Method and device for ai-based beam management using motion information

Abstract

Provided are a method for a first device to perform wireless communication and a device supporting same. The method may comprise the steps of: acquiring information related to a motion of the first device on the basis of at least one sensor; transferring the information related to the motion to an artificial intelligence (AI) model; and performing beam management on the basis of a result acquired from the AI model.

Description

AI-based beam management method and device utilizing motion information

[0001] The present disclosure relates to a wireless communication system.

[0002] 5G NR, the successor to LTE (long-term evolution), is a new clean-slate mobile communications system characterized by high performance, low latency, and high availability. 5G NR can utilize all available spectrum resources, from low-frequency bands below 1 GHz, mid-frequency bands between 1 GHz and 10 GHz, and high-frequency (millimeter wave) bands above 24 GHz.

[0003] 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 Internet of Things (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 1 below. For example, Table 1 can represent an example of the requirements of a 6G system.

[0004] Per device peak data rate1 TbpsE2E latency1 msMaximum spectral efficiency100bps / HzMobility supportUp to 1000km / hrSatellite integrationFullyAIFullyAutonomous vehicleFullyXRFullyHaptic CommunicationFully

[0005] In one embodiment, a method for a first device to perform wireless communication is provided. The method may include: obtaining information related to the motion of the first device based on at least one sensor; transmitting the information related to the motion to an artificial intelligence (AI) model; and performing beam management based on a result obtained from the AI ​​model.

[0006] In one embodiment, a first device configured to perform wireless communication is provided. The first device may include at least one transceiver; at least one processor; and at least one memory connected to the at least one processor and storing instructions. For example, the instructions, when executed by the at least one processor, may cause the first device to: acquire information related to a motion of the first device based on at least one sensor; transmit the information related to the motion to an artificial intelligence (AI) model; and perform beam management based on a result acquired from the AI ​​model.

[0007] In one embodiment, a processing device configured to control a first device is provided. The processing device comprises at least one processor; and at least one memory coupled to the at least one processor and storing instructions, wherein the instructions, when executed by the at least one processor, cause the first device to: obtain information related to a motion of the first device based on at least one sensor; transmit the information related to the motion to an artificial intelligence (AI) model; and perform beam management based on a result obtained from the AI ​​model.

[0008] In one embodiment, a non-transitory computer-readable storage medium having instructions recorded thereon is provided. The instructions, when executed, cause a first device to: obtain information related to motion of the first device based on at least one sensor; transmit the information related to motion to an artificial intelligence (AI) model; and perform beam management based on results obtained from the AI ​​model.

[0009] FIG. 1 illustrates a communication structure that can be provided in a 6G system according to one embodiment of the present disclosure.

[0010] FIG. 2 illustrates an electromagnetic spectrum according to one embodiment of the present disclosure.

[0011] FIG. 3 illustrates an example of a typical scenario of an NTN based on a transparent payload, according to one embodiment of the present disclosure.

[0012] FIG. 4 illustrates an example of a typical scenario of an NTN based on a regenerative payload, according to one embodiment of the present disclosure.

[0013] Figure 5 shows an example of a mobility information format.

[0014] Figure 6 shows an example of utilizing the path prediction data element of BSM (Basic Safety Message).

[0015] Figure 7 shows an example of expressing a path as a list of points.

[0016] Figure 8 shows an example of mobility information being transmitted from a peripheral electronic device to a communication module.

[0017] Figure 9 shows an example of mobility information being transmitted from the application layer to the access layer within the communication module.

[0018] Figure 10 illustrates an example of interaction between mobility information and a base station.

[0019] Figure 11 shows an AI-based beam management method utilizing motion information.

[0020] FIG. 12 illustrates a method for a first device to perform wireless communication according to one embodiment of the present disclosure.

[0021] FIG. 13 illustrates a method for a second device to perform wireless communication according to one embodiment of the present disclosure.

[0022] Fig. 14 illustrates a communication system (1) according to one embodiment of the present disclosure.

[0023] FIG. 15 illustrates a wireless device according to an embodiment of the present disclosure.

[0024] FIG. 16 illustrates a signal processing circuit for a transmission signal according to one embodiment of the present disclosure.

[0025] FIG. 17 illustrates a wireless device according to one embodiment of the present disclosure.

[0026] FIG. 18 illustrates a mobile device according to one embodiment of the present disclosure.

[0027] FIG. 19 illustrates a vehicle or autonomous vehicle according to one embodiment of the present disclosure.

[0028] As used herein, "A or B" can mean "only A," "only B," or "both A and B." In other words, as used herein, "A or B" can be interpreted as "A and / or B." For example, as used herein, "A, B or C" can mean "only A," "only B," "only C," or "any combination of A, B and C."

[0029] As used herein, a slash ( / ) or a comma can mean "and / or." For example, "A / B" can mean "A and / or B." Accordingly, "A / B" can mean "only A," "only B," or "both A and B." For example, "A, B, C" can mean "A, B, or C."

[0030] In this specification, "at least one of A and B" may mean "only A", "only B" or "both A and B". Additionally, in this specification, the expressions "at least one of A or B" or "at least one of A and / or B" may be interpreted identically to "at least one of A and B".

[0031] Additionally, in this specification, “at least one of A, B and C” can mean “only A,” “only B,” “only C,” or “any combination of A, B and C.” Additionally, “at least one of A, B or C” or “at least one of A, B and / or C” can mean “at least one of A, B and C.”

[0032] Additionally, parentheses used herein may mean "for example." Specifically, when indicated as "control information (PDCCH)", "PDCCH" may be proposed as an example of "control information." In other words, "control information" in this specification is not limited to "PDCCH," and "PDCCH" may be proposed as an example of "control information." Furthermore, even when indicated as "control information (i.e., PDCCH)", "PDCCH" may be proposed as an example of "control information."

[0033] In the following explanation, ‘when, if, in case of’ can be replaced with ‘based on’.

[0034] Technical features individually described in a single drawing in this specification may be implemented individually or simultaneously.

[0035] In this specification, higher layer parameters may be parameters that are set for the terminal, preset, or predefined. For example, a base station or network may transmit higher layer parameters to the terminal. For example, higher layer parameters may be transmitted via radio resource control (RRC) signaling or medium access control (MAC) signaling.

[0036] In this specification, "configured or defined" may be interpreted as being configured or preset to a device through predefined signaling (e.g., SIB, MAC, RRC) from a base station or network. In this specification, "configured or defined" may be interpreted as being preset to a device.

[0037] The technology proposed in this specification can be used in various wireless communication systems such as CDMA (code division multiple access), FDMA (frequency division multiple access), TDMA (time division multiple access), OFDMA (orthogonal frequency division multiple access), and SC-FDMA (single carrier frequency division multiple access). CDMA can be implemented with wireless technologies such as UTRA (universal terrestrial radio access) or CDMA2000. TDMA can be implemented with wireless technologies such as GSM (global system for mobile communications) / GPRS (general packet radio service) / EDGE (enhanced data rates for GSM evolution). OFDMA can be implemented with wireless technologies such as IEEE (Institute of Electrical and Electronics Engineers) 802.11 (Wi-Fi), IEEE 802.16 (WiMAX), IEEE 802-20, E-UTRA (evolved UTRA), LTE (long term evolution), and 5G NR.

[0038] The technology proposed in this specification can be implemented with 6G wireless technology and applied to various 6G systems. For example, 6G systems can have key factors such as enhanced mobile broadband (eMBB), ultra-reliable low latency communications (URLLC), massive machine-type communication (mMTC), artificial intelligence (AI) integrated communication, tactile internet, high throughput, high network capacity, high energy efficiency, low backhaul and access network congestion, and enhanced data security.

[0039] FIG. 1 illustrates a communication structure that can be provided in a 6G system according to one embodiment of the present disclosure. The embodiment of FIG. 1 can be combined with various embodiments of the present disclosure.

[0040] New network characteristics in 6G may include:

[0041] - Satellite integrated network

[0042] - 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).

[0043] - Seamless integration of wireless information and energy transfer

[0044] - Ubiquitous super 3D connectivity: Access to networks and core network functions of drones and very low Earth orbit satellites will create super 3D connectivity in 6G ubiquitous.

[0045] Some general requirements for the new network characteristics of 6G, such as the above, may be as follows:

[0046] - small cell networks

[0047] - Ultra-dense heterogeneous network

[0048] - High-capacity backhaul

[0049] - 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.

[0050] - Softwarization and virtualization

[0051] Below, the core implementation technologies of the 6G system are described.

[0052] - Artificial Intelligence: Incorporating AI into communications can streamline and improve real-time data transmission. AI can use numerous analytics to determine how complex target tasks should be performed. This means AI can increase efficiency and reduce processing delays. Time-consuming tasks such as handovers, 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.

[0053] - THz communication (terahertz communication): Data rates can be increased by increasing the bandwidth. This can be achieved by utilizing sub-THz communication with a wide bandwidth and applying advanced massive MIMO technology. THz waves, also known as sub-millimeter waves, typically refer to the frequency range between 0.1 THz and 10 THz with corresponding wavelengths 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 lies in the far infrared (IR) frequency band. While part of the optical band, the 300 GHz to 3 THz band lies at the boundary of the optical band, immediately following the RF band. Therefore, this 300 GHz to 3 THz band exhibits similarities to RF. Figure 2 illustrates the electromagnetic spectrum according to one embodiment of the present disclosure. The embodiment of FIG. 2 can be combined with various embodiments of the present disclosure. Key characteristics of THz communications include (i) widely available bandwidth to support very high data rates, and (ii) high path loss at high frequencies (highly directional antennas are essential). The narrow beamwidth generated by the highly directional antenna reduces interference. The small wavelength of THz signals allows for a much 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.

[0054] - Large-scale MIMO technology

[0055] - Hologram beamforming (HBF)

[0056] - Optical wireless technology

[0057] - Free-space optical transmission backhaul network (FSO backhaul network)

[0058] - Quantum communication

[0059] - Cell-free communication

[0060] - Integration of wireless information and power transmission

[0061] - Integration of wireless communication and sensing

[0062] - Integrated access and backhaul network

[0063] - Big data analysis

[0064] - Reconfigurable intelligent surface

[0065] - metaverse

[0066] - Block chain

[0067] Unmanned aerial vehicles (UAVs): UAVs, or drones, will be a key element in 6G wireless communications. In most cases, high-speed data wireless connectivity can be provided using UAV technology. Base stations (BSs) can be installed on UAVs to provide cellular connectivity. UAVs may offer specific capabilities not found in fixed BS 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 cannot provide services in volatile environments. 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.

[0068] - Autonomous driving (self-driving): V2X (vehicle to everything), a key element in building autonomous driving infrastructure, can be a technology that allows cars to communicate and share with various elements on the road for autonomous driving, such as vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) wireless communication. Fast transmission speeds and low-latency technologies are essential to maximize autonomous driving performance and ensure high safety. Furthermore, in the future, autonomous driving will go beyond simply providing warnings or guidance messages to drivers and may require active intervention in vehicle operation and direct control of the vehicle in dangerous situations. To this end, the amount of information that needs to be transmitted and received may become enormous, so 6G is expected to maximize autonomous driving with faster transmission speeds and lower latency than 5G.

[0069] - Non-terrestrial networks (NTN): NTN may refer to a network or network segment that uses radio frequency (RF) resources mounted on a satellite (or unmanned aerial system (UAS) platform). FIG. 3 illustrates an example of a typical NTN scenario based on a transparent payload according to an embodiment of the present disclosure. FIG. 4 illustrates an example of a typical NTN scenario based on a regenerative payload according to an embodiment of the present disclosure. The embodiments of FIG. 3 or FIG. 4 may be combined with various embodiments of the present disclosure. Referring to FIG. 3, a satellite (or UAS platform) may create a service link with a UE. The satellite (or UAS platform) may be connected to a gateway via a feeder link. The satellite may be connected to a data network via the gateway. A beam footprint may refer to an area where a signal transmitted by a satellite can be received. Referring to Figure 4, a satellite (or UAS platform) can establish a service link with a UE. A satellite (or UAS platform) connected to a UE can be connected to another satellite (or UAS platform) via an inter-satellite link (ISL). The other satellite (or UAS platform) can be connected to a gateway via a feeder link. Based on the playback payload, a satellite can be connected to a data network through another satellite and the gateway. If an ISL does not exist between a satellite and another satellite, a feeder link between the satellite and the gateway may be required. Figures 3 and 4 are merely examples of NTN scenarios, and NTN can be implemented based on various scenarios.For example, a satellite (or UAS platform) may implement a transparent or regenerative (with onboard processing) payload. For example, a satellite (or UAS platform) may generate multiple beams over a designated service area depending on the field of view of the satellite (or UAS platform). For example, the field of view of the satellite (or UAS platform) may vary depending on the onboard antenna diagram and minimum elevation angle. For example, a transparent payload may include radio frequency filtering, frequency conversion, and amplification. Therefore, the waveform signal repeated by the payload may not be altered. For example, a regenerative payload may include radio frequency filtering, frequency conversion and amplification, demodulation / decoding, switching and / or routing, and coding / modulation. For example, a regenerative payload may be substantially equivalent to equipping the satellite (or UAS platform) with all or part of the base station functionality.

[0070] - Integrated Sensing and Communication (ISAC): Wireless sensing is a technology that uses radio frequencies to determine the instantaneous linear velocity, angle, distance (range), etc. of an object, thereby obtaining information about the characteristics of the environment and / or objects within the environment. Because radio frequency sensing does not require a device to connect to the object through a network, it can provide a service for object positioning without a device. The ability to obtain range, velocity, and angle information from radio frequency signals can enable a wide range of new capabilities, such as various object detection, object recognition (e.g., vehicles, humans, animals, UAVs), and high-precision localization, tracking, and activity recognition. Wireless sensing services can provide information to a variety of industries (e.g., drones, smart homes, V2X, factories, railways, public safety, etc.), enabling applications such as intruder detection, assisted vehicle steering and navigation, trajectory tracking, collision avoidance, traffic management, and health and traffic management. In some cases, wireless sensing can utilize non-3GPP type sensors (e.g., radar, cameras) to further support 3GPP-based sensing. For example, the operation of wireless sensing services, i.e., sensing operations, may depend on the transmission, reflection, and scattering of wireless sensing signals. Therefore, wireless sensing offers an opportunity to enhance existing communication systems from a communication network to a wireless communication and sensing network.

[0071] Meanwhile, the Intelligent Transport System (ITS) has recently expanded beyond traditional modes of transportation (e.g., trains, automobiles, etc.) to encompass expanded mobility, such as drones and mobile robots. Connectivity between vehicles and infrastructure is a critical topic in ITS, and significant development is being conducted to improve the communication performance of connected mobility.

[0072] For example, by combining internal and external communication systems within a vehicle and exchanging data or information based on this, safety or traffic efficiency can be improved from an ITS perspective. For example, in a car, the vehicle's internal communication system can handle communication between sensors and electronic control systems that collect information about the vehicle's internal operating status, vehicle movement, and surrounding environment. The vehicle's external communication system can handle communication between the communication infrastructure and other mobility systems.

[0073] For example, the internal communication systems of mobility devices exist in a variety of systems developed in-house by manufacturers and component suppliers. Representative communication systems include MOST (Media Oriented System Transport), LIN (Local Interconnected Network), and CAN (Controller Area Network), along with performance-enhanced versions such as CAN FD (Flexible Data-rate) and FlexRay.

[0074] For example, external communication systems for mobility include direct communication (e.g., Dedicated Short-Range Communication (DSRC), 3GPP PC5) and mobile communication (e.g., LTE or NR), and standards organizations such as IEEE and 3GPP are developing new technologies to improve communication performance. Recently, there has been active discussion on methods to improve communication performance based on artificial intelligence (AI) models. That is, the goal is to enhance CSI (Channel State Information) feedback, beam management, or positioning accuracy by utilizing learned AI models.

[0075] Meanwhile, research is currently underway to train AI models using limited information within the communication module or information exchanged between base stations (e.g., past CSI or location information). However, due to the nature of mobility, which allows for diverse movements in 3D space and the uncertainty of the body's mobility and / or movement, CSI management, beam management, and precision positioning can be challenging. For example, in the case of automobiles, rolling can occur when the vehicle passes over an uneven road surface. Alternatively, pitching can occur due to sudden stops or starts. Alternatively, for example, in the case of drones, turbulence during flight can prevent smooth flight. For example, in the aforementioned cases, unpredictable body movements within the communication module mounted on the body can cause problems with CSI and beam management. Alternatively, for example, a sudden rotation of the fuselage may cause the performance of the communication module and antenna performing beam forming to deteriorate.

[0076] In the present disclosure, a method for extracting movement information of a mobility when the mobility communicates with an external entity (e.g., a base station and / or a Road Side Unit (RSU) and / or an ITS terminal) and transmitting it to a communication module, and a device supporting the same are proposed as follows.

[0077] In addition, in the present disclosure, when information is transmitted, the format of information transmitted from an electronic device (e.g., a gateway or a motion control unit) to a communication terminal in the case of an internal network of mobility, or the format of information transmitted from an application layer to an access layer within the communication terminal, is proposed as follows.

[0078] For example, among the vast amount of information exchanged within the mobility network, data that can represent mobility information for use in the AI ​​model of the communication module can be extracted. In this case, for example, the mobility information for a defined mobility can be defined as 1) the current motion information of the mobility (e.g., 3-axis speed or 3-axis rotation) and 2) the expected and / or planned path of the mobility.

[0079] For example, the motion information of the first mobility may be a three-axis linear motion vector and a three-axis rotational motion vector calculated from measurements of internally installed sensors (e.g., a GNSS sensor, an inertial measurement unit (IMU), an accelerometer, a gyroscope sensor, a geomagnetic sensor, a gravity sensor, or a rotation vector sensor, etc.). In addition, for example, the mobility may generate information by processing measurements obtained from sensors, and the processed information may be input into an AI model as described below. For example, the mobility may perform dead reckoning by performing calculations based on measurements obtained from a GNSS sensor and measurements obtained from an IMU sensor, and the processed information obtained based on this may be used as an input parameter of an AI model as described below.

[0080] For example, the predicted path or planned path of the second mobility may mean predicted path information calculated based on the current location, speed, direction, driver or operator input, etc., for the future movement path of the mobility, or pre-planned path information considering movement to the destination and obstacle avoidance. If, for example, a communication module performs communication between mobilities or infrastructures including V2X and exchanges the predicted path or planned path at this time, this information can be used instead. For example, in the Society of Automotive Engineers (SAE) standard of V2X, if a mobility transmits a Basic Safety Message (BSM), the DE_PathPrediction value configured in the message can be utilized.

[0081] Figure 5 illustrates an example of a mobility information format. For example, the two types of mobility information described above can be exchanged in a format like Figure 5.

[0082] Referring to FIG. 5, for example, the first motion information may include a velocity vector of the x, y, and z axes and a rotation vector of the x, y, and z axes, and at this time, the heading value and / or WGS84 (World Geodetic System 1984), which are the direction of movement of the mobility, may be used as the cardinal direction of the x, y, and z. For example, the heading direction of the mobility may be defined as the x-axis, the axis perpendicular to the x-axis in a plane perpendicular to the direction of gravity as the y-axis, and the direction of gravity as the z-axis. For example, if WGS84 is used as the standard, the origin may be set at the center of mass of the Earth, the x-axis may be set in the direction of the intersection of the Greenwich meridian and the equator, the y-axis may be set in the direction of 90 degrees east longitude, and the z-axis may be set in the direction of the North Pole, and the velocity vector and rotation vector may be expressed. For example, for the elements of the velocity vector and the rotation vector, they can be expressed as absolute values ​​and / or relative values ​​and / or change probability [%].

[0083] For example, the second path information may be a predicted path or a planned path calculated internally, and the path may be represented by a list of location points or a list of radii of curvature, or may be represented in other ways.

[0084] Figure 6 illustrates an example of utilizing the path prediction data element of the BSM (Basic Safety Message). The embodiment of Figure 6 can be combined with various embodiments of the present disclosure. For example, if a communication terminal exchanges a BSM, it can be used as illustrated in Figure 6. For example, the PathPrediction data element of the BSM can be composed of a list of radii of curvature.

[0085] Figure 7 illustrates an example of representing a path as a list of points. The embodiment of Figure 7 can be combined with various embodiments of the present disclosure.

[0086] For example, the confidence value / level commonly included in the above two pieces of information (motion information, path information) can represent information such as certainty or expected error.

[0087] For example, the AI ​​model may be located in the application layer or the access layer of the communication module. For example, in the former case (where the AI ​​model is located in the application layer of the communication module), the mobility information derived above can be transmitted from the internal mobility network to the communication module.

[0088] FIG. 8 illustrates an example of mobility information being transmitted from a peripheral electronic device to a communication module. The embodiment of FIG. 8 can be combined with various embodiments of the present disclosure.

[0089] Referring to FIG. 8, the above-described mobility information can be transmitted from another electronic device to a telematic module via a gateway.

[0090] FIG. 9 illustrates an example of mobility information being transmitted from the application layer within a communication module to the access layer. The embodiment of FIG. 9 can be combined with various embodiments of the present disclosure.

[0091] Referring to FIG. 9, if the AI ​​model is located in the access layer of the communication module, each sensor information can be collected in the application layer of the communication module, and mobility mobility information can be calculated and then transmitted to the access layer within the communication module.

[0092] Additionally, it can be used as an input parameter of the AI ​​model of a base station, for example, in communication between base stations.

[0093] Figure 10 illustrates an example of interaction between mobility information and a base station. The embodiment of Figure 10 can be combined with various embodiments of the present disclosure.

[0094] Referring to FIG. 10, a mobility can report to a base station that its mobility information is being provided to a communication module in an internal network, and the base station can request or instruct the method and / or conditions for transmitting the mobility information of the mobility being exchanged to the base station. Furthermore, for example, a mobility can transmit its own mobility information to the base station at the request or instruction of the base station.

[0095] For example, mobility information transmitted to a communication module can be utilized as an input parameter of an AI model to enable external communication with improved communication performance (e.g., beam and / or CSI (channel state information) prediction). Furthermore, for example, when there is interaction between base stations (e.g., reporting, requesting, responding, etc.), mobility information of the mobility can be utilized as an input parameter of an AI model within the base station.

[0096] Fig. 11 illustrates an AI-based beam management method utilizing motion information. The embodiment of Fig. 11 can be combined with various embodiments of the present disclosure.

[0097] Referring to FIG. 11, UE A and UE B may be mobilities that perform beam-based communication. For example, UE A and UE B may be mobilities that can move in a three-dimensional space, such as vehicles, drones, and mobile robots. For example, UE A may select a beam that can be paired with UE B by transmitting an RS (reference signal) to UE B. That is, for example, UE A may perform beam forming or beam switching to perform beam-based communication with UE B. For example, UE A may perform beam forming to determine a beam aligned with UE B (or a beam matched with the beam of UE B) (1110), and transmit data, etc. to UE B based on the beam (1110). Meanwhile, based on the state of UE A or the driving / flight environment around UE A, a change in the motion of UE A that UE A did not anticipate, such as rolling or pitching, may occur (1120). For example, the beam (1110) determined by UE A performing beam forming may no longer be aligned with UE B (1130). That is, for example, if UE A does not maintain the beam (1110) and perform beam management, beam-based communication with UE B may become impossible. In this case, for example, UE A may obtain information related to the motion of UE A based on sensors installed inside. For example, the information related to the motion may include processed information obtained by performing a calculation based on one or more measurement values ​​as well as measurement values ​​obtained from sensors installed inside UE A. For example, UE A may use information related to the motion of UE A as an input parameter of an AI model.For example, the information used as an input parameter of the AI ​​model may be processed information obtained by performing a calculation based on one or more measurement values ​​as well as measurement values ​​obtained from sensors. For example, UE A may perform dead reckoning by performing a calculation based on measurement values ​​obtained from a GNSS sensor installed inside UE A and measurement values ​​obtained from an IMU sensor, and may use the processed information obtained based on this as an input parameter of the AI ​​model. Meanwhile, UE A may input information related to the motion of UE A into the AI ​​model to obtain information related to beam prediction. For example, UE A may perform beam forming again based on the information related to beam prediction obtained from the AI ​​model. For example, UE A may perform beam forming again to determine a beam aligned with UE B (or a beam matched with the beam of UE B) (1140), and transmit data, etc. to UE B based on the beam (1140). Alternatively, for example, UE A may perform beamforming again to determine the thickness of the beam associated with the new beamforming. For example, UE A may measure the communication status (or quality) based on the beam associated with the previous beamforming and determine whether the communication status (or quality) is below (or above) a threshold. For example, if UE A determines that the communication status (or quality) based on the beam associated with the previous beamforming is degraded, UE A may perform beamforming again and determine the thickness of the beam associated with the newly performed beamforming to be sharper or thicker than the thickness of the beam associated with the previous beamforming so as to improve the communication status (or quality).That is, for example, UE A can perform beam management to maintain beam-based communication with UE B based on the results obtained by inputting information related to the motion of UE A into an AI model.

[0098] According to various embodiments of the present disclosure, the mobility information of the transmitted mobility can be utilized as input to an AI (Artificial Intelligence) model installed within a communication module, which can improve communication performance (e.g., channel state information (CSI) prediction, beam prediction, or positioning accuracy). Specifically, even if an unexpected motion occurs in the mobility, the mobility can input the motion information into the AI ​​model and determine the direction or thickness of the beam from the acquired information, thereby minimizing operations such as beam measurement or beam sweeping that the mobility must perform to maintain beam-based communication. That is, for example, resources related to beam RS (beam reference signal) for beam pairing or beam selection can be efficiently allocated. Or, for example, power of the mobility can be saved by efficiently performing operations related to beam management. Alternatively, the reliability of beam-based communication can be ensured by reducing delays that may occur, for example, by performing operations related to beam management.

[0099] FIG. 12 illustrates a method for a first device to perform wireless communication according to an embodiment of the present disclosure. The embodiment of FIG. 12 may be combined with various embodiments of the present disclosure.

[0100] Referring to FIG. 12, in step S1210, the first device may obtain information related to the motion of the first device based on at least one sensor. In step S1220, the first device may transmit the information related to the motion to an artificial intelligence (AI) model. In step S1230, the first device may perform beam management based on the results obtained from the AI ​​model.

[0101] Additionally, for example, the first device can perform first beam forming. For example, whether to maintain a beam associated with the first beam forming for the beam management can be determined based on a result obtained from the AI ​​model. For example, based on the fact that the direction of the beam associated with the first beam forming is not aligned with that of the second device, second beam forming for the beam management can be performed using the result obtained from the AI ​​model. For example, the direction of the beam associated with the second beam forming can be determined based on information related to beam prediction obtained from the AI ​​model. For example, based on the information related to beam prediction, the direction of the beam associated with the second beam forming can be determined to be a more aligned direction than the direction of the beam associated with the first beam forming. For example, the thickness of the beam associated with the second beam forming can be determined based on information related to beam prediction obtained from the AI ​​model. In this case, for example, based on information related to the beam prediction, the thickness of the beam related to the second beam forming may be determined to be sharper or thicker than the thickness of the beam related to the first beam forming. In this case, for example, the thickness of the beam related to the second beam forming may be determined based on the state of communication quality based on the beam related to the first beam forming. For example, based on the information related to the motion being transmitted to the AI ​​model, information related to at least one of the direction, angle, power, covered range, or thickness of the beam for beam management may be acquired.

[0102] For example, the motion-related information may include at least one of (i) information related to a three-axis linear motion vector of the first device, (ii) information related to a three-axis rotational motion vector of the first device, or (iii) information related to reliability.

[0103] For example, information related to the motion may be generated based on a value measured by at least one sensor, such as at least one of a global navigation satellite system (GNSS) sensor, an inertial measurement unit (IMU) sensor, an acceleration sensor, a gyroscope sensor, a geomagnetic sensor, a gravity sensor, or a rotation vector sensor.

[0104] For example, based on the results obtained from the AI ​​model, the state of a channel related to a beam determined based on the beam management can be predicted.

[0105] For example, based on the AI ​​model being located in the application layer of the first device, information related to the motion may be transmitted to a telematic module related to the application layer of the first device through the internal network of the first device.

[0106] For example, based on the AI ​​model being located in the access layer of the first device, information related to the motion can be transmitted from the application layer of the first device to the access layer of the first device.

[0107] Additionally, for example, the first device may transmit information related to at least one of a predicted path or a planned path of the first device to the AI ​​model. For example, information related to beam management may be obtained from the AI ​​model based on (i) information related to the motion, and (ii) information related to at least one of the predicted path or the planned path.

[0108] The proposed method can be applied to devices according to various embodiments of the present disclosure. First, the processor (102) of the first device (100) can obtain information related to the motion of the first device based on at least one sensor. Then, the processor (102) of the first device (100) can transfer the information related to the motion to an artificial intelligence (AI) model. Then, the processor (102) of the first device (100) can perform beam management based on the results obtained from the AI ​​model.

[0109] According to one embodiment of the present disclosure, a first device configured to perform wireless communication may be provided. For example, the first device may include at least one transceiver; at least one processor; and at least one memory connected to the at least one processor and storing instructions. For example, the instructions, based on execution by the at least one processor, may cause the first device to: acquire information related to a motion of the first device based on at least one sensor; transmit the information related to the motion to an artificial intelligence (AI) model; and perform beam management based on a result acquired from the AI ​​model.

[0110] According to one embodiment of the present disclosure, a processing device configured to control a first device may be provided. For example, the processing device may include at least one processor; and at least one memory connected to the at least one processor and storing instructions. For example, the instructions, based on execution by the at least one processor, may cause the first device to: acquire information related to a motion of the first device based on at least one sensor; transmit the information related to the motion to an artificial intelligence (AI) model; and perform beam management based on a result acquired from the AI ​​model.

[0111] According to one embodiment of the present disclosure, a non-transitory computer-readable storage medium having instructions recorded thereon may be provided. For example, the instructions, when executed, may cause a first device to: acquire information related to the motion of the first device based on at least one sensor; transmit the information related to the motion to an artificial intelligence (AI) model; and perform beam management based on results obtained from the AI ​​model.

[0112] FIG. 13 illustrates a method for a second device to perform wireless communication according to an embodiment of the present disclosure. The embodiment of FIG. 13 may be combined with various embodiments of the present disclosure.

[0113] Referring to FIG. 13, in step S1310, the second device can obtain information related to the motion of the second device based on at least one sensor. In step S1320, the second device can transmit information related to the motion of the second device to the first device. In step S1330, the second device can perform beam management based on a result obtained from an artificial intelligence (AI) model of the first device. For example, the result obtained from the AI ​​model of the first device can be obtained based on information related to the motion of the second device being input into the AI ​​model of the first device.

[0114] The proposed method can be applied to devices according to various embodiments of the present disclosure. First, the processor (202) of the second device (200) can obtain information related to the motion of the second device based on at least one sensor. Then, the processor (202) of the second device (200) can control the transceiver (206) to transmit the information related to the motion of the second device to the first device. Then, the processor (202) of the second device (200) can perform beam management based on the result obtained from the artificial intelligence (AI) model of the first device. For example, the result obtained from the AI ​​model of the first device can be obtained based on the information related to the motion of the second device being input into the AI ​​model of the first device.

[0115] According to one embodiment of the present disclosure, a second device configured to perform wireless communication may be provided. For example, the second device may include at least one transceiver; at least one processor; and at least one memory connected to the at least one processor and storing instructions. For example, the instructions, based on execution by the at least one processor, may cause the second device to: acquire information related to the motion of the second device based on at least one sensor; transmit information related to the motion of the second device to a first device; and perform beam management based on a result acquired from an artificial intelligence (AI) model of the first device. For example, the result acquired from the AI ​​model of the first device may be acquired based on information related to the motion of the second device being input into the AI ​​model of the first device.

[0116] According to one embodiment of the present disclosure, a processing device configured to control a second device may be provided. For example, the processing device may include at least one processor; and at least one memory connected to the at least one processor and storing instructions. For example, the instructions, based on execution by the at least one processor, may cause the second device to: obtain information related to the motion of the second device based on at least one sensor; transmit information related to the motion of the second device to a first device; and perform beam management based on a result obtained from an artificial intelligence (AI) model of the first device. For example, the result obtained from the AI ​​model of the first device may be obtained based on information related to the motion of the second device being input into the AI ​​model of the first device.

[0117] According to one embodiment of the present disclosure, a non-transitory computer-readable storage medium having instructions recorded thereon may be provided. For example, the instructions, when executed, may cause a second device to: obtain information related to the motion of the second device based on at least one sensor; transmit information related to the motion of the second device to a first device; and perform beam management based on a result obtained from an artificial intelligence (AI) model of the first device. For example, the result obtained from the AI ​​model of the first device may be obtained based on information related to the motion of the second device being input into the AI ​​model of the first device.

[0118] The various embodiments of the present disclosure may be combined with each other.

[0119] Below, a description is given of devices to which various embodiments of the present disclosure can be applied.

[0120] Although not limited thereto, the various descriptions, functions, procedures, proposals, methods and / or operational flowcharts disclosed in this document may be applied to various fields requiring wireless communication / connectivity (e.g., 5G) between devices.

[0121] 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.

[0122] FIG. 14 illustrates a communication system (1) according to one embodiment of the present disclosure. The embodiment of FIG. 14 can be combined with various embodiments of the present disclosure.

[0123] Referring to FIG. 14, a communication system (1) to which various embodiments of the present disclosure are applied includes a wireless device, a base station, and a network. Here, the wireless device refers to a device that performs communication using a wireless access technology (e.g., 5G NR (New RAT), LTE (Long Term Evolution)) 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 device / server (400). 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 vehicle may include an Unmanned Aerial Vehicle (UAV) (e.g., a drone) and / or an Aerial Vehicle (AV) (e.g., an Advanced Air Mobility (AAM)). The XR device may include an Augmented Reality (AR) / Virtual Reality (VR) / Mixed Reality (MR) device, and may be implemented in the form of a Head-Mounted Device (HMD), a Head-Up Display (HUD) equipped in a vehicle, a television, a smartphone, a computer, a wearable device, a home appliance, a digital signage, a vehicle, a robot, etc. The portable device may include a smartphone, a smart pad, a wearable device (e.g., a smart watch, smart glasses), a computer (e.g., a laptop, etc.), etc. The home appliance may include a TV, a refrigerator, a washing machine, etc. The IoT device may include a sensor, a smart meter, etc. For example, a base station and a network may also be implemented as a wireless device, and a specific wireless device (200a) may operate as a base station / network node to other wireless devices.

[0124] Here, the wireless communication technology implemented in the wireless devices (100a to 100f) 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 (100a to 100f) 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 devices (100a to 100f) 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.

[0125] Wireless devices (100a to 100f) can be connected to a network (300) via a base station (200). Artificial Intelligence (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 (400) via the network (300). The network (300) can be configured using a 3G network, a 4G (e.g., LTE) network, a 5G (e.g., NR) network, etc. The wireless devices (100a to 100f) can communicate with each other via the base station (200) / network (300), but can also communicate directly (e.g., sidelink communication) without going through the base station / network. 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 (e.g., sensors) can communicate directly with other IoT devices (e.g., sensors) or other wireless devices (100a to 100f).

[0126] Wireless communication / connection (150a, 150b, 150c) can be established between wireless devices (100a~100f) / base stations (200), and base stations (200) / base stations (200). Here, wireless communication / connection can be achieved 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 wireless communication / connection (150a, 150b, 150c), wireless devices and base stations / wireless devices, and base stations and base stations can transmit / receive wireless signals to each other. For example, wireless communication / connection (150a, 150b, 150c) can transmit / receive signals through various physical channels. To this end, at least some of various configuration information setting processes for transmitting / receiving wireless signals, various signal processing processes (e.g., channel encoding / decoding, modulation / demodulation, resource mapping / demapping, etc.), and resource allocation processes can be performed based on various proposals of the present disclosure.

[0127] FIG. 15 illustrates a wireless device according to an embodiment of the present disclosure. The embodiment of FIG. 15 may be combined with various embodiments of the present disclosure.

[0128] Referring to FIG. 15, the first wireless device (100) and the second wireless device (200) can transmit and receive wireless signals via various wireless access technologies (e.g., LTE, NR). Here, {the first wireless device (100), the second wireless device (200)} can correspond to {the wireless device (100x), the base station (200)} and / or {the wireless device (100x), the wireless device (100x)} of FIG. 14.

[0129] A first wireless device (100) includes one or more processors (102) and one or more memories (104), and may further include one or more transceivers (106) and / or one or more antennas (108). The processor (102) controls the memories (104) and / or the transceivers (106), and may be configured to implement the descriptions, functions, procedures, proposals, methods, and / or operational flowcharts disclosed in this document. For example, the processor (102) may process information in the memory (104) to generate first information / signal, and then transmit a wireless signal including the first information / signal via the transceiver (106). Furthermore, the processor (102) may receive a wireless signal including second information / signal via the transceiver (106), and then store information obtained from signal processing of the second information / signal in the memory (104). The memory (104) may be connected to the processor (102) and may store various information related to the operation of the processor (102). For example, the memory (104) may perform some or all of the processes controlled by the processor (102), or may store software code including commands for performing the descriptions, functions, procedures, proposals, methods, and / or operation flowcharts disclosed in this document. Here, the processor (102) and the memory (104) may be part of a communication modem / circuit / chip designed to implement a wireless communication technology (e.g., LTE, NR). The transceiver (106) may be connected to the processor (102) and may transmit and / or receive wireless signals via one or more antennas (108). The transceiver (106) may include a transmitter and / or a receiver. The transceiver (106) may be used interchangeably with an RF (Radio Frequency) unit. In the present disclosure, a wireless device may also mean a communication modem / circuit / chip.

[0130] A second wireless device (200) includes one or more processors (202), one or more memories (204), and may further include one or more transceivers (206) and / or one or more antennas (208). The processor (202) controls the memories (204) and / or the transceivers (206), and may be configured to implement the descriptions, functions, procedures, proposals, methods, and / or operational flowcharts disclosed in this document. For example, the processor (202) may process information in the memory (204) to generate third information / signals, and then transmit a wireless signal including the third information / signals via the transceivers (206). In addition, the processor (202) may receive a wireless signal including fourth information / signals via the transceivers (206), and then store information obtained from signal processing of the fourth information / signals in the memory (204). The memory (204) may be connected to the processor (202) and may store various information related to the operation of the processor (202). For example, the memory (204) may perform some or all of the processes controlled by the processor (202), or may store software code including commands for performing the descriptions, functions, procedures, proposals, methods, and / or operation flowcharts disclosed in this document. Here, the processor (202) and the memory (204) may be part of a communication modem / circuit / chip designed to implement wireless communication technology (e.g., LTE, NR). The transceiver (206) may be connected to the processor (202) and may transmit and / or receive wireless signals via one or more antennas (208). The transceiver (206) may include a transmitter and / or a receiver. The transceiver (206) may be used interchangeably with an RF unit. In the present disclosure, a wireless device may also mean a communication modem / circuit / chip.

[0131] Hereinafter, the hardware elements of the wireless device (100, 200) will be described in more detail. Although not limited thereto, one or more protocol layers may be implemented by one or more processors (102, 202). For example, one or more processors (102, 202) may implement one or more layers (e.g., functional layers such as PHY, MAC, RLC, PDCP, RRC, SDAP). One or more processors (102, 202) 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 operation flowcharts disclosed in this document. One or more processors (102, 202) may generate messages, control information, data, or information according to the descriptions, functions, procedures, proposals, methods, and / or operation flowcharts disclosed in this document. One or more processors (102, 202) can 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 (106, 206). One or more processors (102, 202) can receive signals (e.g., baseband signals) from one or more transceivers (106, 206) and obtain PDUs, SDUs, messages, control information, data or information according to the descriptions, functions, procedures, proposals, methods and / or operational flowcharts disclosed herein.

[0132] One or more processors (102, 202) may be referred to as a controller, a microcontroller, a microprocessor, or a microcomputer. One or more processors (102, 202) 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 (102, 202). The descriptions, functions, procedures, proposals, methods, and / or operational flowcharts disclosed in this document may be implemented using firmware or software, and the firmware or software may be implemented to include modules, procedures, functions, etc. The descriptions, functions, procedures, suggestions, methods and / or operation flowcharts disclosed in this document may be implemented using firmware or software configured to perform one or more processors (102, 202) or stored in one or more memories (104, 204) and executed by one or more processors (102, 202). The descriptions, functions, procedures, suggestions, methods and / or operation flowcharts disclosed in this document may be implemented using firmware or software in the form of codes, instructions and / or sets of instructions.

[0133] One or more memories (104, 204) may be coupled to one or more processors (102, 202) and may store various forms of data, signals, messages, information, programs, codes, instructions, and / or commands. The one or more memories (104, 204) may be configured as ROM, RAM, EPROM, flash memory, hard drives, registers, cache memory, computer-readable storage media, and / or combinations thereof. The one or more memories (104, 204) may be located internally and / or externally to the one or more processors (102, 202). Additionally, the one or more memories (104, 204) may be coupled to the one or more processors (102, 202) via various technologies, such as wired or wireless connections.

[0134] One or more transceivers (106, 206) can transmit user data, control information, wireless signals / channels, etc., as mentioned in the methods and / or flowcharts of this document, to one or more other devices. One or more transceivers (106, 206) can receive user data, control information, wireless signals / channels, etc., as mentioned in the descriptions, functions, procedures, proposals, methods and / or flowcharts of this document, from one or more other devices. For example, one or more transceivers (106, 206) can be connected to one or more processors (102, 202) and can transmit and receive wireless signals. For example, one or more processors (102, 202) can control one or more transceivers (106, 206) to transmit user data, control information, or wireless signals to one or more other devices. Additionally, one or more processors (102, 202) may control one or more transceivers (106, 206) to receive user data, control information, or wireless signals from one or more other devices. Additionally, one or more transceivers (106, 206) may be coupled to one or more antennas (108, 208), and one or more transceivers (106, 206) 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 (108, 208). In this document, one or more antennas may be multiple physical antennas or multiple logical antennas (e.g., antenna ports). One or more transceivers (106, 206) can convert received user data, control information, wireless signals / channels, etc. from RF band signals to baseband signals in order to process the received user data, control information, wireless signals / channels, etc. using one or more processors (102, 202).One or more transceivers (106, 206) may convert user data, control information, wireless signals / channels, etc. processed by one or more processors (102, 202) from baseband signals to RF band signals. For this purpose, one or more transceivers (106, 206) may include an (analog) oscillator and / or filter.

[0135] FIG. 16 illustrates a signal processing circuit for a transmission signal according to an embodiment of the present disclosure. The embodiment of FIG. 16 can be combined with various embodiments of the present disclosure.

[0136] Referring to FIG. 16, the signal processing circuit (1000) may include a scrambler (1010), a modulator (1020), a layer mapper (1030), a precoder (1040), a resource mapper (1050), and a signal generator (1060). Although not limited thereto, the operations / functions of FIG. 16 may be performed in the processor (102, 202) and / or the transceiver (106, 206) of FIG. 15. The hardware elements of FIG. 16 may be implemented in the processor (102, 202) and / or the transceiver (106, 206) of FIG. 15. For example, blocks 1010 to 1060 may be implemented in the processor (102, 202) of FIG. 15. Additionally, blocks 1010 to 1050 may be implemented in the processor (102, 202) of FIG. 15, and block 1060 may be implemented in the transceiver (106, 206) of FIG. 15.

[0137] The codeword can be converted into a wireless signal through the signal processing circuit (1000) of FIG. 16. Here, the codeword is an encoded bit sequence of an information block. The information block can include a transport block (e.g., an UL-SCH transport block, a DL-SCH transport block). The wireless signal can be transmitted through various physical channels (e.g., a PUSCH or a PDSCH).

[0138] Specifically, the codeword can be converted into a bit sequence scrambled by a scrambler (1010). 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 (1020). The modulation method may include pi / 2-BPSK (pi / 2-Binary Phase Shift Keying), m-PSK (m-Phase Shift Keying), m-QAM (m-Quadrature Amplitude Modulation), etc. The complex modulation symbol sequence can be mapped to one or more transmission layers by a layer mapper (1030). The modulation symbols of each transmission layer can be mapped to the corresponding antenna port(s) by a precoder (1040) (precoding). The output z of the precoder (1040) can be obtained by multiplying the output y of the layer mapper (1030) by a precoding matrix W of N*M. Here, N is the number of antenna ports, and M is the number of transmission layers. Here, the precoder (1040) can perform precoding after performing transform precoding (e.g., DFT transform) on complex modulation symbols. In addition, the precoder (1040) can perform precoding without performing transform precoding.

[0139] The resource mapper (1050) 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 (1060) 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 (1060) can include an Inverse Fast Fourier Transform (IFFT) module, a Cyclic Prefix (CP) inserter, a Digital-to-Analog Converter (DAC), a frequency uplink converter, etc.

[0140] The signal processing process for receiving signals in a wireless device can be configured in reverse order of the signal processing process (1010 to 1060) of FIG. 16. For example, a wireless device (e.g., 100, 200 of FIG. 15) 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.

[0141] Figure 17 illustrates a wireless device according to an embodiment of the present disclosure. The wireless device may be implemented in various forms depending on the use case / service (see Figure 14). The embodiment of Figure 17 may be combined with various embodiments of the present disclosure.

[0142] Referring to FIG. 17, the wireless device (100, 200) corresponds to the wireless device (100, 200) of FIG. 15 and may be composed of various elements, components, units / units, and / or modules. For example, the wireless device (100, 200) may include a communication unit (110), a control unit (120), a memory unit (130), and additional elements (140). The communication unit may include a communication circuit (112) and a transceiver(s) (114). For example, the communication circuit (112) may include one or more processors (102, 202) and / or one or more memories (104, 204) of FIG. 15. For example, the transceiver(s) (114) may include one or more transceivers (106, 206) and / or one or more antennas (108, 208) of FIG. 15. The control unit (120) is electrically connected to the communication unit (110), the memory unit (130), and the additional elements (140) and controls the overall operation of the wireless device. For example, the control unit (120) may control the electrical / mechanical operation of the wireless device based on the program / code / command / information stored in the memory unit (130). In addition, the control unit (120) may transmit information stored in the memory unit (130) to an external device (e.g., another communication device) via a wireless / wired interface through the communication unit (110), or store information received from an external device (e.g., another communication device) via a wireless / wired interface in the memory unit (130).

[0143] The additional element (140) may be configured in various ways depending on the type of the wireless device. For example, the additional element (140) may include at least one of a power unit / battery, an input / output unit (I / O unit), a driving unit, and a computing unit. Although not limited thereto, the wireless device may be implemented in the form of a robot (Fig. 14, 100a), a vehicle (Fig. 14, 100b-1, 100b-2), an XR device (Fig. 14, 100c), a portable device (Fig. 14, 100d), a home appliance (Fig. 14, 100e), an IoT device (Fig. 14, 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. 14, 400), a base station (Fig. 14, 200), a network node, etc. Wireless devices may be mobile or stationary depending on the use / service.

[0144] In FIG. 17, various elements, components, units / parts, and / or modules within the wireless device (100, 200) may be interconnected entirely via a wired interface, or at least some may be wirelessly connected via a communication unit (110). For example, within the wireless device (100, 200), the control unit (120) and the communication unit (110) may be wired, and the control unit (120) and the first unit (e.g., 130, 140) may be wirelessly connected via the communication unit (110). In addition, each element, component, unit / part, and / or module within the wireless device (100, 200) may further include one or more elements. For example, the control unit (120) may be composed of one or more processor sets. For example, the control unit (120) may be composed of a set of a communication control processor, an application processor, an electronic control unit (ECU), a graphics processing processor, a memory control processor, etc. As another example, the memory unit (130) may be composed of a random access memory (RAM), a dynamic RAM (DRAM), a read only memory (ROM), a flash memory, a volatile memory, a non-volatile memory, and / or a combination thereof.

[0145] Below, the implementation example of Fig. 17 is described in more detail with reference to the drawings.

[0146] FIG. 18 illustrates a mobile device according to an embodiment of the present disclosure. The mobile device may include a smartphone, a smart pad, a wearable device (e.g., a smartwatch, smartglasses), 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). The embodiment of FIG. 18 may be combined with various embodiments of the present disclosure.

[0147] Referring to FIG. 18, the portable device (100) may include an antenna unit (108), a communication unit (110), a control unit (120), a memory unit (130), a power supply unit (140a), an interface unit (140b), and an input / output unit (140c). The antenna unit (108) may be configured as a part of the communication unit (110). Blocks 110 to 130 / 140a to 140c correspond to blocks 110 to 130 / 140 of FIG. 17, respectively.

[0148] The communication unit (110) can transmit and receive signals (e.g., data, control signals, etc.) with other wireless devices and base stations. The control unit (120) can control components of the mobile device (100) to perform various operations. The control unit (120) can include an AP (Application Processor). The memory unit (130) can store data / parameters / programs / codes / commands required for operating the mobile device (100). In addition, the memory unit (130) can store input / output data / information, etc. The power supply unit (140a) supplies power to the mobile device (100) and can include a wired / wireless charging circuit, a battery, etc. The interface unit (140b) can support connection between the mobile device (100) and other external devices. The interface unit (140b) can include various ports (e.g., audio input / output ports, video input / output ports) for connection with external devices. The input / output unit (140c) can input or output video information / signals, audio information / signals, data, and / or information input from a user. The input / output unit (140c) may include a camera, a microphone, a user input unit, a display unit (140d), a speaker, and / or a haptic module.

[0149] For example, in the case of data communication, the input / output unit (140c) 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 (130). The communication unit (110) converts the information / signals stored in the memory into wireless signals, and can directly transmit the converted wireless signals to other wireless devices or to a base station. In addition, the communication unit (110) 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 (130) and then output in various forms (e.g., text, voice, image, video, haptic) through the input / output unit (140c).

[0150] FIG. 19 illustrates a vehicle or autonomous vehicle according to one embodiment of the present disclosure. The vehicle or autonomous vehicle may be implemented as a mobile robot, a car, a train, a manned or unmanned aerial vehicle (AV), a ship, or the like. The embodiment of FIG. 19 may be combined with various embodiments of the present disclosure.

[0151] Referring to FIG. 19, a vehicle or autonomous vehicle (100) may include an antenna unit (108), a communication unit (110), a control unit (120), a driving unit (140a), a power supply unit (140b), a sensor unit (140c), and an autonomous driving unit (140d). The antenna unit (108) may be configured as a part of the communication unit (110). Blocks 110 / 130 / 140a to 140d correspond to blocks 110 / 130 / 140 of FIG. 17, respectively.

[0152] The communication unit (110) can transmit and receive signals (e.g., data, control signals, etc.) with external devices such as other vehicles, base stations (e.g., base stations, road side units, etc.), and servers. The control unit (120) can control elements of the vehicle or autonomous vehicle (100) to perform various operations. The control unit (120) can include an ECU (Electronic Control Unit). The drive unit (140a) can drive the vehicle or autonomous vehicle (100) on the ground. The drive unit (140a) can include an engine, a motor, a power train, wheels, brakes, a steering device, etc. The power supply unit (140b) supplies power to the vehicle or autonomous vehicle (100) and can include a wired / wireless charging circuit, a battery, etc. The sensor unit (140c) can obtain vehicle status, surrounding environment information, user information, etc. The sensor unit (140c) may include an IMU (inertial measurement unit) sensor, a collision sensor, a wheel sensor, a speed sensor, an incline sensor, a weight detection sensor, a heading sensor, a position module, a vehicle forward / backward sensor, a battery sensor, a fuel sensor, a tire sensor, a steering sensor, a temperature sensor, a humidity sensor, an ultrasonic sensor, an illuminance sensor, a pedal position sensor, etc. The autonomous driving unit (140d) may implement a technology for maintaining a driving lane, a technology for automatically controlling speed such as adaptive cruise control, a technology for automatically driving along a set path, a technology for automatically setting a path and driving when a destination is set, etc.

[0153] For example, the communication unit (110) can receive map data, traffic information data, etc. from an external server. The autonomous driving unit (140d) can generate an autonomous driving route and driving plan based on the acquired data. The control unit (120) can control the drive unit (140a) so that the vehicle or autonomous vehicle (100) moves along the autonomous driving route according to the driving plan (e.g., speed / direction control). During autonomous driving, the communication unit (110) can irregularly / periodically acquire the latest traffic information data from an external server and can acquire surrounding traffic information data from surrounding vehicles. In addition, during autonomous driving, the sensor unit (140c) can acquire vehicle status and surrounding environment information. The autonomous driving unit (140d) can update the autonomous driving route and driving plan based on newly acquired data / information. The communication unit (110) can transmit information regarding the vehicle location, autonomous driving route, driving plan, etc. to the external server. External servers can predict traffic information data in advance using AI technology or other technologies based on information collected from vehicles or autonomous vehicles, and provide the predicted traffic information data to the vehicles or autonomous vehicles.

[0154] The claims set forth in this specification may be combined in various ways. For example, the technical features of the method claims of this specification may be combined and implemented as a device, and the technical features of the device claims of this specification may be combined and implemented as a method. Furthermore, the technical features of the method claims and the technical features of the device claims of this specification may be combined and implemented as a device, and the technical features of the method claims and the technical features of the device claims of this specification may be combined and implemented as a method.

Claims

1. In a method for performing wireless communication by a first device, A step of obtaining information related to motion of the first device based on at least one sensor; A step of transmitting information related to the above motion to an AI (artificial intelligence) model; and A method comprising: a step of performing beam management based on results obtained from the AI ​​model; 2. In paragraph 1, A step of performing a first beam forming; further comprising: A method wherein whether to maintain a beam related to the first beam forming for the above beam management is determined based on a result obtained from the AI ​​model.

3. In paragraph 2, A method in which second beam forming for beam management is performed using a result obtained from the AI ​​model based on the direction of the beam associated with the first beam forming being not aligned with the second device.

4. In paragraph 3, A method in which the direction of the beam associated with the second beam forming is determined based on information related to beam prediction obtained from the AI ​​model.

5. In paragraph 4, A method wherein, based on information related to the beam prediction, the direction of the beam related to the second beam forming is determined to be more aligned than the direction of the beam related to the first beam forming.

6. In paragraph 3, A method in which the thickness of the beam associated with the second beam forming is determined based on information related to beam prediction obtained from the AI ​​model.

7. In paragraph 1, A method in which information related to the motion is transmitted to the AI ​​model, and information related to at least one of the direction, angle, power, covered range, or thickness of the beam for beam management is obtained.

8. In paragraph 1, A method wherein the motion-related information comprises at least one of (i) information related to a three-axis linear motion vector of the first device, (ii) information related to a three-axis rotational motion vector of the first device, or (iii) information related to reliability.

9. In paragraph 1, A method in which information related to the motion is generated based on a value measured by at least one sensor selected from the group consisting of a GNSS (global navigation satellite system) sensor, an IMU (inertial measurement unit) sensor, an acceleration sensor, a gyroscope sensor, a geomagnetic sensor, a gravity sensor, and a rotation vector sensor.

10. In paragraph 1, A method in which the state of a channel related to a beam determined based on beam management is predicted based on the results obtained from the AI ​​model.

11. In paragraph 1, A method in which information related to the motion is transmitted to a telematic module related to the application layer of the first device through an internal network of the first device, based on the AI ​​model being located in the application layer of the first device.

12. In paragraph 1, A method in which information related to the motion is transmitted from the application layer of the first device to the access layer of the first device based on the AI ​​model being located in the access layer of the first device.

13. In paragraph 1, Further comprising a step of transmitting information related to at least one of a predicted path or a planned path of the first device to the AI ​​model; A method wherein information related to beam management is obtained from the AI ​​model based on (i) information related to the motion, and (ii) information related to at least one of the predicted path or the planned path.

14. In a first device configured to perform wireless communication, At least one transmitter / receiver; at least one processor; and At least one memory connected to said at least one processor and storing instructions, said instructions being executed by said at least one processor, wherein said first device causes: Acquire information related to the motion of the first device based on at least one sensor; Transferring information related to the above motion to an AI (artificial intelligence) model; and A first device that performs beam management based on the results obtained from the above AI model.

15. In a processing device set to control the first device, at least one processor; and At least one memory connected to said at least one processor and storing instructions, said instructions being executed by said at least one processor, wherein said first device causes: Acquire information related to the motion of the first device based on at least one sensor; Transferring information related to the above motion to an AI (artificial intelligence) model; and A processing device that performs beam management based on the results obtained from the above AI model.

16. A non-transitory computer-readable storage medium that records commands, The above commands, when executed, cause the first device to: Acquire information related to the motion of the first device based on at least one sensor; Transferring information related to the above motion to an AI (artificial intelligence) model; and A non-transitory computer-readable storage medium that performs beam management based on results obtained from the above AI model.

17. In a method for performing wireless communication by a second device, A step of obtaining information related to motion of the second device based on at least one sensor; a step of transmitting information related to the motion of the second device to the first device; and A step of performing beam management based on a result obtained from an AI (artificial intelligence) model of the first device; including; A method in which a result obtained from the AI ​​model of the first device is obtained based on information related to the motion of the second device being input into the AI ​​model of the first device.

18. In a second device configured to perform wireless communication, At least one transmitter / receiver; at least one processor; and At least one memory connected to said at least one processor and storing instructions, said instructions being executed by said at least one processor, wherein said second device causes: Acquire information related to the motion of the second device based on at least one sensor; Transmitting information related to the motion of the second device to the first device; and Based on the results obtained from the AI ​​(artificial intelligence) model of the first device, beam management is performed. A second device, wherein the result obtained from the AI ​​model of the first device is obtained based on information related to the motion of the second device being input into the AI ​​model of the first device.

19. In a processing device set to control a second device, at least one processor; and At least one memory connected to said at least one processor and storing instructions, said instructions being executed by said at least one processor, wherein said second device causes: Acquire information related to the motion of the second device based on at least one sensor; Transmitting information related to the motion of the second device to the first device; and Based on the results obtained from the AI ​​(artificial intelligence) model of the first device, beam management is performed. A processing device, wherein the result obtained from the AI ​​model of the first device is obtained based on information related to the motion of the second device being input into the AI ​​model of the first device.

20. A non-transitory computer-readable storage medium that records commands, The above commands, when executed, cause the second device to: Acquire information related to the motion of the second device based on at least one sensor; Transmitting information related to the motion of the second device to the first device; and Based on the results obtained from the AI ​​(artificial intelligence) model of the first device, beam management is performed. A non-transitory computer-readable storage medium in which a result obtained from the AI ​​model of the first device is obtained based on information related to the motion of the second device being input into the AI ​​model of the first device.

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