WO2024210597A2 - Method and device for ai-based beam management using motion information - Google Patents

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

The present disclosure relates to a wireless communication system.

5G NR is a new clean-slate type mobile communication system that is the successor technology to LTE (long term evolution) and has the characteristics of high performance, low latency, and high availability. 5G NR can utilize all available spectrum resources, from low frequency bands below 1 GHz, to intermediate frequency bands between 1 GHz and 10 GHz, and high frequency (millimeter wave) bands above 24 GHz.

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 of battery-free IoT (internet of things) 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.

Per device peak data rate 1 Tbps E2E latency 1 ms Maximum spectral efficiency 100bps/Hz Mobility support Up to 1000km/hr Satellite integration Fully AI Fully Autonomous vehicle Fully XR Fully Haptic Communication Fully

In one embodiment, a method of performing wireless communication by a first device is provided. The method may include: obtaining information related to a 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.

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 coupled 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; transfer 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.

In one embodiment, a processing device configured to control a first device is provided. The processing device includes 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.

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 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.

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

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

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.

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.

Figure 5 shows an example of a mobility information format.

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

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

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

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

Figure 10 shows an example of interaction between mobility information and a base station.

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

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

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

FIG. 14 illustrates a communication system (1) according to one embodiment of the present disclosure.

FIG. 15 illustrates a wireless device according to one embodiment of the present disclosure.

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

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

FIG. 18 illustrates a portable device according to one embodiment of the present disclosure.

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

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”.

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".

As used herein, “at least one of A and B” can mean “only A”, “only B” or “both A and B”. Additionally, as used herein, the expressions “at least one of A or B” or “at least one of A and/or B” can be interpreted as the same as “at least one of A and B”.

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".

In addition, the parentheses used in this specification may mean "for example". Specifically, when it is 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". In addition, even when it is indicated as "control information (i.e., PDCCH)", "PDCCH" may be proposed as an example of "control information".

In the explanation below, 'when, if, in case of' can be replaced with 'based on'.

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

In this specification, higher layer parameters may be parameters that are set for the terminal, set in advance, or defined in advance. For example, a base station or a network may transmit higher layer parameters to the terminal. For example, the higher layer parameters may be transmitted through radio resource control (RRC) signaling or medium access control (MAC) signaling.

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 a network. In this specification, "configured or defined" may be interpreted as being preset to a device.

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.

The technology proposed in this specification can be implemented with 6G wireless technology and can be applied to various 6G systems. For example, the 6G system 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.

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.

New network characteristics in 6G could include:

- Satellite integrated network

- Connected intelligence: Unlike previous generations of wireless communication systems, 6G is revolutionary and will update the wireless evolution from “connected things” to “connected intelligence.” AI can be applied at each stage of the communication process (or at each stage of signal processing, as described below).

- Seamless integration of wireless information and energy transfer

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

Some general requirements from the new network characteristics of 6G as mentioned above can be as follows:

- small cell networks

- Ultra-dense heterogeneous network

- High-capacity backhaul

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

- Softwarization and virtualization

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

- Artificial Intelligence: Introducing AI into communications can simplify and improve real-time data transmission. AI can use a lot of analytics to determine how complex target tasks are performed. In other words, AI can increase efficiency and reduce processing delays. Time-consuming tasks such as handover, network selection, and resource scheduling can be performed instantly using AI. AI can also play a significant role in M2M, machine-to-human, and human-to-machine communications. AI can also be a rapid communication in BCI (Brain Computer Interface). 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.

- THz communication: The data rate can be increased by increasing the bandwidth. This can be done by using sub-THz communication with a wide bandwidth and applying advanced massive MIMO technology. THz waves, also known as sub-millimeter waves, generally refer to 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 range (Sub THz band) is considered to be a major part 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. The 300 GHz to 3 THz band is part of the optical band but is at the boundary of the optical band, just behind the RF band. Therefore, this 300 GHz to 3 THz band shows similarities with RF. FIG. 2 illustrates an electromagnetic spectrum according to an 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 indispensable). The narrow beam width generated by the highly directional antenna reduces interference. The small wavelength of THz signals allows a much larger number of antenna elements to be integrated into devices and BSs operating in this band. This enables the use of advanced adaptive array techniques to overcome range limitations.

- Large-scale MIMO technology

- Hologram beamforming (HBF)

- Optical wireless technology

- Free space optical transmission backhaul network (FSO backhaul network)

- Quantum communication

- Cell-free communication

- Integration of wireless information and power transmission

- Integration of wireless communication and sensing

- Integrated access and backhaul network

- Big data analysis

- Reconfigurable intelligent surface

- Metaverse

- Block-chain

- Unmanned aerial vehicles (UAV): UAVs or drones will be a crucial element in 6G wireless communications. In most cases, high-speed data wireless connectivity can be provided using UAV technology. The base station (BS) entity can be installed on the UAV to provide cellular connectivity. UAVs may have certain features not found in fixed BS infrastructure such as easy deployment, robust line-of-sight links, and freedom of movement with controlled mobility. During emergency situations such as natural disasters, deployment of terrestrial communication infrastructure is not economically feasible and sometimes cannot provide services in volatile environments. UAVs can easily handle such situations. UAVs will be a new paradigm in wireless communications. This technology facilitates three basic requirements of wireless networks namely eMBB, URLLC, and mMTC. UAVs can also support several 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.

- Autonomous driving (self-driving): V2X (vehicle to everything), a key element of building autonomous driving infrastructure, can be a technology that allows cars to communicate and share with various elements on the road, such as vehicle to vehicle (V2V) wireless communication and vehicle to infrastructure (V2I) wireless communication. In order to maximize the performance of autonomous driving and ensure high safety, fast transmission speed and low-latency technology are essential. In addition, in the future, autonomous driving may need to go beyond the level of delivering warnings or guidance messages to drivers and actively intervene in vehicle operation and directly control 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.

- Non-terrestrial networks (NTN): NTN may represent a network or network segment that uses RF (radio frequency) resources mounted on a satellite (or unmanned aerial system (UAS) platform). FIG. 3 illustrates an example of a typical scenario of an NTN based on a transparent payload, according to an embodiment of the present disclosure. FIG. 4 illustrates an example of a typical scenario of an NTN based on a regenerative payload, according to an embodiment of the present disclosure. The embodiment 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 foot print may mean an area where a signal transmitted by a satellite can be received. Referring to FIG. 4, a satellite (or UAS platform) can create a service link with a UE. A satellite (or UAS platform) associated with a UE can be associated with another satellite (or UAS platform) via inter-satellite links (ISLs). The other satellite (or UAS platform) can be associated with a gateway via a feeder link. A satellite can be associated with a data network via another satellite and a gateway based on a regenerative payload. If there is no ISL between a satellite and another satellite, a feeder link between the satellite and the gateway may be required. FIGS. 3 and 4 are only examples of NTN scenarios, and NTN can be implemented based on various scenarios. For example, a satellite (or UAS platform) can implement a transparent or regenerative (with on board processing) payload. For example, a satellite (or UAS platform) may generate multiple beams over a given 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. Thus, 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, coding/modulation. For example, a regenerative payload may be substantially identical to onboarding all or part of a base station function onto the satellite (or UAS platform).

- Integrated sensing and communication (ISAC): Wireless sensing is a technology that uses radio frequencies to detect the instantaneous linear velocity, angle, distance (range), etc. of an object to obtain information about the environment and/or the characteristics of objects in the environment. Since the radio frequency sensing function does not require a connection to the object through a device in the 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 provide a wide range of new functions 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 various industries (e.g., unmanned aerial vehicles, smart homes, V2X, factories, railways, public safety, etc.) to enable applications that provide, for example, intruder detection, assisted vehicle steering and navigation, trajectory tracking, collision avoidance, traffic management, health and traffic management, etc. In some cases, wireless sensing can use non-3GPP type sensors (e.g., radar, camera) to additionally support 3GPP-based sensing. For example, the operation of a wireless sensing service, i.e., sensing operation, may depend on the transmission, reflection, and scattering of wireless sensing signals. Therefore, wireless sensing may provide an opportunity to enhance existing communication systems from communication networks to wireless communication and sensing networks.

Meanwhile, the recent ITS (Intelligent Transport System) has expanded from traditional transportation (e.g., trains, automobiles, etc.) to cover expanded mobility such as drones or mobile robots. And, connectivity between mobilities or infrastructures is a very important topic in ITS, and much development is being done to improve the communication performance of connected mobility.

For example, by mixing the internal communication system and the external communication system of mobility and exchanging data or information based on this, safety or traffic efficiency can be improved from the ITS perspective. For example, in the case of a car, the internal communication system of the vehicle itself can be in charge of communication between sensors that collect information on the internal operating status of the vehicle, the movement of the body, and the surrounding environment, and the electronic control system, and the external communication system of the vehicle can be in charge of communication between the communication infrastructure and other mobility.

For example, the internal communication system of mobility has various systems developed by manufacturers and parts suppliers. Among them, representative communication systems include MOST (Media Oriented System Transport), LIN (Local Interconnected Network), and CAN (Controller Area Network), and CAN FD (Flexible Data-rate) and FlexRay, which have improved performance.

For example, as an external communication system of mobility, there are direct communication (e.g., Dedicated Short-Range Communication (DSRC), 3GPP PC5) and mobile communication (e.g., LTE or NR), and standard organizations such as IEEE and 3GPP are developing new technologies to improve communication performance. Recently, there has been active discussion on a method to improve communication performance based on an AI (Artificial Intelligence) model. That is, the goal is to enhance CSI (Channel State Information) feedback, beam management, or positioning accuracy by using a learned AI model.

Meanwhile, research is currently being conducted on learning AI models with limited information within the communication module or information exchanged between base stations (e.g., past CSI or location information). However, due to the characteristics of mobility, various movements are possible in 3D space, and there may be difficulties in CSI management, beam management, and precision positioning due to the uncertainty of the mobility and/or movement of the body. For example, in the case of a car, rolling may occur when the vehicle passes over an irregular road surface. Or, for example, pitching may occur due to sudden stops or sudden starts. Or, for example, in the case of a drone, turbulence may occur during flight, making smooth flight impossible. For example, in the case described above, CSI and beam management may not be smooth due to unpredictable movements of the body in the communication module mounted on the body. Alternatively, for example, a sudden rotation of the fuselage may cause the performance of the communication module and antenna performing beam forming to deteriorate.

In the present disclosure, a method for extracting movement information of a mobility and transmitting it to a communication module 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 a device supporting the same are proposed as follows.

In addition, the present disclosure proposes 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 when information is transmitted, or the format of information transmitted from an application layer to an access layer within the communication terminal, as follows.

For example, among the many pieces of information exchanged in the internal network of the mobility, data that can express mobility information of the mobility that can be used in the AI model of the communication module can be extracted. In this case, for example, the mobility information of the 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 path and/or planned path of the mobility.

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., GNSS sensors, inertial measurement units (IMUs), acceleration sensors, gyroscope sensors, geomagnetic sensors, gravity sensors, or rotation vector sensors, etc.). In addition, for example, the mobility may generate information by processing measurements obtained from sensors, and the processed information may be input into the AI model as described below. For example, the mobility may perform calculations based on measurements obtained from the GNSS sensors and measurements obtained from the IMU sensors to perform dead reckoning, and may use the processed information obtained based on this as an input parameter of the AI model as described below.

For example, the expected path or planned path of the second mobility may mean predicted path information calculated based on the current location, speed, direction, input values of the driver or operator, etc., for the future movement path of the mobility, or path information planned in advance 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 expected path or planned path at this time, this information can be used instead. For example, in the SAE (Society of Automotive Engineers) standard of V2X, if the mobility transmits a BSM (Basic Safety Message), the DE_PathPrediction value configured in the message can be utilized.

Fig. 5 shows an example of a mobility information format. For example, the two types of mobility information described above can be exchanged in a format like Fig. 5.

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 progressing direction of the mobility, may be used as the cardinal direction of the x, y, and z directions. 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 located at the center of mass of the Earth, the x-axis may be located in the direction of the intersection of the Greenwich meridian and the equator, the y-axis may be located in the direction of 90 degrees east longitude, and the z-axis may be located 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 [%].

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.

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

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

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.

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

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

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

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

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 after the mobility mobility information is calculated, it can be transmitted to the access layer within the communication module.

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

FIG. 10 illustrates an example of interaction between mobility and base station for mobility information. The embodiment of FIG. 10 can be combined with various embodiments of the present disclosure.

Referring to FIG. 10, a mobility can report to a base station a situation in which mobility information of a mobility is provided from an internal network to a communication module, and the base station can request or instruct a method and/or condition for transmitting the mobility information of the mobility being exchanged to the base station. And, for example, the mobility can transmit its own mobility information to the base station according to a request or instruction of the base station.

For example, mobility information of mobility transmitted to the communication module can be utilized as an input parameter of an AI model to perform external communication with improved communication performance (e.g., beam and/or CSI (channel state information) prediction). In addition, for example, when there is interaction between base stations (e.g., report, request, response, etc.), mobility information of mobility can be utilized as an input parameter of an AI model within the base station.

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.

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 a vehicle, a drone, or a mobile robot. 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 corresponding beam (1110). Meanwhile, based on the state of UE A or the driving/flight environment around UE A, a motion change of UE A, such as rolling or pitching, that UE A does not expect 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 not only measurements obtained from sensors installed inside UE A, but also processed information obtained by performing a calculation based on one or more measurements. For example, UE A may use the information related to the motion of UE A as an input parameter of an AI model. For example, 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 a calculation based on measurement values obtained from a GNSS sensor installed inside UE A and measurement values obtained from an IMU sensor to perform dead reckoning, and may use 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 may transmit data, etc. to UE B based on the corresponding 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 that the communication status (or quality) is improved. That is, for example, UE A may perform beam management to maintain beam-based communication with UE B based on the result obtained by inputting information related to the motion of UE A into the AI model.

According to various embodiments of the present disclosure, the mobility information of the transmitted mobility can be utilized as an input of an AI (Artificial Intelligence) model installed inside a communication module, which can improve communication performance (e.g., CSI (channel state information) prediction, beam prediction or positioning accuracy). Specifically, even if an unexpected motion occurs in the mobility, the mobility can determine the direction or thickness of the beam from the information obtained by inputting the motion information into the AI model, thereby allowing the mobility to perform operations such as beam measurement or beam sweeping, which must be performed to maintain beam-based communication, to a minimum. That is, for example, resources related to a 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.

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 can be combined with various embodiments of the present disclosure.

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

Additionally, for example, the first device can perform the first beam forming. For example, whether to maintain the beam related to the first beam forming for the beam management can be determined based on the result obtained from the AI model. For example, based on the fact that the direction of the beam related to the first beam forming is not aligned with the second device, the 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 related to 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 related to the second beam forming can be determined to be a more aligned direction than the direction of the beam related to the first beam forming. For example, the thickness of the beam related to 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 a 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.

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.

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.

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

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.

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.

Additionally, for example, the first device can 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 can 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.

The above 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 result obtained from the AI model.

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 coupled to the at least one processor and storing instructions. For example, the instructions may cause the first device to: obtain information related to a motion of the first device based on at least one sensor; transfer 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.

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 coupled to the at least one processor and storing instructions. For example, the instructions may cause the first device, based on execution by the at least one processor, 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.

According to one embodiment of the present disclosure, a non-transitory computer-readable storage medium having commands recorded thereon may be provided. For example, the commands, when executed, may cause a 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.

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 can be combined with various embodiments of the present disclosure.

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 to the AI model of the first device.

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 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 to the AI model of the first device.

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 may cause the second device to: obtain information related to a 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 to the AI model of the first device.

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 coupled to the at least one processor and storing instructions. For example, the instructions may cause the second device, based on execution by the at least one processor, to: obtain information related to a motion of the second device based on at least one sensor; transmit information related to a 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 a motion of the second device being input to the AI model of the first device.

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 a 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.

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

Below, devices to which various embodiments of the present disclosure can be applied are described.

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

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

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.

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 means 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 Thing) 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) installed in a vehicle, a television, a smartphone, a computer, a wearable device, a home appliance, digital signage, a vehicle, a robot, etc. The portable device 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 wireless devices, and a specific wireless device (200a) may act as a base station/network node to other wireless devices.

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, the 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 (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, the ZigBee technology can create PAN (personal area networks) related to small/low-power digital communication based on various standards such as IEEE 802.15.4, and can be called by various names.

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). Also, IoT devices (e.g., sensors) can communicate directly with other IoT devices (e.g., sensors) or other wireless devices (100a to 100f).

Wireless communication/connection (150a, 150b, 150c) can be established between wireless devices (100a to 100f)/base stations (200), and base stations (200)/base stations (200). Here, the 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 the wireless communication/connection (150a, 150b, 150c), a wireless device and a base station/wireless device, and a base station and a base station 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 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.

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

Referring to FIG. 15, the first wireless device (100) and the second wireless device (200) can transmit and receive wireless signals through 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.

A first wireless device (100) includes one or more processors (102) and one or more memories (104), and may additionally include one or more transceivers (106) and/or one or more antennas (108). The processor (102) controls the memory (104) and/or the transceiver (106), and may be configured to implement the descriptions, functions, procedures, suggestions, 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). Additionally, 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 codes including instructions for performing the descriptions, functions, procedures, proposals, methods, and/or operational 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 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.

The second wireless device (200) includes one or more processors (202), one or more memories (204), and may additionally include one or more transceivers (206) and/or one or more antennas (208). The processor (202) may be configured to control the memories (204) and/or the transceivers (206), and implement the descriptions, functions, procedures, suggestions, 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). Additionally, 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 codes including instructions for performing the descriptions, functions, procedures, proposals, methods, and/or operational flowcharts disclosed in the present 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.

Hereinafter, hardware elements of the wireless device (100, 200) will be described in more detail. Although not limited thereto, one or more protocol layers may be implemented by one or more processors (102, 202). For example, one or more processors (102, 202) may implement one or more layers (e.g., functional layers such as 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 operational 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 operational flowcharts disclosed in this document. One or more processors (102, 202) can generate signals (e.g., baseband signals) comprising PDUs, SDUs, messages, control information, data or information according to the functions, procedures, suggestions and/or methodologies 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, suggestions, methods and/or operational flowcharts disclosed herein.

The one or more processors (102, 202) may be referred to as a controller, a microcontroller, a microprocessor, or a microcomputer. The 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 the one or more processors (102, 202). The descriptions, functions, procedures, suggestions, 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 operational flowcharts disclosed in this document may be implemented using firmware or software configured to perform one or more of the following: included in one or more processors (102, 202), or stored in one or more memories (104, 204) and driven by one or more of the processors (102, 202). The descriptions, functions, procedures, suggestions, methods and/or operational flowcharts disclosed in this document may be implemented using firmware or software in the form of codes, instructions and/or sets of instructions.

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 comprised of 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.

One or more transceivers (106, 206) can transmit user data, control information, wireless signals/channels, etc., as described 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 described in the descriptions, functions, procedures, suggestions, methods and/or flowcharts of this document, from one or more other devices. For example, one or more transceivers (106, 206) can be coupled 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, and 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) may 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 processed user data, control information, wireless signals/channels, etc. from baseband signals to RF band signals using one or more processors (102, 202). For this purpose, one or more transceivers (106, 206) may include an (analog) oscillator and/or filter.

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.

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.

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., UL-SCH transport block, DL-SCH transport block). The wireless signal can be transmitted through various physical channels (e.g., PUSCH, PDSCH).

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 scheme 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. Additionally, the precoder (1040) can perform precoding without performing transform precoding.

The resource mapper (1050) can map modulation symbols of each antenna port to time-frequency resources. The time-frequency resources can include a plurality of symbols (e.g., CP-OFDMA symbols, DFT-s-OFDMA symbols) in the time domain and a plurality of 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.

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 a wireless signal from the outside through an antenna port/transceiver. The received wireless signal can be converted into a baseband signal through a signal restorer. To this end, the signal restorer 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 de-mapper process, a postcoding process, a demodulation process, and a de-scramble process. The codeword can be restored to an original information block through decoding. Accordingly, a signal processing circuit (not shown) for a received signal may include a signal restorer, a resource de-mapper, a postcoder, a demodulator, a de-scrambler and a decoder.

FIG. 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 FIG. 14). The embodiment of FIG. 17 may be combined with various embodiments of the present disclosure.

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 an additional element (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 overall operations of the wireless device. For example, the control unit (120) may control electrical/mechanical operations of the wireless device based on programs/codes/commands/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).

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.

In FIG. 17, various elements, components, units/parts, and/or modules within the wireless device (100, 200) may be entirely interconnected 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 ECU (Electronic Control Unit), a graphics processing processor, a memory control processor, etc. As another example, the memory unit (130) may be composed of a RAM (Random Access Memory), a DRAM (Dynamic RAM), a ROM (Read Only Memory), a flash memory, a volatile memory, a non-volatile memory, and/or a combination thereof.

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

FIG. 18 illustrates a portable device according to an embodiment of the present disclosure. The portable device may include a smart phone, a smart pad, a wearable device (e.g., a smart watch, a smart glass), a portable computer (e.g., a laptop, etc.). The portable device may be referred to as a Mobile Station (MS), a User Terminal (UT), a Mobile Subscriber Station (MSS), a Subscriber Station (SS), an Advanced Mobile Station (AMS), or a Wireless Terminal (WT). The embodiment of FIG. 18 may be combined with various embodiments of the present disclosure.

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.

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 portable 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 portable device (100). In addition, the memory unit (130) can store input/output data/information, etc. The power supply unit (140a) supplies power to the portable device (100) and can include a wired/wireless charging circuit, a battery, etc. The interface unit (140b) can support connection between the portable 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 image information/signals, audio information/signals, data, and/or information input from a user. The input/output unit (140c) can include a camera, a microphone, a user input unit, a display unit (140d), a speaker, and/or a haptic module.

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).

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

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.

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.), servers, etc. 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, a light 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.

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 a driving plan based on the acquired data. The control unit (120) can control the driving 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, the sensor unit (140c) can acquire vehicle status and surrounding environment information during autonomous driving. The autonomous driving unit (140d) can update the autonomous driving route and driving plan based on the newly acquired data/information. The communication unit (110) can transmit information on the vehicle location, autonomous driving route, driving plan, etc. to an external server. External servers can predict traffic information data in advance using AI technology, etc. based on information collected from vehicles or autonomous vehicles, and provide the predicted traffic information data to vehicles or autonomous vehicles.

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. In addition, the technical features of the method claims of this specification 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 of this specification and the technical features of the device claims of this specification may be combined and implemented as a method.

Claims (20)

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 above AI model. 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. In the second paragraph, A method wherein 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. In the third paragraph, A method wherein 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. 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. In the third paragraph, A method wherein 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. In the first paragraph, 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. In the first paragraph, A method according to claim 1, wherein the information related to the motion 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. 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. In the first paragraph, 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. 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. In the first paragraph, A method wherein information related to the motion is transmitted from an 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. In the first paragraph, A step of transmitting information related to at least one of the predicted path or the planned path of the first device to the AI model; further comprising: A method wherein information related to the 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. In a first device configured to perform wireless communication, At least one transceiver; at least one processor; and At least one memory coupled to said at least one processor and storing instructions, said instructions being executed by said at least one processor to cause said first device to: Acquire information related to 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. In a processing device set to control a first device, at least one processor; and At least one memory coupled to said at least one processor and storing instructions, said instructions being executed by said at least one processor to cause said first device to: Acquire information related to 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. A non-transitory computer-readable storage medium that records commands, The above commands, when executed, cause the first device to: Acquire information related to 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. 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 wherein 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. In a second device configured to perform wireless communication, At least one transceiver; at least one processor; and At least one memory coupled to said at least one processor and storing instructions, said instructions causing said second device to: Acquire information related to 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. In a processing device set to control a second device, at least one processor; and At least one memory coupled to said at least one processor and storing instructions, said instructions causing said second device to: Acquire information related to 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 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. A non-transitory computer-readable storage medium that records commands, The above commands, when executed, cause the second device to: Acquire information related to 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, wherein 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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