Apparatus and method for beamforming in wireless communication system

Abstract

The present disclosure is for beamforming in a wireless communication system. This method performed by an apparatus comprises the steps of: transmitting, in different directions, beams including a synchronization signal; performing synchronization on the basis of the synchronization signal; receiving channel state information; acquiring initial information on the basis of the beams; determining a metric for beamforming on the basis of the initial information; finding a solution for a beamforming problem on the basis of the metric by using at least one algorithm; and forming a beam on the basis of the solution and the channel state information.

Description

Device and method for beam forming in a wireless communication system

[0001] The present disclosure relates to a wireless communication system, and to a device and method for beam forming in a wireless communication system.

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

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

[0004] The present disclosure relates to a device and method for performing communication and sensing in a wireless communication system.

[0005] The present disclosure relates to a device and method for forming a beam capable of performing communication and sensing simultaneously in a wireless communication system.

[0006] The present disclosure relates to a device and method for forming a beam suitable for an environment in a wireless communication system.

[0007] The present disclosure relates to a device and method for determining a metric for forming a beam suitable for an environment in a wireless communication system.

[0008] The present disclosure relates to a device and method for forming a beam based on a determined metric in a wireless communication system.

[0009] The present disclosure relates to a device and method for forming a beam using an algorithm based on a determined metric in a wireless communication system.

[0010] The present disclosure relates to a device and method for forming a beam using the same algorithm in a wireless communication system.

[0011] The present disclosure relates to a device and method for forming an optimized beam using an algorithm in a wireless communication system.

[0012] The present disclosure relates to a device and method for forming a suitable beam according to the number of clutters in a wireless communication system.

[0013] The present disclosure relates to a device and method for forming a suitable beam according to the number of targets in a wireless communication system.

[0014] The technical objectives to be achieved in the present disclosure are not limited to those mentioned above, and other technical tasks not mentioned can be considered by a person having ordinary skill in the technical field to which the technical configuration of the present disclosure is applied from the embodiments of the present disclosure described below.

[0015] As an example of the present disclosure, a method performed by a user equipment (UE) in a wireless communication system may include the steps of: transmitting beams including a synchronization signal in different directions; performing synchronization based on the synchronization signal; receiving channel state information; acquiring initial information based on the beam; determining a metric for beam forming based on the initial information; finding a solution to a beam forming problem based on the metric using at least one algorithm; and forming a beam based on the solution and the channel state information.

[0016] As an example of the present disclosure, a method performed by a base station in a wireless communication system may include the steps of: transmitting beams including a synchronization signal in different directions; performing synchronization based on the synchronization signal; receiving channel state information; acquiring initial information based on the beam; determining a metric for beam forming based on the initial information; finding a solution to a beam forming problem based on the metric using at least one algorithm; and forming a beam based on the solution and the channel state information.

[0017] As an example of the present disclosure, in a wireless communication system, a user equipment (UE) includes a transceiver; and a processor connected to the transceiver, wherein the processor is configured to transmit beams including a synchronization signal in different directions, perform synchronization based on the synchronization signal, receive channel state information, acquire initial information based on the beam, determine a metric for beam forming based on the initial information, obtain a solution to a beam forming problem based on the metric using at least one algorithm, and form a beam based on the solution and the channel state information.

[0018] As an example of the present disclosure, in a wireless communication system, a base station includes a transceiver; and a processor connected to the transceiver, wherein the processor is configured to transmit beams including a synchronization signal in different directions, perform synchronization based on the synchronization signal, receive channel state information, acquire initial information based on the beam, determine a metric for beam forming based on the initial information, obtain a solution to a beam forming problem based on the metric using at least one algorithm, and form a beam based on the solution and the channel state information.

[0019] As an example of the present disclosure, a communication device includes at least one processor; and at least one computer memory connected to the at least one processor and storing instructions that, when executed by the at least one processor, direct operations, the operations including: transmitting beams including a synchronization signal in different directions, performing synchronization based on the synchronization signal, receiving channel state information, acquiring initial information based on the beam, determining a metric for beamforming based on the initial information, finding a solution to a beamforming problem based on the metric using at least one algorithm, and forming a beam based on the solution and the channel state information.

[0020] As an example of the present disclosure, a non-transitory computer-readable medium storing at least one instruction includes at least one instruction executable by a processor, wherein the at least one instruction instructs a device to transmit beams including a synchronization signal in different directions, perform synchronization based on the synchronization signal, receive channel state information, acquire initial information based on the beam, determine a metric for beamforming based on the initial information, obtain a solution to a beamforming problem based on the metric using at least one algorithm, and form a beam based on the solution and the channel state information.

[0021] The above-described aspects of the present disclosure are only some of the preferred embodiments of the present disclosure, and various embodiments reflecting the technical features of the present disclosure can be derived and understood by a person having ordinary skill in the art based on the detailed description of the present disclosure to be described below.

[0022] The following effects may be achieved by embodiments based on the present disclosure.

[0023] According to the present disclosure, communication and sensing can be performed simultaneously.

[0024] The effects that can be obtained from the embodiments of the present disclosure are not limited to the effects mentioned above, and other effects not mentioned can be clearly derived and understood by those skilled in the art to which the technical configuration of the present disclosure is applied, from the description of the embodiments of the present disclosure below. In other words, unintended effects resulting from implementing the configuration described in the present disclosure can also be derived from the embodiments of the present disclosure by those skilled in the art.

[0025] The accompanying drawings are intended to aid understanding of the present disclosure and, together with detailed descriptions, may provide embodiments of the present disclosure. However, the technical features of the present disclosure are not limited to specific drawings, and the features disclosed in each drawing may be combined with each other to form new embodiments. Reference numerals in each drawing may indicate structural elements.

[0026] Figure 1 illustrates an example of a communication system applicable to the present disclosure.

[0027] FIG. 2 illustrates an example of a wireless device applicable to the present disclosure.

[0028] FIG. 3 illustrates a method for processing a transmission signal applicable to the present disclosure.

[0029] Figure 4 illustrates a communication procedure between a terminal and a base station applicable to the present disclosure.

[0030] FIG. 5 illustrates an example of a communication structure that can be provided in a 6G (6th generation) system applicable to the present disclosure.

[0031] Figure 6 illustrates an electromagnetic spectrum applicable to the present disclosure.

[0032] Figure 7 illustrates a THz communication method applicable to the present disclosure.

[0033] Figure 8 illustrates a THz signal generation method applicable to the present disclosure.

[0034] FIG. 9 illustrates a wireless communication transceiver applicable to the present disclosure.

[0035] Figure 10 illustrates a transmitter structure applicable to the present disclosure.

[0036] Figure 11 illustrates a system information transmission procedure applicable to the present disclosure.

[0037] Figure 12 illustrates a beam management procedure applicable to the present disclosure.

[0038] FIG. 13 illustrates an example of a joint communications and sensing (JCAS) beamforming protocol according to one embodiment of the present disclosure.

[0039] FIG. 14 illustrates an example of a procedure for performing communication and sensing according to one embodiment of the present disclosure.

[0040] FIG. 15 illustrates an example of a procedure for forming a beam according to one embodiment of the present disclosure.

[0041] FIG. 16 illustrates an example of a procedure for calculating a beamforming matrix based on a first algorithm according to one embodiment of the present disclosure.

[0042] FIG. 17 illustrates an example of a procedure for calculating a beamforming matrix based on a second algorithm according to one embodiment of the present disclosure.

[0043] FIG. 18 illustrates an example of a procedure for performing communication and sensing using JCAS according to one embodiment of the present disclosure.

[0044] Figure 19a shows the JCAS beam forming result when there is one target.

[0045] Figure 19b shows the JCAS beam forming results when there are three targets.

[0046] Figure 20a shows the JCAS beamforming result when there is one target and the echo signal of the clutter has a similar intensity to the echo signal of the target.

[0047] Figure 20b shows the JCAS beamforming results when there are three targets and the echo signal of the clutter has a similar intensity to the echo signal of the targets.

[0048] FIG. 21 illustrates an example of a wireless device applicable to the present disclosure.

[0049] Figure 22 illustrates an example of a portable device applicable to the present disclosure.

[0050] FIG. 23 illustrates an example of a vehicle or autonomous vehicle applicable to the present disclosure.

[0051] Figure 24 illustrates an example of a vehicle applicable to the present disclosure.

[0052] FIG. 25 illustrates an example of an XR device applicable to the present disclosure.

[0053] Figure 26 illustrates an example of a robot applicable to the present disclosure.

[0054] Figure 27 illustrates an example of an AI device applicable to the present disclosure.

[0055] The following embodiments combine the components and features of the present disclosure in a predetermined form. Each component or feature may be considered optional unless explicitly stated otherwise. Each component or feature may be implemented without being combined with other components or features. Furthermore, some components and / or features may be combined to form embodiments of the present disclosure. The order of operations described in the embodiments of the present disclosure may be changed. Some components or features of one embodiment may be included in another embodiment or may be replaced with corresponding components or features of another embodiment.

[0056] In the description of the drawings, procedures or steps that may obscure the gist of the present disclosure are not described, and procedures or steps that can be understood by a person skilled in the art are also not described.

[0057] Throughout the specification, when a part is said to "comprising" or "including" a component, this does not mean that other components may be included, but rather that other components may be excluded, unless otherwise specifically stated. In addition, terms such as "...part," "...unit," and "module" described in the specification mean a unit that processes at least one function or operation, which may be implemented by hardware, software, or a combination of hardware and software. In addition, the words "a" or "an," "one," "the," and similar related words may be used in the context of describing the present disclosure (especially in the context of the claims below) to include both the singular and the plural, unless otherwise indicated herein or clearly contradicted by context.

[0058] Embodiments of the present disclosure described herein focus on the data transmission and reception relationship between a base station and a mobile station. Here, the base station is understood as a terminal node of a network that directly communicates with the mobile station. Certain operations described herein as being performed by the base station may, in some cases, be performed by an upper node of the base station.

[0059] That is, in a network consisting of multiple network nodes including a base station, various operations performed for communication with a mobile station may be performed by the base station or other network nodes other than the base station. In this case, the term 'base station' may be replaced by terms such as fixed station, Node B, eNB (eNode B), gNB (gNode B), ng-eNB, advanced base station (ABS), or access point.

[0060] Additionally, in the embodiments of the present disclosure, the term terminal may be replaced with terms such as user equipment (UE), mobile station (MS), subscriber station (SS), mobile subscriber station (MSS), mobile terminal, or advanced mobile station (AMS).

[0061] Additionally, a transmitter refers to a fixed and / or mobile node that provides data or voice services, and a receiver refers to a fixed and / or mobile node that receives data or voice services. Therefore, for uplink, a mobile station can be the transmitter, and a base station can be the receiver. Similarly, for downlink, a mobile station can be the receiver, and a base station can be the transmitter.

[0062] Embodiments of the present disclosure may be supported by standard documents disclosed in at least one of wireless access systems, such as IEEE 802.xx system, 3rd Generation Partnership Project (3GPP) system, 3GPP Long Term Evolution (LTE) system, 3GPP 5th generation (5G) NR (New Radio) system and 3GPP2 system, and in particular, embodiments of the present disclosure may be supported by 3GPP TS (technical specification) 38.211, 3GPP TS 38.212, 3GPP TS 38.213, 3GPP TS 38.321 and 3GPP TS 38.331 documents.

[0063] Furthermore, the embodiments of the present disclosure can be applied to other wireless access systems and are not limited to the systems described above. For example, they can be applied to systems implemented after the 3GPP 5G NR system and are not limited to a specific system.

[0064] That is, obvious steps or parts not described in the embodiments of the present disclosure can be explained by referring to the above documents. In addition, all terms disclosed in this document can be explained by the above standard documents.

[0065] Hereinafter, preferred embodiments according to the present disclosure will be described in detail with reference to the accompanying drawings. The detailed description set forth below, together with the accompanying drawings, is intended to illustrate exemplary embodiments of the present disclosure and is not intended to represent the only embodiments in which the technical configurations of the present disclosure may be implemented.

[0066] Additionally, specific terms used in the embodiments of the present disclosure are provided to aid in understanding of the present disclosure, and the use of such specific terms may be changed to other forms without departing from the technical spirit of the present disclosure.

[0067] The following technology can be applied to various wireless access systems such as CDMA (code division multiple access), FDMA (frequency division multiple access), TDMA (time division multiple access), OFDMA (orthogonal frequency division multiple access), and SC-FDMA (single carrier frequency division multiple access).

[0068]

[0069] For clarity, the following description is based on 3GPP communication systems (e.g., LTE, NR, etc.), but the technical spirit of the present disclosure is not limited thereto. LTE may refer to technology after 3GPP TS 36.xxx Release 8. Specifically, LTE technology after 3GPP TS 36.xxx Release 10 may be referred to as LTE-A, and LTE technology after 3GPP TS 36.xxx Release 13 may be referred to as LTE-A pro. 3GPP NR may refer to technology after TS 38.xxx Release 15. 3GPP 6G may refer to technology after TS Release 17 and / or Release 18. "xxx" refers to a standard document detail number. LTE / NR / 6G may be collectively referred to as a 3GPP system.

[0070] For background information, terms, abbreviations, etc. used in this disclosure, reference may be made to standard documents published prior to this disclosure. For example, reference may be made to standard documents 36.xxx and 38.xxx.

[0071]

[0072] Communication system applicable to the present disclosure

[0073] Although not limited thereto, the various descriptions, functions, procedures, proposals, methods and / or operational flowcharts of the present disclosure disclosed in this document may be applied to various fields requiring wireless communication / connectivity (e.g., 5G) between devices.

[0074] 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.

[0075] Figure 1 illustrates an example of a communication system applied to the present disclosure.

[0076] Referring to FIG. 1, a communication system (100) applied to the present disclosure 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., LTE, LTE-A, LTE-A pro, NR, 5G, 5G-A, 6G) and may be referred to as a communication / wireless / 5G device. Although not limited thereto, the wireless device may include a robot (100a), a vehicle (100b-1, 100b-2), an XR (extended reality) device (100c), a hand-held device (100d), a home appliance (100e), an IoT (Internet of Things) device (100f), and an AI (artificial intelligence) device / server (100g). For example, the vehicle may include a vehicle equipped with a wireless communication function, an autonomous vehicle, a vehicle capable of performing vehicle-to-vehicle communication, etc. Here, the vehicles (100b-1, 100b-2) may include unmanned aerial vehicles (UAVs) (e.g., drones). The XR devices (100c) include augmented reality (AR) / virtual reality (VR) / mixed reality (MR) devices, and may be implemented in the form of head-mounted devices (HMDs), head-up displays (HUDs) installed in vehicles, televisions, smartphones, computers, wearable devices, home appliances, digital signage, vehicles, robots, etc. The portable devices (100d) may include smartphones, smart pads, wearable devices (e.g., smartwatches, smart glasses), computers (e.g., laptops, etc.), etc. The home appliances (100e) may include TVs, refrigerators, washing machines, etc. The IoT devices (100f) may include sensors, smart meters, etc.For example, the base station (120) and the network (130) may also be implemented as wireless devices, and a specific wireless device (120a) may act as a base station / network node to other wireless devices.

[0077] Wireless devices (100a to 100f) can be connected to a network (130) via a base station (120). AI technology can be applied to the wireless devices (100a to 100f), and the wireless devices (100a to 100f) can be connected to an AI server (100g) via a network (130). The network (130) can be configured using a 3G network, a 4G (e.g., LTE) network, a 5G (e.g., NR), or a 6G network. The wireless devices (100a to 100f) can communicate with each other via the base station (120) / network (130), but can also communicate directly (e.g., sidelink communication) without going through the base station (120) / network (130). For example, vehicles (100b-1, 100b-2) can communicate directly (e.g., V2V (vehicle to vehicle) / V2X (vehicle to everything) communication). Additionally, an IoT device (100f) (e.g., a sensor) can communicate directly with another IoT device (e.g., a sensor) or another wireless device (100a to 100f).

[0078] Wireless communication / connection (150a, 150b, 150c) can be established between wireless devices (100a to 100f) / base stations (120), and base stations (120) / base stations (120). Here, the wireless communication / connection can be established through various wireless access technologies such as uplink / downlink communication (150a), sidelink communication (150b) (or D2D communication), and base station-to-base station communication (150c) (e.g., relay, IAB (integrated access backhaul)). Through the wireless communication / connection (150a, 150b, 150c), the wireless device and base station / wireless device, and base stations and base stations can transmit / receive wireless signals to / from each other. For example, the wireless communication / connection (150a, 150b, 150c) can transmit / receive signals through various physical channels. To this end, based on various proposals of the present disclosure, 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.), resource allocation processes, etc. may be performed.

[0079]

[0080] Devices applicable to the present disclosure

[0081] FIG. 2 illustrates an example of a wireless device applicable to the present disclosure.

[0082] Referring to FIG. 2, the wireless device (200) can transmit and receive wireless signals via various wireless access technologies (e.g., LTE, LTE-A, LTE-A pro, NR, 5G, 5G-A, 6G). The wireless device (200) includes at least one processor (202) and at least one memory (204), and may additionally include at least one transceiver (206) and / or at least one antenna (208).

[0083] The processor (202) controls the memory (204) and / or the transceiver (206), and may be configured to implement the descriptions, functions, procedures, proposals, methods, and / or operational flowcharts disclosed in this document. For example, the processor (202) may process information in the memory (204) to generate first information / signal, and then transmit a wireless signal including the first information / signal via the transceiver (206). In addition, the processor (202) may receive a wireless signal including second information / signal via the transceiver (206), and then store information obtained from signal processing of the second information / signal in the memory (204). The memory (204) may be connected to the processor (202) and may store various information related to the operation of the processor (202). For example, the memory (204) may store software code including instructions for performing some or all of the processes controlled by the processor (202), or for performing the descriptions, functions, procedures, proposals, methods, and / or operational flowcharts disclosed herein. Here, the processor (202) and the memory (204) may be part of a communication modem / circuit / chip designed to implement wireless communication technology. The transceiver (206) may be connected to the processor (202) and may transmit and / or receive wireless signals via at least one antenna (208). The transceiver (206) may include a transmitter and / or a receiver. The transceiver (206) 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.

[0084] Hereinafter, the hardware elements of the wireless device (200) will be described in more detail. Although not limited thereto, at least one protocol layer may be implemented by at least one processor (202). For example, at least one processor (202) may implement at least one layer (e.g., a functional layer such as physical (PHY), media access control (MAC), radio link control (RLC), packet data convergence protocol (PDCP), radio resource control (RRC), and service data adaptation protocol (SDAP)). At least one processor (202) may generate at least one Protocol Data Unit (PDU) and / or at least one Service Data Unit (SDU) according to the descriptions, functions, procedures, proposals, methods, and / or operation flowcharts disclosed in this document. At least one processor (202) may generate a message, control information, data, or information according to the descriptions, functions, procedures, proposals, methods, and / or operation flowcharts disclosed in this document. At least one processor (202) can generate a signal (e.g., a baseband signal) including a PDU, an SDU, a message, control information, data or information according to the functions, procedures, proposals and / or methods disclosed in this document, and provide the signal to at least one transceiver (206). At least one processor (202) can receive a signal (e.g., a baseband signal) from at least one transceiver (206) and obtain the PDU, SDU, message, control information, data or information according to the descriptions, functions, procedures, proposals, methods and / or operational flowcharts disclosed in this document.

[0085] At least one processor (202) may be referred to as a controller, a microcontroller, a microprocessor, or a microcomputer. The at least one processor (202) may be implemented by hardware, firmware, software, or a combination thereof. For example, at least one application specific integrated circuit (ASIC), at least one digital signal processor (DSP), at least one digital signal processing device (DSPD), at least one programmable logic device (PLD), or at least one field programmable gate array (FPGA) may be included in the at least one processor (202). The descriptions, functions, procedures, proposals, methods, and / or operation 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, proposals, methods, and / or operation flowcharts disclosed in this document may be included in the at least one processor (202), or may be stored in at least one memory (204) and driven by the at least one processor (202). The descriptions, functions, procedures, suggestions, methods and / or flowcharts disclosed in this document may be implemented using firmware or software in the form of code, instructions and / or sets of instructions.

[0086] At least one memory (204) can be connected to at least one processor (202) and can store various forms of data, signals, messages, information, programs, codes, instructions and / or commands. The at least one memory (204) can be configured as a read only memory (ROM), a random access memory (RAM), an erasable programmable read only memory (EPROM), a flash memory, a hard drive, a register, a cache memory, a computer readable storage medium and / or a combination thereof. The at least one memory (204) can be located internally and / or externally to the at least one processor (202). In addition, the at least one memory (204) can be connected to the at least one processor (202) via various technologies such as a wired or wireless connection.

[0087] At least one transceiver (206) can transmit user data, control information, wireless signals / channels, etc., mentioned in the methods and / or flowcharts of this document to at least one other device. At least one transceiver (206) can receive user data, control information, wireless signals / channels, etc. mentioned in the descriptions, functions, procedures, proposals, methods and / or flowcharts disclosed in this document from at least one other device. For example, at least one transceiver (206) can be connected to at least one processor (202) and can transmit and receive wireless signals. For example, at least one processor (202) can control at least one transceiver (206) to transmit user data, control information, or wireless signals to at least one other device. Furthermore, at least one processor (202) can control at least one transceiver (206) to receive user data, control information, or wireless signals from at least one other device. In addition, at least one transceiver (206) may be connected to at least one antenna (208), and at least one transceiver (206) may be configured to transmit and receive user data, control information, wireless signals / channels, etc. mentioned in the descriptions, functions, procedures, proposals, methods and / or operation flowcharts disclosed in this document through at least one antenna (208). In this document, at least one antenna may be a plurality of physical antennas or a plurality of logical antennas (e.g., antenna ports). At least one transceiver (206) may convert the received wireless signals / channels, etc. from RF band signals to baseband signals in order to process the received user data, control information, wireless signals / channels, etc. using at least one processor (202). At least one transceiver (206) may convert the processed user data, control information, wireless signals / channels, etc. from baseband signals to RF band signals using at least one processor (202).For this purpose, at least one transceiver (206) may include an (analog) oscillator and / or filter.

[0088] The components of the wireless device described with reference to FIG. 2 may be referred to by different terms in terms of functionality. For example, the processor (202) may be referred to as a control unit, the transceiver (206) as a communication unit, and the memory (204) as a storage unit. In some cases, the communication unit may be used to mean at least a portion of the processor (202) and the transceiver (206).

[0089] The structure of the wireless device described with reference to FIG. 2 can be understood as the structure of at least a portion of various devices. For example, the structure of the wireless device illustrated in FIG. 2 can be at least a portion of various devices described with reference to FIG. 1 (e.g., a robot (100a), a vehicle (100b-1, 100b-2), an XR device (100c), a portable device (100d), a home appliance (100e), an IoT device (100f), an AI device / server (100g)). Furthermore, according to various embodiments, in addition to the components illustrated in FIG. 2, the device may further include other components.

[0090] For example, the device may be a portable device such as a smartphone, a smart pad, a wearable device (e.g., a smart watch, smart glasses), or a portable computer (e.g., a laptop, etc.). In this case, the device may further include at least one of a power supply unit that supplies power and includes a wired / wireless charging circuit, a battery, etc., an interface unit that includes at least one port for connection with another device (e.g., an audio input / output port, a video input / output port), and an input / output unit for inputting and outputting image information / signals, audio information / signals, data, and / or information input from a user.

[0091] For example, the device may be a mobile device such as a mobile robot, a vehicle, a train, an aerial vehicle (AV), a ship, etc. In this case, the device may further include at least one of a driving unit including at least one of an engine, a motor, a power train, wheels, brakes, and a steering unit of the device, a power supply unit including a wired / wireless charging circuit, a battery, etc. that supplies power, a sensor unit that senses status information, environmental information, and user information of the device or its surroundings, an autonomous driving unit that performs functions such as path maintenance, speed control, and destination setting, and a position measurement unit that obtains location information of the mobile device through a global positioning system (GPS) and various sensors.

[0092] For example, the device may be an XR device such as an HMD, a head-up display (HUD) installed in a vehicle, a television, a smartphone, a computer, a wearable device, a home appliance, a digital signage, a vehicle, a robot, etc. In this case, the device may further include at least one of a power supply unit that supplies power and includes a wired / wireless charging circuit, a battery, etc., an input / output unit that obtains control information, data, etc. from the outside and outputs the generated XR object, and a sensor unit that senses status information, environmental information, and user information of the device or the surroundings of the device.

[0093] For example, the device may be a robot that can be classified into industrial, medical, household, military, etc. types depending on the purpose or field of use. In this case, the device may further include at least one of a sensor unit that senses status information, environmental information, and user information of the device or its surroundings, and a driving unit that performs various physical actions, such as moving the robot joints.

[0094] For example, the device may be an AI device such as a TV, a projector, a smartphone, a PC, a laptop, a digital broadcasting terminal, a tablet PC, a wearable device, a set-top box (STB), a radio, a washing machine, a refrigerator, digital signage, a robot, a vehicle, etc. In this case, the device may further include at least one of an input unit that acquires various types of data from the outside, an output unit that generates output related to sight, hearing, or touch, a sensor unit that senses status information, environmental information, and user information of the device or its surroundings, and a training unit that trains a model composed of an artificial neural network using learning data.

[0095] The structure of the wireless device illustrated in FIG. 2 may be understood as a part of a RAN node (e.g., base station, DU, RU, RRㅗ, etc.). That is, the device illustrated in FIG. 2 may be a RAN node. In this case, the device may further include a wired transceiver for front haul and / or back haul communication. However, if the front haul and / or back haul communication is based on wireless communication, at least one transceiver (206) illustrated in FIG. 2 may be used for front haul and / or back haul communication, and a wired transceiver may not be included.

[0096]

[0097] FIG. 3 illustrates a method for processing a transmission signal applicable to the present disclosure. For example, the transmission signal may be processed by a signal processing circuit. At this time, the signal processing circuit (300) may include a scrambler (310), a modulator (320), a layer mapper (330), a precoder (340), a resource mapper (350), and a signal generator (360). At this time, as an example, the operations / functions of FIG. 3 may be performed in the processor (202) and / or the transceiver (206) of FIG. 2. Furthermore, as an example, the hardware elements of FIG. 3 may be implemented in the processor (202) and / or the transceiver (206) of FIG. 2. As an example, blocks 310 to 360 may be implemented in the processor (202) of FIG. 2. Additionally, blocks 310 to 350 may be implemented in the processor (202) of FIG. 2, and block 360 may be implemented in the transceiver (206) of FIG. 2, and are not limited to the above-described embodiment.

[0098] The codeword can be converted into a wireless signal through the signal processing circuit (300) of FIG. 3. Here, the codeword is an encoded bit sequence of an information block. The information block may include a transport block (e.g., a UL-SCH transport block, a DL-SCH transport block). Here, the information block may include data related to AI (e.g., training data, AI model data, input data, output data, etc.), and the codeword may be an encoded bit sequence corresponding to the data related to AI. The wireless signal may be transmitted through various physical channels (e.g., PUSCH, PDSCH). Specifically, the codeword may be converted into a bit sequence scrambled by a scrambler (310). The scramble sequence used for scrambling is generated based on an initialization value, and the initialization value may include ID information of the wireless device, etc. The scrambled bit sequence may be modulated into a modulation symbol sequence by a modulator (320). Modulation schemes may include pi / 2-BPSK (pi / 2-binary phase shift keying), m-PSK (m-phase shift keying), m-QAM (m-quadrature amplitude modulation), etc.

[0099] A complex modulation symbol sequence can be mapped to at least one transport layer by a layer mapper (330). Here, a transport layer is a logical resource unit for mapping a signal or data transmitted through spatial resources to antenna ports, and one transport layer can correspond to one stream or one antenna port. Each of the complex modulation symbols included in the complex modulation symbol sequence is mapped to at least one transport layer, thereby determining which antenna port it will be transmitted through. The modulation symbols of each transport layer can be mapped to the corresponding antenna port(s) by a precoder (340). The output z of the precoder (340) can be obtained by multiplying the output y of the layer mapper (330) by a precoding matrix W of NХM. Here, N is the number of antenna ports, and M is the number of transport layers. Here, the precoder (340) may perform precoding after performing transform precoding (e.g., discrete Fourier transform (DFT) transform) on complex modulation symbols. Additionally, the precoder (340) may perform precoding without performing transform precoding.

[0100] The resource mapper (350) can map modulation symbols of each antenna port to time-frequency resources. The time-frequency resources can include multiple symbols (e.g., CP-OFDMA symbols, DFT-s-OFDMA symbols) in the time domain and multiple subcarriers in the frequency domain. The signal generator (360) generates a wireless signal from the mapped modulation symbols, and the generated wireless signal can be transmitted to another device through each antenna. To this end, the signal generator (360) can include an inverse fast Fourier transform (IFFT) module, a cyclic prefix (CP) inserter, a digital-to-analog converter (DAC), a frequency uplink converter, and the like.

[0101] The signal processing process for a received signal in a wireless device may be configured in reverse order of the signal processing process (310 to 360) of FIG. 3. For example, a wireless device (e.g., 200 of FIG. 2) may receive a wireless signal from the outside through an antenna port / transceiver. The received wireless signal may be converted into a baseband signal through a signal restorer. For this purpose, the signal restorer may 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 may be restored to a codeword through a resource demapper process, a postcoding process, a demodulation process, and a descrambling process. The codeword may be restored to the original information block through decoding. Therefore, a signal processing circuit (not shown) for a received signal may include a signal restorer, a resource demapper, a postcoder, a demodulator, a descrambler, and a decoder.

[0102]

[0103] Figure 4 illustrates a communication procedure between a terminal and a base station applicable to the present disclosure. Figure 4 illustrates operations of a terminal (410) and a base station (420) transmitting and / or receiving data and operations performed prior thereto.

[0104] Referring to FIG. 4, in step 401, the terminal (410) and the base station (420) perform synchronization. For example, the terminal (410) performs an initial cell search operation. Specifically, the terminal (410) can detect at least one synchronization signal transmitted from the base station (420) according to a predefined rule. Here, the synchronization signal can include multiple synchronization signals classified according to structure or purpose (e.g., primary synchronization signal, secondary synchronization signal). Through this, the terminal (410) can check the boundary of the frame, subframe, slot, and / or symbol of the base station (420) and obtain information about the base station (420) (e.g., cell identifier).

[0105] In step 403, the terminal (410) obtains system information transmitted from the base station (420). The system information is information related to the properties, characteristics, and / or capabilities of the base station (420) required to access the base station (420) and use the service, and may be classified by content (e.g., whether it is essential for access), transmission structure (e.g., channel used, whether provided on-demand), etc., and may be classified into, for example, a master information block (MIB) and a system information block (SIB). If necessary, the terminal (410) may transmit a signal requesting system information before receiving the system information. The system information may include information related to an AI function. For example, the system information may include at least one of information related to an AI model, information related to training, and information related to inference / prediction, as information required for operations performed based on AI. However, the request and provision of the system information may be performed after a random access procedure described below.

[0106] In step 405, the terminal (410) and the base station (420) perform a random access procedure. The terminal (410) may transmit and / or receive at least one message (e.g., a random access preamble, a random access response (RAR) message, etc.) for the random access procedure based on information related to the random access channel of the base station (420) obtained through system information (e.g., channel position, channel structure, supported preamble structure, etc.). For example, the terminal (410) may transmit a preamble (e.g., MSG1) through the random access channel, receive an RAR message (e.g., MSG2), transmit a message (e.g., MSG3) including information related to the terminal (410) (e.g., identification information) to the base station (420) using scheduling information included in the RAR message, and receive a message (e.g., MSG4) for contention resolution and / or connection establishment. As another example, MSG1 and MSG3 may be sent and received as one message, or MSG2 and MSG4 may be sent and received as one message.

[0107] In step 407, the terminal (410) and the base station (420) perform signaling of control information. Here, the control information may be defined in various layers, such as a layer that controls a connection (e.g., a radio resource control (RRC) layer), a layer that handles mapping between logical channels and transport channels (e.g., a media access control (MAC) layer), and a layer that handles physical channels (e.g., a physical (PHY) layer). For example, the terminal (410) and the base station (420) may perform at least one of signaling for establishing a connection, signaling for determining settings related to communication, and signaling for indicating allocated resources. In addition, the signaling of the control information may be performed to convey information related to an AI function. For example, the information related to an AI function is information necessary for an operation performed based on AI, and may include at least one of information related to an AI model, information related to training, and information related to inference / prediction. More specifically, information related to the AI ​​function signaled in step 407 may be combined and / or combined with information related to the AI ​​function signaled in step 403, and the two may be defined in a hierarchical, mutually complementary, or substitutive structure.

[0108] In step 409, the terminal (410) and the base station (420) transmit and / or receive data. In other words, the terminal (410) and the base station (420) can process, transmit, and / or receive data based on the signaling of the control information. For example, when transmitting data, the terminal (410) or the base station (420) can perform at least one of channel encoding, rate matching, scrambling, constellation mapping, layer mapping, waveform modulation, antenna mapping, and resource mapping on the information bits. Conversely, when receiving data, the terminal (410) or the base station (420) can perform at least one of signal extraction from resources, waveform demodulation for each antenna, signal arrangement considering layer mapping, constellation demapping, descrambling, and channel decoding. Here, the transmitted data is data related to AI, and may include, for example, data for AI-based operations or data generated by AI-based operations.

[0109] Steps 401 to 409 illustrated with reference to FIG. 4 do not necessarily have to be performed in the order illustrated in FIG. 4, and the order of at least some of the steps may vary. Furthermore, at least some of steps 401 to 409 may be combined into a single step or omitted. That is, the steps illustrated in FIG. 4 may be performed in various modified forms.

[0110]

[0111] 6G communication systems and core implementation technologies of 6G systems

[0112] The 5G system defines various operating bands within FR1 (frequency range 1), which covers 410 MHz to 7125 MHz, and FR2 (frequency range 2), which covers 24,250 MHz to 71,000 MHz. Various frequencies are being discussed as operating bands for the subsequent 6G system, and the use of higher frequencies than 5G systems is also being considered for wider bandwidth and higher transmission speeds. One such band is the THz (terahertz) frequency band, which covers approximately 100 GHz to 10 THz. The THz frequency band is a band that has both the transparency of radio waves and the straightness of light waves, and communications using the THz frequency band are expected to play a transitional role from existing radio-centered communications to lightwave-based communications.

[0113] 6G systems utilizing the THz frequency band are aimed at i) very high data rates per device, ii) a very large number of connected devices, iii) global connectivity, iv) very low latency, v) reducing energy consumption of battery-free IoT devices, vi) ultra-reliable connectivity, and vii) connected intelligence with machine learning capabilities. The vision of the 6G system can be divided into four aspects: “intelligent connectivity,” “deep connectivity,” “holographic connectivity,” and “ubiquitous connectivity,” and the 6G system can be designed to satisfy the requirements as shown in [Table 1] below.

[0114] Per device peak data rate1 TbpsE2E latency1 msMaximum spectral efficiency100 bps / HzMobility supportup to 1000 km / hrSatellite integrationFullyAIFullyAutonomous vehicleFullyXRFullyHaptic CommunicationFully

[0115] At this time, the 6G system may have key factors such as enhanced mobile broadband (eMBB), ultra-reliable low latency communications (URLLC), massive machine type communications (mMTC), AI integrated communication, tactile internet, high throughput, high network capacity, high energy efficiency, low backhaul and access network congestion, and enhanced data security. FIG. 5 illustrates an example of a communication structure that can be provided in a 6G system applicable to the present disclosure. Referring to FIG. 5, the 6G system is expected to have simultaneous wireless communication connectivity that is 50 times higher than that of a 5G wireless communication system. URLLC, a key feature of 5G, is expected to become an even more crucial technology in 6G communications, offering end-to-end latency of less than 1 ms. Furthermore, 6G systems will boast significantly higher volumetric spectral efficiency than the commonly used area spectral efficiency. 6G systems can offer extremely long battery life and advanced battery technologies for energy harvesting, eliminating the need for separate charging for mobile devices in 6G systems. New network characteristics in 6G may include:

[0116] - Satellite integrated network: 6G is expected to integrate with satellites to provide a global mobile network. The integration of terrestrial, satellite, and airborne networks into a single wireless communications system is crucial for 6G.

[0117] 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).

[0118] - Seamless integration of wireless information and energy transfer: 6G wireless networks will transfer power to charge the batteries of devices such as smartphones and sensors. Therefore, wireless information and energy transfer (WIET) will be integrated.

[0119] - 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.

[0120] Some general requirements for the new network characteristics of 6G, such as the above, may be as follows:

[0121] - Small cell networks: The concept of small cell networks was introduced to improve received signal quality in cellular systems by increasing throughput, energy efficiency, and spectral efficiency. Consequently, small cell networks are essential for 5G and beyond-5G (5GB) communication systems. Accordingly, 6G communication systems also adopt the characteristics of small cell networks.

[0122] Ultra-dense heterogeneous networks: Ultra-dense heterogeneous networks will be another key feature of 6G communication systems. Multi-tier networks comprised of heterogeneous networks improve overall QoS and reduce costs.

[0123] High-capacity backhaul: Backhaul connections are characterized by high-capacity backhaul networks to support high-volume traffic. High-speed fiber optics and free-space optics (FSO) systems may be potential solutions to this problem.

[0124] - 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.

[0125] - Softwarization and virtualization: Softwarization and virtualization are two critical features that form the foundation of the design process for 5GB networks to ensure flexibility, reconfigurability, and programmability. Furthermore, billions of devices can be shared on a shared physical infrastructure.

[0126] To satisfy the above-mentioned characteristics, the core implementation technologies of the 6G system may include artificial intelligence (AI), THz (terahertz) communication, optical wireless technology, FSO backhaul network, massive MIMO technology, blockchain, 3D networking, quantum communication, unmanned aerial vehicles, cell-free communication, wireless information and energy transfer (WIET), integration of sensing and communication, integration of access backhaul networks, holographic beamforming, big data analysis, and large intelligent surface (LIS).

[0127] For example, THz communication can be utilized in 6G systems. THz communication is a communication that utilizes a spectrum in a frequency band between 0.3 THz and 3 THz with a corresponding wavelength in the range of 0.1 mm to 1 mm, as shown in FIG. 6. Referring to FIG. 6, the frequency band of THz waves is located in the middle region between the infrared band and the millimeter wave band, and therefore, THz waves can be understood as radio waves with the shortest wavelength and light waves with the longest wavelength. Therefore, THz waves share some of the characteristics of infrared and microwave waves, and specifically, they can simultaneously have the transparency of electromagnetic waves and the straightness of light waves.

[0128]

[0129] Fig. 7 illustrates a THz communication method applicable to the present disclosure. Referring to Fig. 7, THz wireless communication refers to wireless communication using THz waves having a frequency of approximately 0.1 to 10 THz (1 THz = 1012 Hz), and may refer to terahertz (THz) band wireless communication using a very high carrier frequency of 100 GHz or higher. THz waves are located between the RF (Radio Frequency) / millimeter (mm) and infrared bands, and (i) compared to visible light / infrared rays, they penetrate non-metallic / non-polarizable materials well, and compared to RF / millimeter waves, they have a shorter wavelength, thus having high linearity and enabling beam focusing.

[0130] In addition, since the photon energy of THz waves is only a few meV, it has the characteristic of being harmless to the human body. The frequency band expected to be used for THz wireless communication may be the D-band (110 GHz to 170 GHz) or H-band (220 GHz to 325 GHz) bands where propagation loss due to absorption of molecules in the air is small. In addition to 3GPP, standardization discussions for THz wireless communication are being centered around the IEEE 802.15 THz WG (working group), and standard documents issued by the IEEE 802.15 TG (task group) (e.g., TG3d, TG3e) can specify or supplement the contents described in this specification. THz wireless communication can be applied to wireless cognition, sensing, imaging, wireless communication, THz navigation, etc.

[0131] Specifically, referring to Fig. 7, THz wireless communication scenarios can be categorized into macro networks, micro networks, and nanoscale networks. In macro networks, THz wireless communication can be applied to vehicle-to-vehicle (V2V) connections and backhaul / fronthaul connections. In micro networks, THz wireless communication can be applied to fixed point-to-point or multi-point connections such as indoor small cells, wireless connections in data centers, and near-field communication such as kiosk downloading. Table 2 below shows examples of technologies that can be utilized in THz waves.

[0132] Transceivers DeviceAvailable immature: UTC-PD, RTD and SBDModulation and codingLow order modulation techniques (OOK, QPSK), LDPC, Reed Soloman, Hamming, Polar, TurboAntennaOmni and Directional, phased array with low number of antenna elementsBandwidth69 GHz (or 23 GHz) at 300 GHzChannel modelsPartiallyData rate100 GbpsOutdoor deploymentNoFee space lossHighCoverageLowRadio Measurements300 GHz inddorDevice sizeFew micrometers

[0133] FIG. 8 illustrates a THz signal generation method applicable to the present disclosure. FIG. 9 also illustrates a wireless communication transceiver applicable to the present disclosure. Referring to FIGS. 8 and 9, the optical device-based THz wireless communication technology refers to a method of generating and modulating a THz signal using an optical device. The optical device-based THz signal generation technology is a technology that generates an ultra-high-speed optical signal using a laser and an optical modulator, and converts it into a THz signal using an ultra-high-speed photodetector. Compared to a technology that uses only electronic devices, this technology makes it easy to increase the frequency, enables high-power signal generation, and obtains a flat response characteristic over a wide frequency band. For the optical device-based THz signal generation, as illustrated in FIG. 8, a laser diode, a wideband optical modulator, and an ultra-high-speed photodetector are required. In the case of FIG. 8, light signals from two lasers with different wavelengths are combined to generate a THz signal corresponding to the wavelength difference between the lasers. In Fig. 8, an optical coupler refers to a semiconductor device that transmits an electrical signal using optical waves to provide electrical isolation and coupling between circuits or systems, and a uni-travelling carrier photo-detector (UTC-PD) is a type of photodetector that uses electrons as active carriers and reduces the travel time of electrons through bandgap grading. The UTC-PD is capable of photodetection at 150 GHz or higher.In Fig. 9, EDFA (erbium-doped fiber amplifier) ​​represents an erbium-doped fiber amplifier, PD (photo detector) represents a semiconductor device that can convert an optical signal into an electrical signal, OSA represents an optical module (optical sub assembly) that modularizes various optical communication functions (e.g., photoelectric conversion, electro-optical conversion, etc.) into a single component, and DSO represents a digital storage oscilloscope.

[0134] Figure 10 illustrates a transmitter structure applicable to the present disclosure.

[0135] Referring to Figure 10, in order to modulate data into an optical signal, an optical source such as a laser can be passed through an optical wave guide to change the phase of the signal, etc. At this time, data is loaded by changing the electrical characteristics through a microwave contact, etc. Therefore, the optical modulator output is formed as a modulated waveform.

[0136] Data may be provided from a data signal generator. Here, the data may include various user data, configuration information, control information, etc. transmitted through a channel. Furthermore, the data may include data related to AI-based operations, such as information for configuring an AI model, input / output data for tasks of the AI ​​model, etc. To this end, components related to AI functions (e.g., an AI processing unit) may be included in the data signal generator or may be linked to the data signal generator.

[0137] An optical / electronic converter (O / E converter) can generate THz pulses by optical rectification using a nonlinear crystal, photoelectric conversion using a photoconductive antenna, or emission from a bunch of relativistic electrons. The THz pulse generated in the above manner can have a length in the range of femtoseconds to picoseconds. The optical / electronic converter (O / E converter) performs down conversion by utilizing the nonlinearity of the device.

[0138] Considering the THz spectrum usage, it is likely that multiple contiguous GHz bands will be used for THz systems, either fixed or for mobile services. For an outdoor scenario, the available bandwidth can be categorized based on an oxygen attenuation of 10^2 dB / km in the spectrum up to 1 THz. Accordingly, a framework in which the available bandwidth is divided into multiple band chunks can be considered. As an example of this framework, if the THz pulse length for a single carrier is set to 50 ps, ​​the bandwidth (BW) becomes approximately 20 GHz.

[0139] Effective down-conversion from the infrared band to the THz band depends on how to utilize the nonlinearity of the optical / electrical converter (O / E converter). In other words, to down-convert to the desired THz band, it is necessary to design an O / E converter with the most ideal non-linearity for transferring to the THz band. If an O / E converter that is not suitable for the target frequency band is used, errors in the amplitude and phase of the pulse are likely to occur.

[0140] A THz transmission and reception system can be implemented using a single optical-to-electrical converter in a single-carrier system. Depending on the channel environment, optical-to-electrical converters may be required as many as the number of carriers in a multi-carrier system. This phenomenon will be particularly noticeable in a multi-carrier system that utilizes multiple broadbands according to the aforementioned spectrum usage plan. In this regard, a frame structure for the multi-carrier system may be considered. A signal down-frequency converted based on an optical-to-electrical converter may be transmitted in a specific resource region (e.g., a specific frame). The frequency region of the specific resource region may include multiple chunks. Each chunk may be composed of at least one component carrier (CC).

[0141] Transmitting system information (e.g., MIB) in the THz frequency band can be inefficient because the beam width becomes narrower in high-frequency bands, requiring more beam sweeps to cover the entire cell area. This method of transmitting system information is particularly inefficient when there are only a few users within the cell. Accordingly, a system information transmission procedure, such as that illustrated in FIG. 11, may be employed.

[0142] Figure 11 illustrates a system information transmission procedure applicable to the present disclosure. Figure 11 illustrates an example of a procedure for transmitting system information for THz communication. The procedure illustrated in Figure 11 can be combined with various embodiments of the present disclosure described below. For example, the embodiments described below can be performed based on the system information acquired by the procedure illustrated in Figure 11. As another example, information and / or data transmitted in the procedure illustrated in Figure 11 can be generated and / or processed according to the embodiments described below.

[0143] Referring to FIG. 11, in step 1101, the base station (1120) transmits system information of cell #1 through cell #2. That is, the base station (1120) provides at least two cells, cell #1 uses a THz frequency band, and cell #2 uses a frequency band other than the THz frequency band. Here, the system information may include at least one of an SFN, a PDCCH configuration for SIB1, cell barring, cell re-selection, and subcarrier spacing generated in a higher layer, and may include at least one of an SFN, a half frame indicator, and an SSB index generated in a physical layer. For this purpose, as an example, cell #1 and cell #2 may have a relationship of a secondary cell and a primary cell.

[0144] In step 1103, UE (1110) acquires synchronization for cell #1. Synchronization can be acquired by detecting a synchronization signal. Typically, synchronization is acquired before receiving system information. However, since system information for cell #1 is received from cell #2, synchronization acquisition for cell #1 can be performed after receiving the system information. For example, UE (1110) can acquire synchronization based on system information. However, unlike FIG. 11, in another example, synchronization acquisition can be performed before step 1101.

[0145] In step 1105, UE (1110) transmits a signal for accessing cell #1. For example, the signal may include a random access preamble. The structure of the signal and the resources (e.g., channels) for transmitting the signal can be identified through system information. Thereafter, in step 1107, UE (1110) and base station (1120) perform an access procedure for cell #1 and communicate. In this step, operations according to various embodiments described below may be performed.

[0146] The procedure described with reference to FIG. 11 may be performed when UE (1101) first connects to cell #1 of base station (1120). Alternatively, a similar procedure may be performed when UE (1101) hands over to cell #1 of base station (1120). However, in the case of handover, system information of cell #1 may be received from a cell of a base station other than cell #2 of base station (1120).

[0147]

[0148] Communications in the THz band are expected to experience extremely severe path loss, and to overcome this, terminals and base stations must use extremely sharp beams. The use of sharp beams means that terminals and base stations must perform beam control in addition to beamforming, and the number of beams used increases significantly. Consequently, it takes a very long time to align the transmit and receive beams between the base station and terminals. Furthermore, if the beam alignment between the base station and terminals is misaligned due to the movement or movement of the terminals, frequent re-alignment of the beams is required, which can lead to link instability. Accordingly, a beam management procedure, as illustrated in FIG. 12 below, may be used.

[0149] FIG. 12 illustrates a beam management procedure applicable to the present disclosure. FIG. 12 illustrates an example of a procedure for searching and / or selecting beams for THz communication. The procedure illustrated in FIG. 12 may be combined with various embodiments of the present disclosure described below. For example, the embodiments described below may be performed using at least one beam acquired by the procedure illustrated in FIG. 11. As another example, information and / or data transmitted in the procedure illustrated in FIG. 12 may be generated and / or processed according to the embodiments described below. Herein, a beam may be referred to as a 'spatial domain filter', a 'spatial domain transmit filter', a 'spatial domain receive filter', and other terms having equivalent technical meanings thereto.

[0150] Referring to FIG. 12, in step 1201, a base station (1220) configures resources for beam management. Here, the resources may include at least one of time-frequency resources, channels, and spatial resources (e.g., antenna ports). For example, the base station (1220) may utilize a beam search signal (BSS) that is transmitted spatially separated from an existing downlink signal / channel for beam search. Here, the BSS may be transmitted based on a dedicated port for beam search. The dedicated port may be a different port from a port for transmitting an existing downlink signal / channel (e.g., SSB, PDSCH, etc.). BSS is a term defined for convenience of explanation, and the technical concept according to the present embodiment is not limited to the term BSS itself. That is, a signal transmitted based on a dedicated port defined / configured for beam search may be included in the technical concept according to the present embodiment.

[0151] In step 1203, the base station (1201) transmits measurement signals using multiple transmission beams. For example, the measurement signals may include at least one of a reference signal and a synchronization signal. At this time, the measurement signals may be transmitted as many times as the number of beams that require measurement, and may be transmitted in a multi-beam transmission method that forms multiple beams simultaneously to reduce sweeping time. Here, the multi-beam transmission may be performed based on at least one of a multi-panel, a sub-array, and a true time delay (TTD).

[0152] In step 1205, the UE (1210) transmits a feedback signal to the base station (1220). The feedback signal indicates at least one beam selected by the UE (1210). The UE (1210) may select at least one preferred beam based on the measurement signals received in step 1203. In step 1207, the UE (1210) and the base station (1220) perform communication. At this time, the UE (1210) and the base station (1220) may perform communication using the beam selected in step 1205. If channel reciprocity is established, the transmission beam of the UE (1210) may also be determined through steps 1203 and 1205, and thus, the transmission operation of the UE (1210) may also be performed using the beam selected in step 1205. If channel reciprocity is not established, a procedure including transmitting measurement signals of the UE (1210) and transmitting feedback signals of the base station (1220) may be performed to determine the transmission beam of the UE (1210). In step 1207, operations according to various embodiments described below may be performed.

[0153]

[0154] Specific embodiments of the present disclosure

[0155] The present disclosure relates to beamforming in wireless communication systems, and more particularly, to techniques for beamforming in situations where communication and sensing are performed simultaneously. Specifically, the present disclosure relates to communication and sensing techniques applicable to situations where communication with multiple users and sensing of multiple targets are performed. Below, the present disclosure describes a joint communication and sensing (JCAS) protocol for multi-user communication and multi-target tracking according to various embodiments.

[0156] FIG. 13 illustrates a JCAS beam forming protocol according to one embodiment of the present disclosure.

[0157] Referring to FIG. 13, the JCAS beamforming protocol includes beam sweeping (1301), initial target and clutter positioning (1303), JCAS beamforming metric determination (1305), beamforming problem resolution (1307), and beamforming (1309). The JCAS beamforming protocol can be performed in a JCAS device including at least one antenna. For example, the JCAS beamforming protocol can be performed in a base station (BS) or a terminal (UE). The base station can be referred to as an eNB or a gNB. The terminal can be referred to as a UE. A JCAS device according to an embodiment of the present disclosure can be, for example, a monostatic JCAS device. As another example, the JCAS device can be a bistatic JCAS device. Although the present disclosure is described below assuming a monostatic JCAS device, the present disclosure is not limited to a monostatic JCAS and can be applied to all JCAS devices.

[0158] The JCAS device searches for and senses communication devices using beam sweeping (1301). Here, the communication devices include all devices capable of communicating with the JCAS device. For example, the communication devices may be base stations or terminals. The JCAS device can simultaneously search for and sense communication devices. In other words, the JCAS device can perform both search and sensing of communication devices using a single beam sweeping (1301).

[0159] The JCAS device detects targets and clutter using initial target and clutter positioning (1303). Here, clutter refers to an object that does not want to be detected. In other words, clutter refers to an object other than a target. The JCAS device performs initial target and clutter positioning (1303) to obtain initial information. For example, the initial information may include the distance, angle, RCS, velocity, and intensity of echo signals of each sensing target and clutter. The JCAS device may search for a communication device by performing beam sweeping (1301) and obtain initial information by receiving an echo signal based on the beam sweeping (1301). The beam sweeping (1301) and initial target and clutter positioning (1303) for obtaining initial information may be referred to as initial beam sweeping.

[0160]

[0161] The JCAS device determines a metric for JCAS beamforming (1305). The metric for JCAS beamforming is a metric for forming a beam that allows the JCAS device to perform search and sensing simultaneously. The metric for beamforming may be determined from among multiple metrics.

[0162] Although FIG. 13 illustrates two metrics, this is only for convenience of explanation, and the metric for beamforming of the present disclosure does not necessarily have to be determined from among the two metrics. The plurality of metrics may include a first metric and a second metric. The first metric includes a metric using Sum rate and MSE (mean square error). For example, the first metric may include a metric using Sum rate for beamforming for communication and MSE for transmit beam patterns for beamforming for sensing. The second metric includes a metric using Sum rate and SCNR (signal to clitter plus noise ratio). For example, the second metric may include a metric using Sum rate for beamforming for communication and SCNR for beamforming for sensing.

[0163] For example, a metric for beamforming may be determined from among multiple metrics based on the JCAS operating conditions. In another example, a metric for beamforming may be determined from among multiple metrics based on initial information. In yet another example, a metric for beamforming may be determined from among multiple metrics based on the JCAS operating conditions and initial information.

[0164] For example, if the initial beam sweeping results in the target's echo power being higher than the clutter's echo power, the first metric may be selected. As another example, if the initial beam sweeping results in the target's echo power being lower than or equal to the clutter's echo power, the second metric may be selected. As another example, if the initial beam sweeping results in the clutter's echo power being lower than a value obtained by multiplying the target power by a predefined ratio, the first metric may be selected. For example, if the clutter's echo power is lower than 0.7 times the target power, the first metric may be selected as the metric for beam forming.

[0165]

[0166] The JCAS device includes multiple antennas. A communication device communicating with the JCAS device may be a communication device having at least one antenna. The JCAS device may have at least one sensing target. Here, the signal transmitted through beamforming is expressed as in Mathematical Expression 1.

[0167]

[0168] Here, x is the transmission signal, F is the beamforming matrix, s is the transmission symbol, is a communication symbol, Is Beamforming matrix for, is a radar dedicated symbol, silver It means the beam forming matrix for .

[0169]

[0170] Hereinafter, a first metric according to one embodiment of the present disclosure is described. The first metric may be a metric that utilizes Sum rate for beamforming for communication and MSE for beamforming for sensing.

[0171] The signal-to-interference-plus-noise-ratio (SINR) of the kth communication device is defined as in mathematical equation 2. The communication device may be a terminal that performs communication with the device. Alternatively, the communication device may be a base station that performs communication with the device.

[0172]

[0173] Here, vector is the channel of the kth communication device, Is The kth column vector, Is The j-th column vector, represents noise power, and P represents transmission power.

[0174] The sum transmission rate of the kth communication device is calculated based on the SINR as in Equation 3.

[0175]

[0176] Here, stands for SINR.

[0177] The MSE for the transmission beam pattern used as a sensing metric is calculated as in Equation 4.

[0178]

[0179] Here, Silver angle refers to the required beam pattern for, is the beamforming matrix angle generated by It means the beam of. For example, can be defined based on the target angle obtained using the initial beam sweep. is calculated as in mathematical formula 5.

[0180]

[0181] Here, is the angle is the transmit array response vector.

[0182] Combining Equations 1 to 5, a beamforming problem based on the first metric can be generated as in Equation 6. The beamforming problem based on the first metric can be referred to as an MSE-based beamforming problem.

[0183]

[0184] Here, MSE R is the MSE value for the transmission beam pattern, R k (F) is the sum transmission rate of the kth communication device based on SINR, Tr(FF H ) is a constraint on the transmission power, means the MSE threshold.

[0185]

[0186] Below, a second metric according to one embodiment of the present disclosure is described. The second metric may be a metric using sum rate and signal-to-clutter-plus-noise ratio (SCNR).

[0187] SCNR is the ratio of the received power of the target echo signal to that of the clutter. SCNR is defined as in Equation 7.

[0188]

[0189] Here, is an echo channel about the target, Echo channel for silver clutter, refers to the receiver noise power of the JCAS device. Here, can be expressed as in mathematical formula 8.

[0190]

[0191] Here, is the angle Transmit array response vector for, is the receive array response vector.

[0192] can be expressed as in mathematical formula 9.

[0193]

[0194] Here, here, is the angle is the transmission array response vector for . is the receive array response vector.

[0195] and may be a value obtained based on the initial beam sweeping.

[0196] Based on Equations 7 to 9, a beamforming problem based on the second metric is generated as in Equation 10. The beamforming problem based on the second metric may be referred to as a beamforming problem based on SCNR.

[0197]

[0198] Here, is the SCNR value for the transmission beam pattern, R k (F) is the sum transmission rate of the kth communication device based on SINR, Tr(FF H ) is a constraint on the transmission power, means the SCNR threshold.

[0199]

[0200] The JCAS device performs beamforming problem solving (1307). The JCAS device can solve the beamforming problem generated based on the metric determined in JCAS beamforming metric determination (1305). The JCAS device can solve the beamforming problem using a predefined algorithm.

[0201] Below, an algorithm for solving a beam forming problem based on the first metric or the second metric is described.

[0202] To solve the beamforming problem based on the first metric, channel estimation errors must be considered. A channel with channel estimation errors is expressed as in Equation 11.

[0203]

[0204] Here, is the channel estimation error. The covariance of the channel estimation error is is expressed as . Table 3 shows the calculation formula for calculating the first-order KKT conditions. Here, am.

[0205] lemma 1. the KKT stationarity condition of the problem in (30) is satisfied if the following condition holds:(32) where(33) (34) Here, in (33) and (34) is the Lagrangian multiplier and(35) (36) where and are any functions that satisfy (36)

[0206] Here, and are expressed as mathematical expressions 12 and 13, respectively.

[0207]

[0208]

[0209] stands for the Kronecker product, is the standard basis vector It is a diagonal matrix with elements as diagonal elements. is the Lagrangian multiplier, which is a parameter that requires optimization.

[0210] A solution that satisfies the conditions in Table 3 and achieves the optimal stationary point. A first algorithm for finding is described. The first algorithm is a fixed Based on value is an algorithm for optimizing. In this specification, at least one algorithm can be expressed using pseudo-code.

[0211] Table 4 presents an example of a first algorithm for solving a beamforming problem using the first metric. Table 4 also expresses an embodiment of the first algorithm in pseudocode.

[0212] Algorithm 1:GPI-based beamforming1initialize: 2 Set the iteration count t=13while or do4 build matrix 5 build matrix 6 Compute 7 t t+18 9return .

[0213] Here, is the tth inner solution, is the stopping criterion of the first algorithm. and is a matrix represented in Table 3. At least some steps of the first algorithm may be repeated until a predefined condition is satisfied. For example, steps 4 to 7 may be repeated based on the condition of step 3. The first algorithm may be performed, for example, by a JCAS device.

[0214] The JCAS device finds the solution using the first algorithm. The best for A second algorithm is used to find the value. The second algorithm may be, for example, a bisection search algorithm. The solution finally obtained using the first algorithm may be referred to as the first solution.

[0215] Table 5 illustrates one embodiment of the second algorithm. Table 5 expresses one embodiment of the second algorithm in pseudocode.

[0216] Algorithm 2: GPI-ISAC1initialize: 2 Set the iteration count n=13While or do4 Algorithm 1 with 5if then6 7else8 9 n n+110 11return

[0217] Here, is the nth external solution, is a stopping criterion. At least some processes of the second algorithm can be repeatedly performed until a predefined condition is satisfied. The second algorithm can be performed by the JCAS device. In other words, the JCAS device uses the first algorithm and the second algorithm to find the optimal Find the value, A beam can be formed based on the value. The final solution obtained using the second algorithm can be referred to as the second solution.

[0218]

[0219] Table 6 shows the calculation formula for calculating the KKT conditions of the beam forming problem based on the second metric.

[0220] lemma 2. the KKT stationarity condition of the problem in (42) is satisfied if the following condition holds:(46) where(47) (48) Here, in (47) and (48) is the Lagrangian multiplier and(49) where and are any functions that satisfy (49).

[0221] Here, the matrix and are defined as in mathematical expressions 14 and 15, respectively.

[0222]

[0223]

[0224] Here, is an echo channel for the target, Each represents an echo channel for clutter. stands for the Kronecker product, is the Lagrangian multiplier, which is a parameter that requires optimization.

[0225] A solution that satisfies the conditions in Table 6 and achieves the optimal stationary point. An embodiment of the first algorithm for finding the second metric is shown in Table 7. Table 7 shows an embodiment of the first algorithm for solving the beam forming problem using the second metric.

[0226] Algorithm 1:GPI-based beamforming1initialize: 2 Set the iteration count t=13while or do4 build matrix 5 build matrix 6 compute 7 t t+18 9return .

[0227] Here, and is the matrix expressed in Table 6, is the inner solution of the first algorithm, is a stopping criterion of the first algorithm. At least some steps of the first algorithm may be repeated until a predefined condition is satisfied. For example, steps 4 to 7 may be repeated based on the condition of step 3. The first algorithm may be performed, for example, by a JCAS device.

[0228] The JCAS device finds the solution using the first algorithm. The best for A second algorithm is used to find the value. The second algorithm may be, for example, a bisection search algorithm.

[0229] Table 8 shows one embodiment of a second algorithm for solving the beamforming problem based on the second metric.

[0230] Algorithm 2: GPI-ISAC1initialize: 2 Set the iteration count n=13While or do4 Algorithm 1 with 5if then6 7else8 9 n n+110 11return

[0231] Here, is the nth external solution, is a stopping criterion. At least some processes of the second algorithm can be repeatedly performed until a predefined condition is satisfied. The second algorithm can be performed by the JCAS device. In other words, the JCAS device uses the first algorithm and the second algorithm to find the optimal Find the value, Beams can be formed based on values.

[0232]

[0233] The above-described embodiments can be performed by various devices. According to one embodiment of the present disclosure, the above-described embodiments can be performed by a device including an antenna. The device can perform communication and sensing simultaneously. The device includes a device that forms a beam capable of performing communication and sensing simultaneously. The device includes various types of devices. For example, the device can be a base station or a terminal. For another example, the device can be a vehicle or an IoT device, etc. According to various embodiments of the present disclosure, a device that performs communication and sensing can be referred to as a JCAS device, a beam forming device, a communication device, a sensing device, a sensor, a smart device, a smart IoT, a smart appliance, or other terms having equivalent technical meanings thereto.

[0234]

[0235] FIG. 14 illustrates an example of a procedure for performing communication and sensing according to one embodiment of the present disclosure. FIG. 14 illustrates a method performed by a device performing sensing, and the operating subject is hereinafter referred to as a "device."

[0236] Referring to FIG. 14, in step S1401, the device performs a connection procedure. For example, the device may establish a connection with at least one of a base station and a terminal. The device may establish a connection with a communication device by performing an initial connection procedure. The initial connection procedure includes transmitting a synchronization signal and a random access procedure. The device may perform beam sweeping to transmit a synchronization signal to the communication device. The synchronization signal may include at least one of a primary synchronization signal (PSS), a secondary synchronization signal (SSS), a sidelink primary synchronization signal (SPSS), a sidelink secondary synchronization signal (SSSS), a physical broadcast channel (PBCH), and a physical sidelink broadcast channel (PSBCH). The device may perform a random access procedure based on the synchronization signal. The device may complete the initial connection by performing the random access procedure.

[0237] In step S1403, the device acquires channel state information. To acquire the channel state information, the device transmits a reference signal. Here, the reference signal refers to a signal for channel measurement. For example, the reference signal may include at least one of a CSI-RS, a DMRS, and an SRS. The device acquires the channel state information as a response to the reference signal from a communication device that has received the reference signal. The channel state information may be received from the communication device via a measurement report message.

[0238] In step S1405, the device acquires initial information. The initial information may include, for example, at least one of the distance, angle, RCS, velocity, and intensity of the echo signal of the target and the clutter. The device may acquire the initial information based on the received echo signal. Here, the echo signal refers to a signal in which a transmitted beam is reflected from at least one of the target and the clutter. In other words, the echo signal may be a signal received in response to the transmitted beam. For example, the echo signal may be a signal in which the transmitted beam is reflected in step S1401.

[0239] In step S1407, the device forms a beam based on information for beamforming. Here, the information for beamforming includes at least one of initial information and channel state information. For example, the information for beamforming may include initial information and channel state information acquired in steps S1403 and S1405. The device may determine a metric based on the information for beamforming. For example, the device determines the metric based on the initial information and the measurement report. The metric may be a metric determined from among a plurality of metrics. For example, the metric may be a metric determined from among a first metric and a second metric. According to various embodiments, the device may generate a beamforming problem based on the determined metric and the information for beamforming. The device may find a solution to the beamforming problem based on at least one algorithm. The at least one algorithm may include a first algorithm and a second algorithm. The device may form a beam using the solution to the beamforming problem.

[0240] In step S1409, the device performs communication and sensing using the formed beam. The device can transmit the formed beam. The device can transmit data to a connected communication device using the beam. For example, the device can transmit data by transmitting a communication symbol through the formed beam. The device can sense a target and clutter using the beam. For example, the device can sense the target and clutter by receiving an echo signal reflected from the target and clutter by the transmitted beam.

[0241]

[0242] FIG. 15 illustrates an example of a beam forming procedure according to one embodiment of the present disclosure. FIG. 15 illustrates a method performed by a device for forming a beam based on an algorithm, and the operating subject is hereinafter referred to as a "device."

[0243] Referring to FIG. 15, in step S1501, the device determines a metric based on the beam sweeping result and determines a beam problem. The device can obtain initial information using beam sweeping. The device can determine one of a plurality of metrics as a metric for beam forming based on the initial information. For example, the device can determine one metric from among a first metric and a second metric. The first metric includes a metric using Sum rate and MSE (mean square error). For example, the first metric can include a metric using Sum rate for beam forming for communication and using MSE for a transmit beam pattern for beam forming for sensing. The second metric includes a metric using Sum rate and SCNR (signal to clitter plus noise ratio). For example, the second metric can include a metric using Sum rate for beam forming for communication and using SCNR for beam forming for sensing. The device can determine a beam problem based on the determined metrics. The beam problem includes a problem for determining a beam forming matrix for forming a beam.

[0244] In step S1503, the device obtains a first solution using a first algorithm. The first solution refers to the final inner solution obtained using the first algorithm. The first algorithm includes an algorithm for optimizing the first solution based on fixed Lagrange multipliers. The device obtains the first solution based on the determined metric. For example, if the first metric is determined, the first algorithm may include matrices that satisfy the KKT condition based on the first metric. As another example, if the second metric is determined, the first algorithm may include matrices that satisfy the KKT condition based on the second metric.

[0245] In step S1505, the device obtains a second solution using the second algorithm and the first solution. The second solution refers to the final external solution obtained using the second algorithm. The device includes an algorithm that optimizes Lagrange multipliers for the first solution, which is a solution of the first algorithm. The second algorithm obtains the second solution based on the determined metric. For example, if the first metric is determined, the second algorithm may include constraints for the beamforming problem based on the first metric. In another example, if the second metric is determined, the second algorithm may include constraints for the beamforming problem based on the second metric.

[0246] In step S1507, the device may form a beam using the second solution. For example, the device may form a beam including communication symbols for communication and sensing symbols for sensing. For example, the device scrambles data. The device channel-codes the scrambled data to generate a plurality of codewords. The device modulates the generated plurality of codewords to obtain modulation symbols. The device generates communication symbols using the modulation symbols. The device may transmit the communication symbols and sensing symbols using the beam formed using the second solution.

[0247]

[0248] FIG. 16 illustrates an example of a procedure for calculating a beamforming matrix based on a first algorithm according to one embodiment of the present disclosure. FIG. 16 illustrates a method performed by a device for calculating a solution to the first algorithm, and the operating entity is hereinafter referred to as a "device."

[0249] Referring to FIG. 16, in step S1601, the device determines an initial value. For example, the initial value may be a randomly selected vector. As another example, the initial value may be a predefined vector. As another example, the initial value may be a vector selected from among predefined vectors. The method for determining the initial value is not limited to the example. The device determines the repetition count as 1. The repetition count refers to the number of times steps S1603 to S1609 are repeated.

[0250] In step S1603, the device calculates the first matrix and the second matrix using the internal solution obtained immediately before. If step S1603 is performed for the first time, the device calculates the first matrix and the second matrix using the initial value as the internal solution. For example, the first matrix and the second matrix may be matrices that satisfy the KKT condition. The first matrix and the second matrix may be matrices according to a determined metric. For example, the first matrix and the second matrix may be matrices that satisfy the KKT condition according to the first metric. Alternatively, the first matrix and the second matrix may be matrices that satisfy the KKT condition according to the second metric.

[0251] In step S1605, the device obtains an inner solution using the first and second matrices. The inner solution includes fixed Lagrange multipliers. In other words, the calculated inner solution may vary depending on the variation of the Lagrange multipliers. Upon obtaining the inner solution, the device increases the iteration count by 1.

[0252] In step S1607, the device determines whether the stopping condition is satisfied. If the stopping condition is not satisfied, the device repeats steps S1603 to S1605. If the stopping condition is satisfied, the device performs step S1609. In other words, the device can terminate the first algorithm if the stopping condition is satisfied. For example, the device can perform step S1609 if the difference between the internal solution obtained immediately before and the internal solution obtained before that is less than or equal to the stopping criterion. In other words, if the difference between the internal solution obtained as a result of the Nth iteration and the internal solution obtained as a result of the (N-1)th iteration is less than or equal to the stopping criterion, the device performs step S1609. As another example, the device can perform step S1609 if the iteration count is equal to the maximum value of the iteration count.

[0253] At step S1609, the device determines the obtained inner solution as the first solution. Here, the obtained inner solution may be an inner solution of an iteration count that satisfies a stopping condition. For example, if the Nth obtained inner solution satisfies the stopping condition, the device determines the Nth obtained inner solution as the first solution. The first solution includes a fixed Lagrange multiplier. In other words, the first solution may be a vector that varies according to the variation of the Lagrange multiplier. The device may perform the second algorithm using the determined first solution.

[0254]

[0255] FIG. 17 illustrates an example of a procedure for calculating a beamforming matrix based on a second algorithm according to one embodiment of the present disclosure. FIG. 17 illustrates a method performed by a device for calculating a solution to the second algorithm, and the operating entity is hereinafter referred to as a "device."

[0256] Referring to FIG. 17, in step S1701, the device determines an initial value. Here, the initial value means an initial value of a Lagrange multiplier. For example, the initial value may be a randomly selected arbitrary value. In another example, the initial value may be a predefined value. In another example, the initial value may be a value selected from among predefined values. The method for determining the initial value is not limited to the example. The device determines an iteration count as 1. The iteration count means the number of times steps S1703 to S1707 are repeated.

[0257] In step S1703, the device determines whether a condition for determining an external solution is satisfied using the first solution and the external solution. Here, the external solution may refer to a Lagrangian constant previously obtained using the second algorithm. When the second algorithm is performed for the first time, the external solution may be an initial value determined in step S1701. The condition for determining an external solution may include, for example, a constraint condition of a beam forming problem. For example, the device substitutes the external solution into the first solution and determines whether the condition is satisfied based on the first solution. The constraint condition may include a constraint condition according to a metric determined for beam forming. In other words, the constraint condition includes at least one of a constraint condition of a beam problem according to the first metric and a constraint condition of a beam problem according to the second metric.

[0258] In step S1705, the device determines an external solution based on whether the conditions for determining an external solution are satisfied. If the first solution does not satisfy the constraints of the beamforming problem, the device determines the average of the previously determined external solution and the maximum value of the Lagrange constant as the external solution. If the first solution satisfies the constraints of the beamforming problem, the device determines the average of the previously determined external solution and the minimum value of the Lagrange constant as the external solution. After determining the external solution, the device increases the iteration count by 1.

[0259] At step S1707, the device determines whether a stopping condition is satisfied. For example, the device may determine that the stopping condition is satisfied if the difference between the determined external solution and the previously determined external solution is less than or equal to a stopping criterion. As another example, the device may determine that the stopping condition is satisfied if the iteration count is greater than the maximum value of the iteration count. If the stopping condition is satisfied, the device performs step S1709. If the stopping condition is not satisfied, the device repeats steps S1703 and S1705.

[0260] In step S1709, the device determines the first solution by substituting the determined external solution as the second solution. The device can form a beam using the second solution. For example, the device can form a beamforming matrix using the second solution. The device can form a beam using the beamforming matrix.

[0261]

[0262] FIG. 18 illustrates an example of a procedure for performing communication and sensing using JCAS according to one embodiment of the present disclosure. In the embodiment of FIG. 18, the base station (1820) may be a device. Although the base station (1820) is depicted as a device in the embodiment of FIG. 18, the device may be a terminal.

[0263] Referring to FIG. 18, in step S1801, the base station (1820) transmits a synchronization signal using beam sweeping. For example, the base station (1820) may transmit a beam including the synchronization signal. The base station (1820) transmits the synchronization signal to at least one of the terminal (1810) and the target (1830). Although FIG. 18 illustrates one terminal (1810) and one target (1830), the base station (1820) may transmit the synchronization signal to at least one terminal and one target.

[0264] In step S1803, the base station (1820) receives an echo signal for the transmitted synchronization signal. For example, the base station (1820) may receive an echo signal that is a reflection of the transmitted synchronization signal from the target (1830) and returns. Although not illustrated in FIG. 18, the base station (1820) may also receive echo signals reflected from clutter and terminals (1810) other than the target (1830).

[0265] In step S1805, the base station (1820) transmits system information to the terminal (1810). The system information may include, for example, at least one of a MIB and a SIB. The base station (1820) may transmit the system information to the terminal (1810) for initial connection. The base station (1820) and the terminal (1810) may establish a connection using the system information.

[0266] In step S1807, the base station (1820) acquires initial information and determines a metric. The base station (1820) may acquire initial information from an echo signal. For example, the base station (1820) may determine a metric based on the strength of the echo signal. The base station (1820) may determine a metric among the first metric and the second metric based on the strength of the echo signal.

[0267] In step S1809, the base station (1820) may transmit pilot symbols for channel estimation to the terminal (1810). The pilot symbols for channel estimation may include reference signals. The base station (1820) may transmit information indicating a measurement report to the terminal (1810).

[0268] In step S1811, the base station (1820) receives a channel estimation pilot symbol or CSI report from the terminal (1810). The channel estimation pilot symbol or CSI report may be transmitted from the terminal (1810) as a response to the pilot symbol transmitted by the base station (1820). The CSI report may be expressed as a measurement report.

[0269] In step S1813, the base station (1820) forms a beam based on the determined metric. The base station (1820) forms the beam using the determined metric and at least one of the channel estimation pilot symbol and CSI report received from the terminal (1810). For example, the base station (1820) may form the beam using the first algorithm and the second algorithm.

[0270] In step S1815, the base station (1820) transmits a precoded JCAS signal using the formed beam. The base station (1820) can transmit the precoded JCAS signal to the terminal (1810) and the target (1830). For example, the base station (1820) can transmit the precoded JCAS signal to at least one terminal and the target. Here, the base station (1820) can transmit the precoded JCAS signal using a beam formed differently for each of at least one terminal.

[0271] In step S1817, the base station (1820) receives an echo signal. The echo signal may be a signal reflected from at least one of the target (1830) and clutter, including a beam containing a precoded JCAS signal.

[0272]

[0273] Figure 19a shows the JCAS beam forming result when there is one target.

[0274] Figure 19b shows the JCAS beam forming results when there are three targets.

[0275] Figures 19a and 19b illustrate the results of JCAS beamforming in the absence of clutter. Referring to Figures 19a and 19b, when there is only one target, there is little difference between the beams based on the first metric and the beams based on the second metric. When there are three targets, the beams based on the first metric show more uniform signal strength across all targets than the beams based on the second metric. In other words, when there is no clutter, the beams based on the first metric show better sensing performance than the beams based on the second metric.

[0276] Figure 20a shows the JCAS beamforming result when there is one target and the echo signal of the clutter has a similar intensity to the echo signal of the target.

[0277] Figure 20b shows the JCAS beamforming results when there are three targets and the echo signal of the clutter has a similar intensity to the echo signal of the targets.

[0278] Referring to FIGS. 20A and 20B, when there is one target, the beam based on the second metric better eliminates the influence of clutter than the beam based on the first metric. When there are three targets, both the beam based on the first metric and the beam based on the second metric detect the target well, but the beam based on the second metric better eliminates the influence of clutter. In other words, using the beam based on the second metric can detect less clutter than using the beam based on the first metric.

[0279] The results in Figures 19 and 20 show that the second metric is more suitable for beamforming when the influence of clutter is strong. When the influence of clutter is small, the first metric is more suitable for beamforming.

[0280]

[0281] Below, examples of wireless device utilization to which various embodiments of the present disclosure are applied are described.

[0282] Figure 21 illustrates an example of a wireless device applicable to the present disclosure. The wireless device may be implemented in various forms depending on the use case / service (see Figure 1).

[0283] Referring to FIG. 21, the wireless device (200) corresponds to the wireless device (200) of FIG. 2 and may be composed of various elements, components, units / units, and / or modules. For example, the wireless device (200) may include a communication unit (210), a control unit (220), a memory unit (230), and additional elements (240). The communication unit may include a communication circuit (212) and a transceiver(s) (214). For example, the communication circuit (212) may include one or more processors (202) and / or one or more memories (204) of FIG. 2. For example, the transceiver(s) (214) may include one or more transceivers (206) and / or one or more antennas (208) of FIG. 2. The control unit (220) is electrically connected to the communication unit (210), the memory unit (230), and the additional elements (240) and controls the overall operations of the wireless device. For example, the control unit (220) can control the electrical / mechanical operations of the wireless device based on the program / code / command / information stored in the memory unit (230). In addition, the control unit (220) can transmit information stored in the memory unit (230) to an external device (e.g., another communication device) via a wireless / wired interface through the communication unit (210), or store information received from an external device (e.g., another communication device) via a wireless / wired interface in the memory unit (230).

[0284] The additional element (240) may be configured in various ways depending on the type of the wireless device. For example, the additional element (240) 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. 1, 100a), a vehicle (Fig. 1, 100b-1, 100b-2), an XR device (Fig. 1, 100c), a portable device (Fig. 1, 100d), a home appliance (Fig. 1, 100e), an IoT device (Fig. 1, 100f), a digital broadcasting terminal, a hologram device, a public safety device, an MTC device, a medical device, a fintech device (or a financial device), a security device, a climate / environmental device, an AI server / device (Fig. 1, 400), a base station (Fig. 1, 200), a network node, etc. Wireless devices may be mobile or stationary depending on the use / service.

[0285] In FIG. 21, various elements, components, units / parts, and / or modules within the wireless device (200) may be entirely interconnected via a wired interface, or at least some may be wirelessly connected via a communication unit (210). For example, within the wireless device (200), the control unit (220) and the communication unit (210) may be wired, and the control unit (220) and a first unit (e.g., 230, 240) may be wirelessly connected via the communication unit (210). In addition, each element, component, unit / part, and / or module within the wireless device (200) may further include one or more elements. For example, the control unit (220) may be composed of a set of one or more processors. For example, the control unit (220) 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 RAM (Random Access Memory), DRAM (Dynamic RAM), ROM (Read Only Memory), flash memory, volatile memory, non-volatile memory, and / or a combination thereof.

[0286] Below, the implementation example of Fig. 21 is described in more detail with reference to the drawings.

[0287] Figure 22 illustrates examples of portable devices applicable to the present disclosure. Portable devices may include smartphones, smart pads, wearable devices (e.g., smartwatches, smartglasses), and portable computers (e.g., laptops, etc.). Portable devices may also be referred to as mobile stations (MS), user terminals (UT), mobile subscriber stations (MSS), subscriber stations (SS), advanced mobile stations (AMS), or wireless terminals (WT).

[0288] Referring to FIG. 22, the portable device (200) may include an antenna unit (208), a communication unit (210), a control unit (220), a memory unit (230), a power supply unit (240a), an interface unit (240b), and an input / output unit (240c). The antenna unit (208) may be configured as a part of the communication unit (210). Blocks 210 to 230 / 240a to 240c of FIG. 22 correspond to blocks 210 to 230 / 240 of FIG. 21, respectively.

[0289] The communication unit (210) can transmit and receive signals (e.g., data, control signals, etc.) with other wireless devices and base stations. The control unit (220) can control components of the mobile device (200) to perform various operations. The control unit (220) can include an AP (Application Processor). The memory unit (230) can store data / parameters / programs / codes / commands required for operating the mobile device (200). In addition, the memory unit (230) can store input / output data / information, etc. The power supply unit (240a) supplies power to the mobile device (200) and can include a wired / wireless charging circuit, a battery, etc. The interface unit (240b) can support connection between the mobile device (200) and other external devices. The interface unit (240b) can include various ports (e.g., audio input / output ports, video input / output ports) for connection with external devices. The input / output unit (240c) can input or output video information / signals, audio information / signals, data, and / or information input from a user. The input / output unit (240c) may include a camera, a microphone, a user input unit, a display unit (240d), a speaker, and / or a haptic module.

[0290] For example, in the case of data communication, the input / output unit (240c) 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 (230). The communication unit (210) 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 (210) 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 (230) and then output in various forms (e.g., text, voice, image, video, haptic) through the input / output unit (240c).

[0291] Figure 23 illustrates examples of vehicles or autonomous vehicles applicable to the present disclosure. The vehicles or autonomous vehicles may be implemented as mobile robots, cars, trains, manned / unmanned aerial vehicles (AVs), ships, etc.

[0292] Referring to FIG. 23, a vehicle or autonomous vehicle (200-1) may include an antenna unit (208-1), a communication unit (210-1), a control unit (220-1), a driving unit (240a-1), a power supply unit (240b-1), a sensor unit (240c-1), and an autonomous driving unit (240d-1). The antenna unit (208-1) may be configured as a part of the communication unit (210-1). Blocks 210-1 / 230-1 / 240a-1 to 240d-1 of FIG. 23 correspond to blocks 210 / 230 / 240 of FIG. 21, respectively.

[0293] The communication unit (210-1) can transmit and receive signals (e.g., data, control signals, etc.) with external devices such as other vehicles, base stations (e.g., base stations, roadside base stations (ROS), etc.), and servers. The control unit (220-1) can control elements of the vehicle or autonomous vehicle (200-1) to perform various operations. The control unit (220-1) may include an ECU (Electronic Control Unit). The drive unit (240a-1) can drive the vehicle or autonomous vehicle (200-1) on the ground. The drive unit (240a-1) may include an engine, a motor, a power train, wheels, brakes, a steering device, etc. The power supply unit (240b-1) supplies power to the vehicle or autonomous vehicle (200-1) and may include a wired / wireless charging circuit, a battery, etc. The sensor unit (240c-1) can obtain vehicle status, surrounding environment information, user information, etc. The sensor unit (240c-1) 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 (240d-1) 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.

[0294] For example, the communication unit (210-1) can receive map data, traffic information data, etc. from an external server. The autonomous driving unit (240d-1) can generate an autonomous driving route and driving plan based on the acquired data. The control unit (220-1) can control the drive unit (240a-1) so that the vehicle or autonomous vehicle (200-1) moves along the autonomous driving route according to the driving plan (e.g., speed / direction control). During autonomous driving, the communication unit (210-1) 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 (240c-1) can acquire vehicle status and surrounding environment information. The autonomous driving unit (240d-1) can update the autonomous driving route and driving plan based on newly acquired data / information. The communication unit (210-1) can transmit information regarding the vehicle location, autonomous driving route, driving plan, etc. to an external server. The external server can predict traffic information data in advance using AI technology, etc. based on information collected from the vehicle or autonomous vehicles, and provide the predicted traffic information data to the vehicle or autonomous vehicles. If the device (220-2) is an autonomous vehicle, it can perform the same procedure as the vehicle or autonomous vehicle (200-1). In addition, if the device (220-2) is a base station or a roadside base station, the device (220-2) can transmit data, control signals, etc. to the vehicle or autonomous vehicle (200-1) through the communication unit (210-2).

[0295] Figure 24 illustrates an example of a vehicle applicable to the present disclosure. The vehicle may also be implemented as a means of transportation, a train, an aircraft, a ship, etc. Referring to Figure 24, the vehicle (200) may include a communication unit (210), a control unit (220), a memory unit (230), an input / output unit (240a), and a position measurement unit (240b). Here, blocks 210 to 230 / 240a to 240b correspond to blocks 210 to 230 / 240 of Figure 21, respectively.

[0296] The communication unit (210) can transmit and receive signals (e.g., data, control signals, etc.) with other vehicles or external devices such as base stations. The control unit (220) can control components of the vehicle (200) to perform various operations. The memory unit (230) can store data / parameters / programs / codes / commands that support various functions of the vehicle (100). The input / output unit (240a) can output AR / VR objects based on information in the memory unit (230). The input / output unit (240a) can include a HUD. The position measurement unit (240b) can obtain position information of the vehicle (200). The position information can include absolute position information of the vehicle (200), position information within a driving line, acceleration information, position information with respect to surrounding vehicles, etc. The position measurement unit (240b) can include GPS and various sensors.

[0297] For example, the communication unit (210) of the vehicle (200) can receive map information, traffic information, etc. from an external server and store them in the memory unit (230). The location measurement unit (240b) can obtain vehicle location information through GPS and various sensors and store the information in the memory unit (230). The control unit (220) can create a virtual object based on the map information, traffic information, and vehicle location information, and the input / output unit (240a) can display the created virtual object on the vehicle window (240a-1, 240a-2). In addition, the control unit (220) can determine whether the vehicle (200) is being driven normally within the driving line based on the vehicle location information. If the vehicle (200) abnormally deviates from the driving line, the control unit (220) can display a warning on the vehicle window through the input / output unit (240a). Additionally, the control unit (220) can broadcast a warning message regarding driving abnormalities to surrounding vehicles through the communication unit (210). Depending on the situation, the control unit (220) can transmit vehicle location information and information regarding driving / vehicle abnormalities to relevant authorities through the communication unit (210).

[0298] Figure 25 illustrates examples of XR devices applicable to the present disclosure. The XR devices may be implemented as HMDs, head-up displays (HUDs) installed in vehicles, televisions, smartphones, computers, wearable devices, home appliances, digital signage, vehicles, robots, and the like.

[0299] Referring to FIG. 25, the XR device (200a) may include a communication unit (210), a control unit (220), a memory unit (230), an input / output unit (240a), a sensor unit (240b), and a power supply unit (240c). Here, blocks 210 to 230 / 240a to 240c of FIG. 25 correspond to blocks 210 to 230 / 240 of FIG. 21, respectively.

[0300] The communication unit (210) can transmit and receive signals (e.g., media data, control signals, etc.) with external devices such as other wireless devices, portable devices, or media servers. The media data can include videos, images, sounds, etc. The control unit (220) can control components of the XR device (200a) to perform various operations. For example, the control unit (220) can be configured to control and / or perform procedures such as video / image acquisition, (video / image) encoding, metadata generation and processing, etc. The memory unit (230) can store data / parameters / programs / codes / commands required for driving the XR device (200a) / generating XR objects. The input / output unit (240a) can obtain control information, data, etc. from the outside, and output the generated XR object. The input / output unit (240a) can include a camera, a microphone, a user input unit, a display unit, a speaker, and / or a haptic module. The sensor unit (240b) can obtain the XR device status, surrounding environment information, user information, etc. The sensor unit (240b) may include a proximity sensor, an illuminance sensor, an acceleration sensor, a magnetic sensor, a gyro sensor, an inertial sensor, an RGB sensor, an IR sensor, a fingerprint recognition sensor, an ultrasonic sensor, a light sensor, a microphone, and / or a radar. The power supply unit (240c) supplies power to the XR device (200a) and may include a wired / wireless charging circuit, a battery, etc.

[0301] For example, the memory unit (230) of the XR device (200a) may include information (e.g., data, etc.) required for creating an XR object (e.g., AR / VR / MR object). The input / output unit (240a) may obtain a command to operate the XR device (200a) from the user, and the control unit (220) may operate the XR device (200a) according to the user's operating command. For example, when the user attempts to watch a movie, news, etc. through the XR device (200a), the control unit (220) may transmit content request information to another device (e.g., a mobile device (200b)) or a media server through the communication unit (230). The communication unit (230) may download / stream content such as movies and news from another device (e.g., a mobile device (200b)) or a media server to the memory unit (230). The control unit (220) controls and / or performs procedures such as video / image acquisition, (video / image) encoding, and metadata generation / processing for content, and can generate / output an XR object based on information about surrounding space or real objects acquired through the input / output unit (240a) / sensor unit (240b).

[0302] In addition, the XR device (200a) is wirelessly connected to the mobile device (200b) through the communication unit (210), and the operation of the XR device (200a) can be controlled by the mobile device (200b). For example, the mobile device (200b) can act as a controller for the XR device (200a). To this end, the XR device (200a) can obtain 3D location information of the mobile device (200b), and then generate and output an XR object corresponding to the mobile device (200b).

[0303] Figure 26 illustrates examples of robots applicable to the present disclosure. Robots can be classified into industrial, medical, household, and military types, depending on their intended use or field.

[0304] Referring to FIG. 26, the robot (200) may include a communication unit (210), a control unit (220), a memory unit (230), an input / output unit (240a), a sensor unit (240b), and a driving unit (240c). Here, blocks 210 to 230 / 240a to 240c of FIG. 26 correspond to blocks 210 to 230 / 240 of FIG. 21, respectively.

[0305] The communication unit (210) can transmit and receive signals (e.g., driving information, control signals, etc.) with external devices such as other wireless devices, other robots, or control servers. The control unit (220) can control components of the robot (200) to perform various operations. The memory unit (230) can store data / parameters / programs / codes / commands that support various functions of the robot (200). The input / output unit (240a) can obtain information from the outside of the robot (200) and output information to the outside of the robot (200). The input / output unit (240a) can include a camera, a microphone, a user input unit, a display unit, a speaker, and / or a haptic module. The sensor unit (240b) can obtain internal information of the robot (200), surrounding environment information, user information, etc. The sensor unit (240b) may include a proximity sensor, an illuminance sensor, an acceleration sensor, a magnetic sensor, a gyro sensor, an inertial sensor, an IR sensor, a fingerprint recognition sensor, an ultrasonic sensor, a light sensor, a microphone, a radar, etc. The driving unit (240c) may perform various physical operations, such as moving the robot joints. In addition, the driving unit (240c) may enable the robot (200) to drive on the ground or fly in the air. The driving unit (240c) may include an actuator, a motor, wheels, brakes, propellers, etc.

[0306] Figure 27 illustrates an example of an AI device applicable to the present disclosure.

[0307] AI devices can be implemented as fixed or mobile devices, such as TVs, projectors, smartphones, PCs, laptops, digital broadcasting terminals, tablet PCs, wearable devices, set-top boxes (STBs), radios, washing machines, refrigerators, digital signage, robots, and vehicles.

[0308] Referring to FIG. 27, the AI ​​device (200) may include a communication unit (210), a control unit (220), a memory unit (230), an input / output unit (240a / 240b), a learning processor unit (240c), and a sensor unit (240d). Blocks 210 to 230 / 240a to 240d of FIG. 27 correspond to blocks 210 to 230 / 140 of FIG. 21, respectively.

[0309] The communication unit (210) can transmit and receive wired and wireless signals (e.g., sensor information, user input, learning models, control signals, etc.) with external devices such as other AI devices (e.g., 100a to 100f, 120 of FIG. 1) or AI servers (e.g., 100g of FIG. 1) using wired and wireless communication technology. To this end, the communication unit (210) can transmit information within the memory unit (230) to the external device or transfer a signal received from the external device to the memory unit (230).

[0310] The control unit (220) may determine at least one executable operation of the AI ​​device (200) based on information determined or generated using a data analysis algorithm or a machine learning algorithm. In addition, the control unit (220) may control components of the AI ​​device (200) to perform the determined operation. For example, the control unit (220) may request, search, receive, or utilize data from the learning processor unit (240c) or the memory unit (230), and may control components of the AI ​​device (200) to perform at least one executable operation, a predicted operation, or an operation determined to be desirable. In addition, the control unit (220) may collect history information including the operation contents of the AI ​​device (200) or user feedback on the operation, and store the collected history information in the memory unit (230) or the learning processor unit (240c), or transmit the collected history information to an external device such as an AI server (FIG. 1, 100g). The collected history information may be used to update a learning model.

[0311] The memory unit (230) can store data that supports various functions of the AI ​​device (200). For example, the memory unit (230) can store data obtained from the input unit (240a), data obtained from the communication unit (210), output data of the learning processor unit (240c), and data obtained from the sensing unit (140). In addition, the memory unit (230) can store control information and / or software codes necessary for the operation / execution of the control unit (220).

[0312] The input unit (240a) can obtain various types of data from the outside of the AI ​​device (200). For example, the input unit (220) can obtain learning data for model learning, input data to which the learning model will be applied, etc. The input unit (240a) may include a camera, a microphone, and / or a user input unit. The output unit (240b) may generate output related to vision, hearing, or touch. The output unit (240b) may include a display unit, a speaker, and / or a haptic module, etc. The sensing unit (140d) can obtain at least one of internal information of the AI ​​device (200), information about the surrounding environment of the AI ​​device (200), and user information using various sensors. The sensing unit (140d) may include a proximity sensor, an illuminance sensor, an acceleration sensor, a magnetic sensor, a gyro sensor, an inertial sensor, an RGB sensor, an IR sensor, a fingerprint recognition sensor, an ultrasonic sensor, a light sensor, a microphone, and / or a radar, etc.

[0313] The learning processor unit (240c) can train a model composed of an artificial neural network using learning data. The learning processor unit (240c) can perform AI processing together with the learning processor unit of the AI ​​server (Fig. 1, 100g). The learning processor unit (240c) can process information received from an external device via the communication unit (210) and / or information stored in the memory unit (230). In addition, the output value of the learning processor unit (240c) can be transmitted to an external device via the communication unit (210) and / or stored in the memory unit (230).

[0314] The proposed methods described above can be implemented independently, but they can also be implemented as a combination (or merge) of some of the proposed methods. Rules can be defined so that the base station notifies the terminal of the applicability of the proposed methods (or information about the rules of the proposed methods) through a predefined signal (e.g., a physical layer signal or a higher layer signal).

[0315] The present disclosure may be embodied in other specific forms without departing from the technical ideas and essential features described herein. Therefore, the above detailed description should not be construed as limiting in all respects but rather as illustrative. The scope of the present disclosure should be determined by a reasonable interpretation of the appended claims, and all modifications within the equivalent scope of the present disclosure are intended to be included within the scope of the present disclosure. Furthermore, claims that are not explicitly cited in the claims may be combined to form an embodiment or incorporated into a new claim through a post-filing amendment.

[0316] Embodiments of the present disclosure can be applied to various wireless access systems. Examples of various wireless access systems include the 3rd Generation Partnership Project (3GPP) or 3GPP2 systems.

[0317] The embodiments of the present disclosure can be applied not only to the various wireless access systems described above, but also to all technical fields that utilize these various wireless access systems. Furthermore, the proposed method can also be applied to mmWave and THz communication systems utilizing ultra-high frequency bands.

[0318] Additionally, embodiments of the present disclosure can be applied to various applications such as autonomous vehicles and drones.

Claims

1. In a method performed by a UE (user equipment) in a wireless communication system, A step of transmitting beams containing synchronization signals in different directions; A step of performing synchronization based on the above synchronization signal; Step of receiving channel status information; A step of acquiring initial information based on the above beam; A step of determining a metric for beam forming based on the above initial information; A step of obtaining a solution to a beam forming problem based on the metric using at least one algorithm; and A method comprising the step of forming a beam based on the above-mentioned sea and the channel state information.

2. In paragraph 1, A method for forming the beam, wherein the metrics include a first metric based on MSE and a second metric based on SCNR.

3. In paragraph 1, A method wherein said at least one algorithm comprises an algorithm for optimizing a beamforming matrix that satisfies constraints of said beamforming problem.

4. In paragraph 3, At least one of the above algorithms, A method comprising a first algorithm for calculating a first solution, which is an optimized vector among vectors satisfying the KKT condition according to the above metric, and a second algorithm for optimizing a Lagrangian multiplier of the first solution within a range satisfying the above constraint condition.

5. In paragraph 4, The above first algorithm is, A step of determining the initial value of the above vector; A step of calculating an inner solution using a matrix satisfying the above KKT condition; A step of determining whether the above internal solution satisfies the stopping condition; A method comprising a step of determining an internal solution satisfying the above stopping condition as the first solution.

6. In paragraph 4, The second algorithm above is, A step of initializing the above Lagrange multipliers; A step of substituting the Lagrange multipliers into the first solution; A step of determining whether the first solution into which the Lagrange multiplier is substituted satisfies the constraint; A step of determining a new Lagrange multiplier depending on whether the above constraints are satisfied; a step of determining whether the new Lagrange multiplier satisfies the stationary condition; and Including a step of outputting the first solution with the new Lagrange multiplier substituted as the second solution, The above second solution is a solution to the beam forming problem.

7. In paragraph 1, The above initial information includes the intensity of the echo signal of the target and the intensity of the echo signal of the clutter, The steps for determining the above metrics are: A method comprising the step of determining the metric based on the intensity of the echo signal of the target and the intensity of the echo signal of the clutter.

8. In a method performed by a base station in a wireless communication system, A step of transmitting beams containing synchronization signals in different directions; A step of performing synchronization based on the above synchronization signal; Step of receiving channel status information; A step of acquiring initial information based on the above beam; A step of determining a metric for beam forming based on the above initial information; A step of obtaining a solution to a beam forming problem based on the metric using at least one algorithm; and A method comprising the step of forming a beam based on the above-mentioned sea and the channel state information.

9. In paragraph 8, A method for forming the beam, wherein the metrics include a first metric based on MSE and a second metric based on SCNR.

10. In paragraph 8, A method wherein said at least one algorithm comprises an algorithm for optimizing a beamforming matrix that satisfies constraints of said beamforming problem.

11. In paragraph 10, At least one of the above algorithms, A method comprising a first algorithm for calculating a first solution, which is an optimized vector among vectors satisfying the KKT condition according to the above metric, and a second algorithm for optimizing a Lagrangian multiplier of the first solution within a range satisfying the above constraint.

12. In paragraph 11, The above first algorithm is, A step of determining the initial value of the above vector; A step of calculating an inner solution using a matrix satisfying the above KKT condition; A step of determining whether the above internal solution satisfies the stopping condition; A method comprising a step of determining an internal solution satisfying the above stopping condition as the first solution.

13. In paragraph 11, The second algorithm above is, A step of initializing the above Lagrange multipliers; A step of substituting the Lagrange multipliers into the first solution; A step of determining whether the first solution into which the Lagrange multiplier is substituted satisfies the constraint; A step of determining a new Lagrange multiplier depending on whether the above constraints are satisfied; a step of determining whether the new Lagrange multiplier satisfies the stationary condition; and Including a step of outputting the first solution with the new Lagrange multiplier substituted as the second solution, The above second solution is a solution to the beam forming problem.

14. In paragraph 8, The above initial information includes the intensity of the echo signal of the target and the intensity of the echo signal of the clutter, The steps for determining the above metrics are: A method comprising the step of determining the metric based on the intensity of the echo signal of the target and the intensity of the echo signal of the clutter.

15. In a wireless communication system, in UE (user equipment), Transmitter and receiver; and A processor connected to the above transmitter and receiver is included, The above processor, Transmit beams containing synchronization signals in different directions, Synchronization is performed based on the above synchronization signal, Receive channel status information, Obtain initial information based on the above beam, Based on the above initial information, determine a metric for beam forming, Find a solution to the beam forming problem based on the above metric using at least one algorithm, A UE configured to form a beam based on the above-mentioned sea and the above-mentioned channel state information.

16. In a wireless communication system, at a base station, Transmitter and receiver; and A processor connected to the above transmitter and receiver is included, The above processor, Transmit beams containing synchronization signals in different directions, Synchronization is performed based on the above synchronization signal, Receive channel status information, Obtain initial information based on the above beam, Based on the above initial information, determine a metric for beam forming, Find a solution to the beam forming problem based on the above metric using at least one algorithm, A base station configured to form a beam based on the above-mentioned sea and the above-mentioned channel state information.

17. In communication devices, At least one processor; At least one computer memory connected to said at least one processor and storing instructions that direct operations when executed by said at least one processor, The above actions are, A step of transmitting beams containing synchronization signals in different directions; A step of performing synchronization based on the above synchronization signal; Step of receiving channel status information; A step of acquiring initial information based on the above beam; A step of determining a metric for beam forming based on the above initial information; A step of obtaining a solution to a beam forming problem based on the metric using at least one algorithm; and A communication device comprising a step of forming a beam based on the above-mentioned sea and the channel state information.

18. In a non-transitory computer-readable medium storing at least one instruction, comprising at least one instruction executable by the processor, At least one of the above commands causes the device to: Transmit beams containing synchronization signals in different directions, Synchronization is performed based on the above synchronization signal, Receive channel status information, Obtain initial information based on the above beam, Based on the above initial information, determine a metric for beam forming, Find a solution to the beam forming problem based on the above metric using at least one algorithm, A computer-readable medium for instructing to form a beam based on the above-described sea and channel state information.

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