication system. Terminal capability information may include a list of information regarding sensors utilized by the terminal.

[0298] Referring to Figure 32, the base station can broadcast an SSB (S3210). The terminal can perform an initial beam search (S3220). The terminal can receive the SSB broadcast by the base station through multiple reception beams and determine an initial optimal reception beam and panel. The terminal can perform frame boundary synchronization, MIB decoding, and SIB decoding.

[0299] The terminal may transmit a PRACH to the base station (S3230). In step S3220, the terminal may transmit a RACH preamble to the base station based on the results of frame boundary synchronization, MIB decoding, or SIB decoding performed.

[0300] The base station can receive a PRACH from the terminal (S3240). The base station can transmit an RAR message to the terminal (S3240). The terminal can receive the RAR message from the base station (S3250). The terminal can transmit an RRC request message to the base station (S3260). The base station can receive the RRC request message from the terminal (S3260). The base station can transmit an RRC configuration message to the terminal (S3270). The terminal can receive the RRC configuration message from the base station (S3270). Through this process, the state of the terminal can be changed to an RRC connected state.

[0301] NAS registration and authentication procedures may be performed between a terminal and a core network (S3280). A base station may request terminal capability information from a terminal (S3290). A terminal may receive a request for terminal capability information from a base station (S3290). The terminal may transmit the terminal capability information to a core network (S3300). The terminal may transmit the terminal capability information to the core network through the base station. The terminal capability information may include UE-EUTRA capability information, UE-NR capability information, and UE-MRDC capability information. The UE-NR capability information may include a MIMO-ParameterPerBand field, and the MIMO-ParameterPerBand field may include parameters regarding beam-related information. In addition, the terminal capability information may further include sensor usage capability (SensorUsageCapability) information. The sensor information capability information may be a list of sensor information that the terminal is utilizing.

[0302] By transmitting sensor information used by the terminal to the core network of the system, the core network can be informed that the terminal is performing prediction-based beam tracking operation and positioning information-related application service operation utilizing sensor information.

[0303] In addition, by transmitting sensor information used by the terminal to the core network of the system, compensation information of the compensation table proposed in the present disclosure can be transmitted in a manner consistent with information suitable for the corresponding sensor based on the sensor information used by the terminal.

[0304] FIG. 33 is a flowchart illustrating a process between a terminal and a core network according to an embodiment of the present disclosure. FIGS. 34 to 40 are conceptual diagrams of a correction table according to an embodiment of the present disclosure. FIGS. 34 to 40 are conceptual diagrams of a correction table according to an embodiment of the present disclosure. FIG. 41 is a conceptual diagram of a correction offset table according to an embodiment of the present disclosure.

[0305] Figure 33 illustrates a process of transmitting a correction table to compensate for errors caused by external environmental factors that occur when operating a corresponding sensor of a terminal utilizing various sensor information.

[0306] Before Fig. 33, the terminal and core network can share the terminal capability information described above in Fig. 32.

[0307] Referring to FIG. 33, a terminal may transmit location information to a core network (S3310). The terminal may transmit the location information to the core network via a TRP (TRANSMISSION AND RECEPTION POINT). When the terminal enters a specific area, the terminal may periodically transmit the location information to the core network or transmit the location information to the core network based on the location. The terminal may include a mobile terminal such as a UAM, UAV, vehicle, or ship. The specific area may include a geofence or an area requiring correction of the measurement values ​​of a sensor included in the terminal. For example, an area requiring correction may be an area significantly affected by rainfall, strong winds, or fading. The core network may receive the location information from the terminal (S3310).

[0308] The core network can determine whether a terminal has entered a specific area based on its location information (S3320). The core network can store the terminal's location information on a server. Based on this location information, the core network can determine whether the terminal has entered a specific area (e.g., a geo-fence).

[0309] The core network can determine a correction table based on terminal capability information (S3330). The core network can obtain sensor information operable in the terminal based on sensor usability information included in the terminal capability information. The core network can determine a correction table to update based on the sensor information operable in the terminal. A correction table can be provided for each sensor. The correction table can be generated based on factors affecting the sensor's measurement accuracy.

[0310] Referring to FIGS. 34 to 37, for example, the geomagnetic sensor can measure the magnitude of the magnetic field acting on each of the X, Y, and Z axes as a voltage level using the Hall effect, etc. The geomagnetic sensor can predict the magnitude of the magnetic field in the true north direction based on the voltage level. The true north direction can differ by 10 degrees from the magnetic north direction.

[0311] A correction table for a geomagnetic sensor may include correction values ​​that affect the magnetic field measurements in the X, Y, and Z axes over time according to regional factors (regional locations), as shown in Fig. 34. The correction table illustrated in Fig. 35 may be a static correction table.

[0312] A correction table for a geomagnetic sensor may include correction values ​​that affect the magnetic field measurements in the X, Y, and Z axes over time according to environmental factors (regional locations), as shown in FIGS. 35 and 36. In FIG. 36, the environmental factor may be a magnetic storm, and in FIG. 37, the environmental factor may be an environmental change in the same region. The correction tables shown in FIGS. 36 and 37 may be temporary correction tables.

[0313] A correction table for a geomagnetic sensor may include correction values ​​that affect magnetic field measurements in the X, Y, and Z axes for wind speed and direction according to local location, as shown in Fig. 37. The correction table illustrated in Fig. 38 may be a static correction table.

[0314] Referring to Figure 38, for example, a gyro sensor may measure angular velocity, and thus the velocity may affect the value of the Coriolis force measured for this purpose. A compensation table for a gyro sensor may include, as compensation values, the velocity changes in the X, Y, and Z axes over time due to environmental changes.

[0315] Referring to FIGS. 39 and 40, for example, the acceleration sensor may measure the angular velocity of each of the X, Y, and Z axes by measuring the amount of change in acceleration. The acceleration sensor may include a piezoresistive sensor and a capacitive sensor. The compensation table for the acceleration sensor may include a compensation table for the piezoresistive sensor as in FIG. 39 and a compensation table for the capacitive sensor as in FIG. 40. The compensation table for the acceleration sensor may include compensation values ​​for the X, Y, and Z axes according to wind speed and wind direction. Here, the compensation values ​​may include compensation values ​​for the amount of change in mass or the amount of change in acceleration.

[0316] The correction tables of Figures 34 to 40 may include data already created based on statistical data. The correction values ​​included in the correction table may be quantitative data statistics based on back-end data, etc., when configuring a system network. Furthermore, if the core network operates ML (Machine Learning), etc., it may be possible to operate it as modeling data information for self-learning in the future.

[0317] Referring back to Figure 33, the core network can transmit the correction table to the terminal (S3340). The core network can transmit the correction table to the terminal via TRP. The core network can transmit the correction table to the terminal via a new RRC message. The core network can transmit the correction table to the terminal via an existing RRC reconfiguration message or DCI.

[0318] The terminal can receive a calibration table from the core network (S3340). Based on the calibration table, the terminal can obtain a final predicted value (S3350). The terminal can select a calibration value to apply to the sensor currently used by the terminal from the calibration table. The terminal can determine an offset value based on the calibration value and a calibration offset table. The calibration offset table may be generated or managed through its own calibration process for specific precision data compensation depending on the terminal's operating environment.

[0319] Referring to FIG. 41, for example, the calibration offset table may include offset values ​​of the X, Y, and Z axes for each correction value according to each sensor. Here, each sensor may include a geomagnetic sensor, an acceleration sensor, or a gyro sensor.

[0320] Referring again to Figure 33, the terminal can generate the final predicted value of the sensor based on the offset value. The terminal can generate the final predicted value of the sensor using a Kalman filter or an extended Kalman filter. The terminal can generate the final predicted value of the sensor based on Equation 7.

[0321]

[0322] In mathematical expression 7, may be the final predicted value of the sensor, can be a pre-predicted value of the sensor, can only benefit the sword, can be the measurement value of the sensor, can be an offset, can be an observation model matrix.

[0323] The terminal can perform beam prediction and beam tracking (S3360). The terminal can adjust the beam angle based on the final predicted value. The terminal can perform beam prediction and beam tracking based on the adjusted beam angle.

[0324] FIG. 42 is a flowchart between a terminal and a core network according to another embodiment of the present disclosure.

[0325] In Fig. 42, before S4210, the terminal may have entered a specific area.

[0326] Referring to Figure 42, a terminal can transmit location information to the core network (S4210). The terminal can transmit location information to the core network via TRP. When the terminal leaves a specific area, the terminal can periodically transmit location information to the core network or transmit location information to the core network based on location.

[0327] The core network can receive location information from the terminal (S4210). Based on the terminal's location information, the core network can determine whether the terminal has left a specific area (S4220). The core network can store the terminal's location information on a server. Based on the location information, the core network can determine whether the terminal has left a specific area. The core network can transmit a correction table utilization off message to the terminal (S4230).

[0328] The terminal may receive a compensation table utilization off message from the core network (S4230). The terminal may obtain a final predicted value (S4240). The terminal may obtain the final predicted value using a Kalman filter or an extended Kalman filter without obtaining compensation or offset for the sensor measurement value through the compensation table. Here, the final predicted value may be identical to the sensor measurement value.

[0329] The terminal can perform beam prediction and beam tracking (S4250). The terminal can adjust the beam angle based on the final predicted value. The terminal can perform beam prediction and beam tracking based on the adjusted beam angle.

[0330] Figure 43 is a conceptual diagram illustrating the effect of a method according to one embodiment of the present invention.

[0331] Referring to Fig. 43, 1) the influence of system noise due to environmental factors may increase, and accordingly, 2) the error covariance or Kalman gain of the Kalman filter or the extended Kalman filter may increase, and accordingly, 3) a problem may occur in which the Kalman filter or the extended Kalman filter may be operated in a way that gives more weight to the actual value than to the predicted value.

[0332] According to a method according to one embodiment of the present invention, 4) a process of correcting the influence of environmental factor noise is included, thereby 5) minimizing the error of the actual measured value and also minimizing the error of the final predicted value.

[0333] Figure 44 is a conceptual diagram illustrating the effect of a method according to one embodiment of the present invention when a terminal operates a geomagnetic sensor.

[0334] Referring to FIG. 44, 1) a change factor in the area of ​​a steel bridge or steel structure or a magnetic field environment may occur, 2) table information for error correction of a geomagnetic sensor according to a change factor in the magnetic field environment may be transmitted to a terminal, 3) the terminal may estimate prior sensor data based on the table information for error correction, 4) the terminal may correct the actual value through correction data (i.e., correction table) and correction value (i.e., correction table) after sensor measurement, and accordingly 5) the prediction accuracy of sensor measurement data through a Kalman filter or an extended Kalman filter of the terminal may increase, and 6) the fast control and accuracy of beam forming may increase.

[0335] Figure 45 is a flowchart of a signal transmission and reception method according to one embodiment of the present disclosure.

[0336] The first node may be the terminal described above, the second node may be the core network described above, and the third node may be the base station or TRP described above.

[0337] The first node can transmit terminal capability information to the second node (S4510). The first node can transmit terminal capability information including information about a sensor included in the first node to the second node.

[0338] The first node can transmit its first location information to the second node (S4520). When the first node enters a specific area, the first node can transmit its first location information to the second node.

[0339] The first node may receive a correction table for the sensor from the second node (S4530). The multiple correction tables may be generated based on factors related to the sensor type and its measurement accuracy. The first node may receive an RRC message, an RRC reconfiguration message, or DCI containing the correction table from the second node via the third node.

[0340] The first node can obtain an offset value for the sensor based on the correction table and the correction offset table (S4540). The first node can select one of the multiple correction values ​​included in the correction table, and obtain an offset value related to the selected correction value from among the multiple offset values ​​included in the correction offset table as an offset value for the sensor.

[0341] The first node can obtain a first measurement value through a sensor (S4550).

[0342] The first node can obtain a final predicted value for the sensor based on the offset value and the first measurement value for the sensor (S4560). The first node can obtain the final predicted value for the sensor based on a Kalman filter or an extended Kalman filter.

[0343] The first node can obtain a prior prediction value for the sensor, calculate a Kalman gain based on the prior prediction value, and obtain a final prediction value for the sensor based on the offset value for the sensor, the first measurement value, and the Kalman gain.

[0344] The first node can perform at least one of beam prediction or beam tracking based on the final prediction value (S4570). The terminal can adjust the beam angle based on the final prediction value. The terminal can perform beam prediction and beam tracking based on the adjusted beam angle.

[0345] Meanwhile, when the first node is out of a specific area, the first node can transmit second location information of the first node to the second node, receive a correction information utilization off message including information regarding use of a correction table from the second node, obtain a second measurement value through the sensor, and perform at least one of beam prediction or beam tracking based on the second measurement value.

[0346] Figure 46 is a flowchart of a signal transmission and reception method according to another embodiment of the present disclosure.

[0347] In Fig. 46, the first node may be the core network described above, the second node may be the terminal described above, and the third node may be the base station or TRP described above.

[0348] The first node can receive terminal capability information from the second node (S4610). The first node can receive terminal capability information including information about a sensor included in the second node from the second node.

[0349] The first node can receive the first location information of the second node from the second node (S4620). Based on the first location information, the first node can determine whether the second node has entered a specific area (S4630).

[0350] If the second node determines that it has not entered a specific area (NO in S4630), the first node can return to step S4620 and receive the first location information from the second node.

[0351] If the second node determines that it has entered a specific area (e.g., S4630), the first node may select a correction table for the central sensor among multiple correction tables based on terminal capability information (S4640). The multiple correction tables may be generated based on factors related to the sensor type and its measurement accuracy.

[0352] The first node may transmit a correction table regarding the sensor to the second node (S4650). The first node may transmit an RRC message, an RRC reconfiguration message, or a DCI containing the correction table regarding the sensor to the second node via the third node.

[0353] The first node can receive the second location information of the second node from the second node (S4660).

[0354] The first node can determine whether the second node has left a specific area based on the second location information (S4670).

[0355] If the second node does not leave the specific area, the first node can return to step S4660 and receive the second location information of the second node from the second node.

[0356] If the second node leaves a specific area (example of S4670), a correction information utilization off message including information on the use of the correction table can be transmitted to the second node (S4680).

[0357] The embodiments described above are combinations of the components and features of the present disclosure in a predetermined form. Each component or feature should be considered optional unless explicitly stated otherwise. Each component or feature may be implemented without being combined with other components or features. Furthermore, it is also possible to combine some components and / or features to form an embodiment of the present disclosure. The order of operations described in the embodiments of the present disclosure may be changed. Some components or features of one embodiment may be included in another embodiment or may be replaced with corresponding components or features of another embodiment. It is self-evident that claims that do not have an explicit citation relationship in the scope of the patent may be combined to form an embodiment or may be incorporated as a new claim through a post-application amendment.

[0358] Embodiments according to the present specification may be implemented by various means, for example, hardware, firmware, software, or a combination thereof. In the case of hardware implementation, an embodiment of the present invention may be implemented by one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, etc.

[0359] When implemented via firmware or software, an embodiment of the present specification may be implemented in the form of a module, procedure, function, or the like that performs the functions or operations described above. The software code may be stored in memory and executed by a processor. The memory may be located within or external to the processor and may exchange data with the processor via various known means.

[0360] It will be apparent to those skilled in the art that this specification may be embodied in other specific forms without departing from the essential characteristics thereof. Therefore, the foregoing detailed description should not be construed in any way as limiting but rather as illustrative. The scope of this specification should be determined by a reasonable interpretation of the appended claims, and all changes within the scope of equivalents herein are intended to be included within the scope of this specification.

Claims

1. In a method of operating a first node in a wireless communication system, A step of transmitting terminal capability information including information about a sensor included in the first node to the second node; A step of transmitting first location information of the first node to the second node when the first node enters a specific area; A step of receiving a correction table regarding the sensor from the second node; A step of obtaining an offset value for the sensor based on the above correction information and a preset calibration offset table; A step of obtaining a first measurement value through the above sensor; A step of obtaining a final predicted value for the sensor based on the offset value and the first measurement value for the sensor; and A method for performing at least one of beam prediction or beam tracking based on the final prediction value.

2. In paragraph 1, The above correction table is, A method generated based on factors related to the type of the sensor and the measurement accuracy of the sensor.

3. In paragraph 1, The step of receiving a correction table regarding the sensor from the second node comprises: A method comprising the step of receiving, from the second node, an RRC (Radio resource control) message, an RRC reconfiguration message, or DCI (downlink control information) including the correction table through a third node.

4. In paragraph 1, The step of obtaining the offset value for the sensor based on the above correction table and the preset correction offset table is: a step of selecting one of a plurality of correction values ​​included in the correction table; and A method comprising the step of obtaining an offset value related to the selected correction value among a plurality of offset values ​​included in the correction offset table as the offset value for the sensor.

5. In paragraph 1, The step of obtaining a final predicted value for the sensor based on the offset value and the first measurement value for the sensor is as follows: A method performed based on a Kalman filter or an extended Kalman filter.

6. In paragraph 1, A step of obtaining a pre-prediction value for the above sensor; and A method further comprising a step of calculating a Kalman gain based on the prior prediction value for the sensor.

7. In paragraph 6, The step of obtaining a final predicted value for the sensor based on the offset value and the first measurement value for the sensor is as follows: A method comprising the step of obtaining the final predicted value for the sensor based on the offset value for the sensor, the first measurement value, and the Kalman gain.

8. In paragraph 1, If the above first node leaves the above specific area, A step of transmitting second location information of the first node to the second node; A step of receiving a correction information utilization off message including information regarding the use of the correction information from the second node; A step of obtaining a second measurement value through the above sensor; and A method further comprising the step of performing at least one of the beam prediction or the beam tracking based on the second measurement value.

9. In the first node operating in the communication system, One or more transmitters and receivers; One or more processors controlling the one or more transceivers; and A memory comprising one or more instructions to be executed by said one or more processors, One or more of the above commands, In a method of operating a first node in a wireless communication system, A step of transmitting terminal capability information including information about a sensor included in the first node to the second node; A step of transmitting first location information of the first node to the second node when the first node enters a specific area; A step of receiving a correction table regarding the sensor from the second node; A step of obtaining an offset value for the sensor based on the above correction information and a preset calibration offset table; A step of obtaining a first measurement value through the above sensor; A step of obtaining a final predicted value for the sensor based on the offset value and the first measurement value for the sensor; and A first node that performs at least one of beam prediction and beam tracking based on the final prediction value.

10. In a method of operating a first node in a wireless communication system, A step of receiving terminal capability information including information about a sensor included in the second node from the second node; A step of transmitting first location information of the second node from the second node; A step of determining whether the second node has entered a specific area based on the first location information; When it is determined that the second node has entered the specific area, a step of selecting a correction table for the sensor among a plurality of correction tables based on the terminal capability information; and A method comprising the step of transmitting a correction table regarding the sensor to the second node.

11. In paragraph 10, The above correction table is, A method generated based on factors related to the type of the sensor and the measurement accuracy of the sensor.

12. In paragraph 10, The step of transmitting the correction table regarding the sensor to the second node is: A method comprising the step of transmitting, to the second node, an RRC (Radio resource control) message, an RRC reconfiguration message, or DCI (downlink control information) including the correction table through a third node.

13. In paragraph 10, A step of receiving second location information of the second node from the second node; A step of determining whether the second node has left the specific area based on the second location information; and A method comprising the step of transmitting, to the second node, a correction information utilization off message including information regarding the use of the correction table when the second node leaves the specific area.

14. In a first node operating in a communication system, One or more transmitters and receivers; One or more processors controlling the one or more transceivers; and A memory comprising one or more instructions to be executed by said one or more processors, One or more of the above commands, A step of receiving terminal capability information including information about a sensor included in the second node from the second node; A step of transmitting first location information of the second node from the second node; A step of determining whether the second node has entered a specific area based on the first location information; When it is determined that the second node has entered the specific area, a step of selecting a correction table for the sensor among a plurality of correction tables based on the terminal capability information; and A first node comprising a step of transmitting a correction table regarding the sensor to the second node.

15. A first device comprising one or more memories and one or more processors functionally connected to the one or more memories, The one or more processors of the first device, Transmit terminal capability information including information about a sensor included in the first device to the second device, To the second device, when the first device enters a specific area, transmit the first location information of the first device, Receive a correction table for the sensor from the second device, Obtain an offset value for the sensor based on the above calibration information and a preset calibration offset table, Obtaining a first measurement value through the above sensor, Obtaining a final predicted value for the sensor based on the offset value and the first measurement value for the sensor, and A first device operable to perform at least one of beam prediction or beam tracking based on the final prediction value.

16. In one or more non-transitory computer-readable media storing one or more instructions, Transmit terminal capability information including information about a sensor included in the first device to the second device, To the second device, when the first device enters a specific area, transmit the first location information of the first device, Receive a correction table for the sensor from the second device, Obtain an offset value for the sensor based on the above calibration information and a preset calibration offset table, Obtaining a first measurement value through the above sensor, Obtaining a final predicted value for the sensor based on the offset value and the first measurement value for the sensor, and A computer-readable medium operable to perform at least one of beam prediction or beam tracking based on the final prediction value.

Images