Method and apparatus for generating multiple beams in wireless communication system

Abstract

A method performed by a first node in a wireless communication system according to an embodiment of the present disclosure may comprise the steps of: generating weights for Tx multiple beams and weights for Tx sensing beams; performing channel estimation on the basis of the weights for the Tx multiple beams and the weights for the Tx sensing beams; performing an update of the weights for the Tx sensing beams; performing an update of the weights for the Tx multiple beams on the basis of the Tx sensing beams on which the update has been performed; and transmitting data to and receiving data from a second node on the basis of the TX multiple beams on which the update has been performed.

Description

Method and device for generating multi-beams in a wireless communication system

[0001] The present disclosure relates to a wireless communication system. Specifically, the present disclosure relates to a method and device for generating a multi-beam in a wireless communication system.

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

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

[0004] Meanwhile, in the Joint Communication and Sensing (JCAS) system, multi-beam generation is crucial for maximizing communication performance and radar sensing accuracy. Communication and sensing beams can be individually designed and combined to form a multi-beam. However, research on optimizing communication and sensing performance through multi-beam formation is still insufficient.

[0005] To solve the above-described problems, the present disclosure provides a multi-beam generation method and device for maximizing communication performance and radar sensing accuracy in a JCAS (Joint Communication and Sensing) system.

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

[0007] In a wireless communication system according to one embodiment of the present disclosure, a method of operating a first node may include the steps of generating weights for a Tx multi-beam and weights for a Tx sensing beam, performing channel estimation based on the weights for the Tx multi-beam and the weights for the Tx sensing beam, performing an update of the weights for the Tx sensing beam, performing an update of the weights for the Tx multi-beam based on the Tx sensing beam on which the update is performed, and transmitting and receiving data with a second node based on the Tx multi-beam on which the update is performed.

[0008] The step of performing the channel estimation based on the weights for the Tx multi-beam and the weights for the Tx sensing beam may include the step of generating a first vector based on the weights for the Tx multi-beam and the weights for the Tx sensing beam, and the step of estimating the channel based on the first vector.

[0009] The step of performing an update of a weight for the Tx sensing beam may further include the steps of: obtaining a weight for the Rx sensing beam; obtaining a left singular vector (LSV) and a right singular vector (RSV) of the weight for the Tx sensing beam; obtaining the LSV and the RSV of the weight for the Rx sensing beam; obtaining a singular value matrix of the weight for the Tx sensing beam based on the LSV and the RSV of the weight for the Tx sensing beam; obtaining a singular value matrix of the weight for the Rx sensing beam based on the LSV and the RSV of the weight for the Rx sensing beam; and performing an update for the Tx sensing beam based on the singular value matrix of the weight for the Tx sensing beam.

[0010] The step of updating the weights for the Tx multi-beam based on the Tx sensing beam on which the update is performed may include the step of combining the Tx communication beam associated with the Tx multi-beam with the Tx sensing beam on which the update is performed.

[0011] The step of combining the Tx communication beam associated with the Tx multi-beam with the Tx sensing beam on which the update is performed may be performed based on one of comm-centric coherent combining or soft combining methods.

[0012] In a wireless communication system according to one embodiment of the present disclosure, a first node may include a transceiver, a memory including at least one command, and at least one processor for executing the at least one command, wherein the at least one command may include: generating a weight for a Tx multi-beam and a weight for a Tx sensing beam; performing channel estimation based on the weight for the Tx multi-beam and the weight for the Tx sensing beam; performing an update of the weight for the Tx sensing beam; performing an update of the weight for the Tx multi-beam based on the Tx sensing beam on which the update is performed; and transmitting and receiving data with a second node based on the Tx multi-beam on which the update is performed.

[0013] The step of performing the channel estimation based on the weights for the Tx multi-beam and the weights for the Tx sensing beam may include the step of generating a first vector based on the weights for the Tx multi-beam and the weights for the Tx sensing beam, and the step of estimating the channel based on the first vector.

[0014] The step of performing an update of a weight for the Tx sensing beam may further include the steps of: obtaining a weight for the Rx sensing beam; obtaining a left singular vector (LSV) and a right singular vector (RSV) of the weight for the Tx sensing beam; obtaining the LSV and the RSV of the weight for the Rx sensing beam; obtaining a singular value matrix of the weight for the Tx sensing beam based on the LSV and the RSV of the weight for the Tx sensing beam; obtaining a singular value matrix of the weight for the Rx sensing beam based on the LSV and the RSV of the weight for the Rx sensing beam; and performing an update for the Tx sensing beam based on the singular value matrix of the weight for the Tx sensing beam.

[0015] The step of updating the weights for the Tx multi-beam based on the Tx sensing beam on which the update is performed may include the step of combining the Tx communication beam associated with the Tx multi-beam with the Tx sensing beam on which the update is performed.

[0016] The step of combining the Tx communication beam associated with the Tx multi-beam with the Tx sensing beam on which the update is performed may be performed based on one of comm-centric coherent combining or soft combining methods.

[0017] In one embodiment of the present disclosure, a device comprising one or more memories and one or more processors functionally connected to the one or more memories, wherein the one or more processors are operable to cause the device to perform the steps of: generating a weight for a Tx multi-beam and a weight for a Tx sensing beam; performing channel estimation based on the weight for the Tx multi-beam and the weight for the Tx sensing beam; performing an update of the weight for a Tx sensing beam; performing an update of the weight for the Tx multi-beam based on the Tx sensing beam on which the update was performed; and transmitting and receiving data with a second node based on the Tx multi-beam on which the update was performed.

[0018] One or more non-transitory computer-readable media storing one or more instructions according to one embodiment of the present disclosure, the instructions operable to perform the steps of: generating weights for Tx multi-beams and weights for Tx sensing beams; performing channel estimation based on the weights for Tx multi-beams and the weights for Tx sensing beams; performing an update of the weights for Tx sensing beams; performing an update of the weights for Tx multi-beams based on the Tx sensing beams on which the updates were performed; and transmitting and receiving data with a second node based on the Tx multi-beams on which the updates were performed.

[0019] According to the present disclosure, high-accuracy radar sensing information can be obtained in a beamspace MIMO-OFDM JCAS system.

[0020] The effects that can be obtained from the present disclosure are not limited to the effects mentioned above, and other effects not mentioned will be clearly understood by those skilled in the art to which the present invention pertains from the description below.

[0021] The accompanying drawings are intended to aid in understanding the present disclosure and, together with detailed descriptions, may provide embodiments of the present disclosure. However, the technical features of the present disclosure are not limited to specific drawings, and the features disclosed in each drawing may be combined with each other to form new embodiments. Reference numerals in each drawing may indicate structural elements.

[0022] Figure 1 is a drawing showing an example of a communication system applicable to this specification.

[0023] Figure 2 is a drawing showing an example of a wireless device applicable to this specification.

[0024] FIG. 3 is a diagram illustrating a method for processing a transmission signal applicable to the present specification.

[0025] FIG. 4 is a drawing showing another example of a wireless device applicable to this specification.

[0026] FIG. 5 is a drawing showing an example of a mobile device applicable to this specification.

[0027] Figure 6 is a diagram showing physical channels applicable to this specification and a signal transmission method using them.

[0028] Figure 7 is a diagram showing the structure of a wireless frame applicable to this specification.

[0029] Figure 8 is a drawing showing a slot structure applicable to this specification.

[0030] FIG. 9 is a diagram showing an example of a communication structure that can be provided in a 6G system applicable to this specification.

[0031] Figure 10 shows an example of a perceptron structure.

[0032] Figure 11 shows an example of a multilayer perceptron structure.

[0033] Figure 12 shows an example of a deep neural network.

[0034] Figure 13 shows an example of a convolutional neural network.

[0035] Figure 14 is a diagram showing an example of a filter operation in a convolutional neural network.

[0036] Figure 15 shows an example of a neural network structure in which a recurrent loop exists.

[0037] Figure 16 shows an example of the operating structure of a recurrent neural network.

[0038] Figure 17 is a diagram showing an electromagnetic spectrum applicable to this specification.

[0039] Fig. 18 is a drawing showing a THz communication method applicable to this specification.

[0040] FIG. 19 is a diagram illustrating a THz wireless communication transceiver applicable to the present specification.

[0041] Fig. 20 is a drawing showing a THz signal generation method applicable to the present specification.

[0042] Figure 21 is a drawing showing a wireless communication transceiver applicable to this specification.

[0043] Figure 22 is a drawing showing a transmitter structure applicable to this specification.

[0044] Fig. 23 is a drawing showing a modulator structure applicable to this specification.

[0045] FIG. 24 is a drawing illustrating an example of a device according to one embodiment of the present disclosure.

[0046] FIG. 25 is a diagram illustrating an example of a multi-beam configuration according to one embodiment of the present disclosure.

[0047] FIG. 26 is a diagram illustrating an example of a sensing beam pattern that minimizes a channel estimation error according to one embodiment of the present disclosure.

[0048] FIG. 27 is a diagram illustrating an example of a multi-beam scenario according to one embodiment of the present disclosure.

[0049] FIG. 28 is a drawing illustrating an example of a multi-beam generation method according to one embodiment of the present disclosure.

[0050] Figures 29 to 34 are drawings illustrating the effect of the method in one embodiment of the present disclosure.

[0051] Figure 35 is a flowchart of a signal transmission and reception method according to one embodiment of the present disclosure.

[0052] The following embodiments combine the components and features of this specification in a predetermined form. Each component or feature may be considered optional unless explicitly stated otherwise. Each component or feature may be implemented without being combined with other components or features. Furthermore, some components and / or features may be combined to form embodiments of this specification. The order of operations described in the embodiments of this specification may be changed. Some components or features of one embodiment may be included in another embodiment or may be replaced with corresponding components or features of another embodiment.

[0053] In the description of the drawings, procedures or steps that may obscure the gist of the present specification are not described, and procedures or steps that can be understood by a person skilled in the art are also not described.

[0054] Throughout the specification, when a part is said to "comprising" (or including) a certain component, this does not mean that other components are excluded, but rather that other components can be included, unless specifically stated otherwise. In addition, terms such as "...part," "...unit," and "module" described in the specification mean a unit that processes at least one function or operation, which may be implemented by hardware, software, or a combination of hardware and software. In addition, the words "a" or "an," "one," "the," and similar related words may be used in the context of describing this specification (especially in the context of the claims below) to include both the singular and the plural, unless otherwise indicated herein or clearly contradicted by context.

[0055] The embodiments of this specification have been described with a focus on the data transmission and reception relationship between a base station and a mobile station. Here, the base station is understood as a terminal node of a network that directly communicates with the mobile station. Certain operations described herein as being performed by the base station may, in some cases, be performed by an upper node of the base station.

[0056] 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.

[0057] Additionally, in the embodiments of the present specification, the term terminal may be replaced with terms such as user equipment (UE), mobile station (MS), subscriber station (SS), mobile subscriber station (MSS), mobile terminal, or advanced mobile station (AMS).

[0058] Additionally, the device (10) refers to a fixed and / or mobile node that provides data services or voice services, and the receiver refers to a fixed and / or mobile node that receives data services or voice services. Accordingly, in the case of the uplink, the mobile station may be the device (10) and the base station may be the receiver. Similarly, in the case of the downlink, the mobile station may be the receiver and the base station may be the device (10).

[0059] Embodiments of the present specification may be supported by standard documents disclosed in at least one of wireless access systems, such as IEEE 802.xx system, 3rd Generation Partnership Project (3GPP) system, 3GPP Long Term Evolution (LTE) system, 3GPP 5G (5th generation) NR (New Radio) system and 3GPP2 system, and in particular, embodiments of the present specification may be supported by 3GPP TS (technical specification) 38.211, 3GPP TS 38.212, 3GPP TS 38.213, 3GPP TS 38.321 and 3GPP TS 38.331 documents.

[0060] Furthermore, the embodiments of this specification may be applied to other wireless access systems and are not limited to the aforementioned systems. For example, they may also be applicable to systems implemented after the 3GPP 5G NR system, and are not limited to a specific system.

[0061] That is, obvious steps or parts not described in the embodiments of this specification may be explained by reference to the above documents. In addition, all terms disclosed in this specification may be explained by the above standard documents.

[0062] Hereinafter, preferred embodiments according to the present specification will be described in detail with reference to the accompanying drawings. The detailed description set forth below, together with the accompanying drawings, is intended to illustrate exemplary embodiments of the present specification and is not intended to represent the only embodiments in which the technical components of the present specification may be implemented.

[0063] Additionally, specific terms used in the embodiments of this specification are provided to aid in understanding of this specification, and the use of these specific terms may be changed to other forms without departing from the technical spirit of this specification.

[0064] 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).

[0065] In order to make the following description clear, the following description is based on a 3GPP communication system (e.g., LTE, NR, etc.), but the technical idea of ​​the present invention is not limited thereto. LTE may refer to technology after 3GPP TS 36.xxx Release 8. Specifically, LTE technology after 3GPP TS 36.xxx Release 10 may be referred to as LTE-A, and LTE technology after 3GPP TS 36.xxx Release 13 may be referred to as LTE-A pro. 3GPP NR may refer to technology after TS 38.xxx Release 15. 3GPP 6G may refer to technology after TS Release 17 and / or Release 18. "xxx" refers to a standard document detail number. LTE / NR / 6G may be collectively referred to as a 3GPP system.

[0066] For background information, terms, abbreviations, etc. used in this specification, reference may be made to standard documents published prior to the invention of the present invention. For example, reference may be made to the 36.xxx and 38.xxx standard documents.

[0067] Communication systems applicable to this specification

[0068] Although not limited thereto, the various descriptions, functions, procedures, proposals, methods and / or operational flowcharts disclosed herein may be applied to various fields requiring wireless communication / connectivity (e.g., 5G) between devices.

[0069] 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.

[0070] FIG. 1 is a diagram illustrating an example of a communication system applicable to the present specification. Referring to FIG. 1, a communication system (100) applicable to the present specification includes a wireless device, a base station, and a network. Here, a wireless device refers to a device that performs communication using a wireless access technology (e.g., 5G NR, LTE) and may be referred to as a communication / wireless / 5G device. Although not limited thereto, the wireless device may include a robot (100a), a vehicle (100b-1, 100b-2), an XR (extended reality) device (100c), a hand-held device (100d), a home appliance (100e), an IoT (Internet of Things) device (100f), and an AI (artificial intelligence) device / server (100g). For example, the vehicle may include a vehicle equipped with a wireless communication function, an autonomous vehicle, a vehicle capable of performing vehicle-to-vehicle communication, etc. Here, the vehicles (100b-1, 100b-2) may include unmanned aerial vehicles (UAVs) (e.g., drones). The XR devices (100c) include augmented reality (AR) / virtual reality (VR) / mixed reality (MR) devices, and may be implemented in the form of head-mounted devices (HMDs), head-up displays (HUDs) installed in vehicles, televisions, smartphones, computers, wearable devices, home appliances, digital signage, vehicles, robots, etc. The portable devices (100d) may include smartphones, smart pads, wearable devices (e.g., smartwatches, smart glasses), computers (e.g., laptops, etc.), etc. The home appliances (100e) may include TVs, refrigerators, washing machines, etc. The IoT devices (100f) may include sensors, smart meters, etc.For example, the base station (120) and the network (130) may also be implemented as wireless devices, and a specific wireless device (120a) may act as a base station / network node to other wireless devices.

[0071] Wireless devices (100a to 100f) can be connected to a network (130) via a base station (120). AI technology can be applied to the wireless devices (100a to 100f), and the wireless devices (100a to 100f) can be connected to an AI server (100g) via a network (130). The network (130) can be configured using a 3G network, a 4G (e.g., LTE) network, a 5G (e.g., NR) network, etc. The wireless devices (100a to 100f) can communicate with each other via the base station (120) / network (130), but can also communicate directly (e.g., sidelink communication) without going through the base station (120) / network (130). For example, vehicles (100b-1, 100b-2) can communicate directly (e.g., V2V (vehicle to vehicle) / V2X (vehicle to everything) communication). In addition, IoT devices (100f) (e.g., sensors) can communicate directly with other IoT devices (e.g., sensors) or other wireless devices (100a to 100f).

[0072] Wireless communication / connection (150a, 150b, 150c) can be established between wireless devices (100a to 100f) / base stations (120), and base stations (120) / base stations (120). Here, the wireless communication / connection can be established through various wireless access technologies (e.g., 5G NR) such as uplink / downlink communication (150a), sidelink communication (150b) (or D2D communication), and 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, at least some of the various configuration information setting processes for transmitting / receiving wireless signals, various signal processing processes (e.g., channel encoding / decoding, modulation / demodulation, resource mapping / demapping, etc.), and resource allocation processes may be performed based on various proposals of this specification.

[0073] Communication systems applicable to this specification

[0074] FIG. 2 is a diagram illustrating an example of a wireless device applicable to this specification.

[0075] Referring to FIG. 2, the first wireless device (200a) and the second wireless device (200b) can transmit and receive wireless signals via various wireless access technologies (e.g., LTE, NR). Here, {the first wireless device (200a), the second wireless device (200b)} can correspond to {the wireless device (100x), the base station (120)} and / or {the wireless device (100x), the wireless device (100x)} of FIG. 1.

[0076] A first wireless device (200a) includes one or more processors (202a) and one or more memories (204a), and may further include one or more transceivers (206a) and / or one or more antennas (208a). The processor (202a) controls the memories (204a) and / or the transceivers (206a), and may be configured to implement the descriptions, functions, procedures, proposals, methods, and / or operational flowcharts disclosed herein. For example, the processor (202a) may process information in the memory (204a) to generate first information / signals, and then transmit a wireless signal including the first information / signals via the transceivers (206a). In addition, the processor (202a) may receive a wireless signal including second information / signals via the transceivers (206a), and then store information obtained from signal processing of the second information / signals in the memory (204a). The memory (204a) may be connected to the processor (202a) and may store various information related to the operation of the processor (202a). For example, the memory (204a) may perform some or all of the processes controlled by the processor (202a), or may store software code including instructions for performing the descriptions, functions, procedures, proposals, methods, and / or operational flowcharts disclosed herein. Here, the processor (202a) and the memory (204a) may be part of a communication modem / circuit / chip designed to implement wireless communication technology (e.g., LTE, NR). The transceiver (206a) may be connected to the processor (202a) and may transmit and / or receive wireless signals via one or more antennas (208a). The transceiver (206a) may include a transmitter and / or a receiver. The transceiver (206a) may be used interchangeably with an RF (radio frequency) unit. In this specification, wireless device may also mean a communication modem / circuit / chip.

[0077] The second wireless device (200b) includes one or more processors (202b), one or more memories (204b), and may further include one or more transceivers (206b) and / or one or more antennas (208b). The processor (202b) controls the memories (204b) and / or the transceivers (206b), and may be configured to implement the descriptions, functions, procedures, proposals, methods, and / or operational flowcharts disclosed herein. For example, the processor (202b) may process information in the memory (204b) to generate third information / signals, and then transmit a wireless signal including the third information / signals via the transceivers (206b). In addition, the processor (202b) may receive a wireless signal including fourth information / signals via the transceivers (206b), and then store information obtained from signal processing of the fourth information / signals in the memory (204b). The memory (204b) may be connected to the processor (202b) and may store various information related to the operation of the processor (202b). For example, the memory (204b) may perform some or all of the processes controlled by the processor (202b), or may store software code including instructions for performing the descriptions, functions, procedures, proposals, methods, and / or operational flowcharts disclosed herein. Here, the processor (202b) and the memory (204b) may be part of a communication modem / circuit / chip designed to implement wireless communication technology (e.g., LTE, NR). The transceiver (206b) may be connected to the processor (202b) and may transmit and / or receive wireless signals via one or more antennas (208b). The transceiver (206b) may include a transmitter and / or a receiver. The transceiver (206b) may be used interchangeably with an RF unit. In this specification, wireless device may also mean a communication modem / circuit / chip.

[0078] Hereinafter, hardware elements of the wireless device (200a, 200b) will be described in more detail. Although not limited thereto, one or more protocol layers may be implemented by one or more processors (202a, 202b). For example, one or more processors (202a, 202b) may implement one or more layers (e.g., functional layers such as physical (PHY), media access control (MAC), radio link control (RLC), packet data convergence protocol (PDCP), radio resource control (RRC), and service data adaptation protocol (SDAP)). One or more processors (202a, 202b) may generate one or more Protocol Data Units (PDUs) and / or one or more Service Data Units (SDUs) according to the descriptions, functions, procedures, proposals, methods, and / or operational flowcharts disclosed herein. One or more processors (202a, 202b) may generate messages, control information, data or information according to the descriptions, functions, procedures, proposals, methods and / or operational flowcharts disclosed herein. One or more processors (202a, 202b) may generate signals (e.g., baseband signals) including PDUs, SDUs, messages, control information, data or information according to the functions, procedures, proposals and / or methods disclosed herein and provide the signals to one or more transceivers (206a, 206b). One or more processors (202a, 202b) may receive signals (e.g., baseband signals) from one or more transceivers (206a, 206b) and obtain PDUs, SDUs, messages, control information, data or information according to the descriptions, functions, procedures, proposals, methods and / or operational flowcharts disclosed herein.

[0079] One or more processors (202a, 202b) may be referred to as a controller, a microcontroller, a microprocessor, or a microcomputer. One or more processors (202a, 202b) may be implemented by hardware, firmware, software, or a combination thereof. For example, one or more application specific integrated circuits (ASICs), one or more digital signal processors (DSPs), one or more digital signal processing devices (DSPDs), one or more programmable logic devices (PLDs), or one or more field programmable gate arrays (FPGAs) may be included in one or more processors (202a, 202b). The descriptions, functions, procedures, proposals, methods, and / or operational flowcharts disclosed herein may be implemented using firmware or software, and the firmware or software may be implemented to include modules, procedures, functions, etc. The descriptions, functions, procedures, suggestions, methods and / or operation flowcharts disclosed in this specification may be implemented using firmware or software configured to perform one or more processors (202a, 202b) or stored in one or more memories (204a, 204b) and executed by one or more processors (202a, 202b). The descriptions, functions, procedures, suggestions, methods and / or operation flowcharts disclosed in this specification may be implemented using firmware or software in the form of codes, instructions and / or sets of instructions.

[0080] One or more memories (204a, 204b) may be coupled to one or more processors (202a, 202b) and may store various forms of data, signals, messages, information, programs, codes, instructions, and / or commands. The one or more memories (204a, 204b) may be configured as read only memory (ROM), random access memory (RAM), erasable programmable read only memory (EPROM), flash memory, hard drives, registers, cache memory, computer readable storage media, and / or combinations thereof. The one or more memories (204a, 204b) may be located internally and / or externally to the one or more processors (202a, 202b). Additionally, the one or more memories (204a, 204b) may be coupled to the one or more processors (202a, 202b) via various technologies, such as wired or wireless connections.

[0081] One or more transceivers (206a, 206b) can transmit user data, control information, wireless signals / channels, etc., as mentioned in the methods and / or flowcharts of this specification, to one or more other devices. One or more transceivers (206a, 206b) can receive user data, control information, wireless signals / channels, etc., as mentioned in the descriptions, functions, procedures, proposals, methods and / or flowcharts of this specification, from one or more other devices. For example, one or more transceivers (206a, 206b) can be coupled to one or more processors (202a, 202b) and can transmit and receive wireless signals. For example, one or more processors (202a, 202b) can control one or more transceivers (206a, 206b) to transmit user data, control information, or wireless signals to one or more other devices. Additionally, one or more processors (202a, 202b) may control one or more transceivers (206a, 206b) to receive user data, control information, or wireless signals from one or more other devices. Additionally, one or more transceivers (206a, 206b) may be coupled to one or more antennas (208a, 208b), and one or more transceivers (206a, 206b) may be configured to transmit and receive user data, control information, wireless signals / channels, or the like, as referred to in the descriptions, functions, procedures, proposals, methods, and / or operational flowcharts disclosed herein, via one or more antennas (208a, 208b). In the present specification, one or more antennas may be multiple physical antennas or multiple logical antennas (e.g., antenna ports). One or more transceivers (206a, 206b) can convert received user data, control information, wireless signals / channels, etc. from RF band signals to baseband signals in order to process the received user data, control information, wireless signals / channels, etc. using one or more processors (202a, 202b).One or more transceivers (206a, 206b) may convert user data, control information, wireless signals / channels, etc. processed by one or more processors (202a, 202b) from baseband signals to RF band signals. For this purpose, one or more transceivers (206a, 206b) may include an (analog) oscillator and / or filter.

[0082] FIG. 3 is a diagram illustrating a method for processing a transmission signal applied to the present specification. For example, the transmission signal may be processed by a signal processing circuit. At this time, the signal processing circuit (300) may include a scrambler (310), a modulator (320), a layer mapper (330), a precoder (340), a resource mapper (350), and a signal generator (360). At this time, as an example, the operations / functions of FIG. 3 may be performed in the processors (202a, 202b) and / or the transceivers (206a, 206b) of FIG. 2. Furthermore, as an example, the hardware elements of FIG. 3 may be implemented in the processors (202a, 202b) and / or the transceivers (206a, 206b) of FIG. 2. For example, blocks 310 to 350 may be implemented in the processor (202a, 202b) of FIG. 2, and block 360 may be implemented in the transceiver (206a, 206b) of FIG. 2, and are not limited to the above-described embodiments.

[0083] The codeword can be converted into a wireless signal through the signal processing circuit (300) of FIG. 3. Here, the codeword is an encoded bit sequence of an information block. The information block may include a transport block (e.g., a UL-SCH transport block, a DL-SCH transport block). The wireless signal may be transmitted through various physical channels (e.g., a PUSCH, a PDSCH) of FIG. 6. Specifically, the codeword can be converted into a bit sequence scrambled by a scrambler (310). The scramble sequence used for scrambling is generated based on an initialization value, and the initialization value may include ID information of the wireless device, etc. The scrambled bit sequence can be modulated into a modulation symbol sequence by a modulator (320). The modulation scheme may include pi / 2-binary phase shift keying (pi / 2-BPSK), m-phase shift keying (m-PSK), m-quadrature amplitude modulation (m-QAM), etc.

[0084] A complex modulation symbol sequence can be mapped to one or more transmission layers by a layer mapper (330). The modulation symbols of each transmission layer can be mapped to the corresponding antenna port(s) by a precoder (340) (precoding). The output z of the precoder (340) can be obtained by multiplying the output y of the layer mapper (330) by an N*M precoding matrix W. Here, N is the number of antenna ports, and M is the number of transmission layers. Here, the precoder (340) can perform precoding after performing transform precoding (e.g., discrete Fourier transform (DFT) transform) on the complex modulation symbols. In addition, the precoder (340) can perform precoding without performing transform precoding.

[0085] 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.

[0086] The signal processing process for receiving signals in a wireless device can be configured in reverse order of the signal processing process (310-360) of FIG. 3. For example, a wireless device (e.g., 200a, 200b of FIG. 2) can receive wireless signals from the outside through an antenna port / transceiver. The received wireless signals can be converted into baseband signals through a signal restorer. For this purpose, the signal restorer can include a frequency downlink converter, an analog-to-digital converter (ADC), a CP remover, and a fast Fourier transform (FFT) module. Thereafter, the baseband signal can be restored to a codeword through a resource demapper process, a postcoding process, a demodulation process, and a descrambling process. The codewords can be restored to the original information blocks through decoding. Accordingly, a signal processing circuit (not shown) for a received signal may include a signal restorer, a resource de-mapper, a postcoder, a demodulator, a de-scrambler, and a decoder.

[0087] Wireless device structure applicable to this specification

[0088] FIG. 4 is a diagram illustrating another example of a wireless device to which the present specification applies.

[0089] Referring to FIG. 4, the wireless device (400) corresponds to the wireless devices (200a, 200b) of FIG. 2 and may be composed of various elements, components, units, and / or modules. For example, the wireless device (400) may include a communication unit (410), a control unit (420), a memory unit (430), and additional elements (440). The communication unit may include a communication circuit (412) and a transceiver(s) (414). For example, the communication circuit (412) may include one or more processors (202a, 202b) and / or one or more memories (204a, 204b) of FIG. 2. For example, the transceiver(s) (414) may include one or more transceivers (206a, 206b) and / or one or more antennas (208a, 208b) of FIG. 2. The control unit (420) is electrically connected to the communication unit (410), the memory unit (430), and the additional elements (440) and controls the overall operation of the wireless device. For example, the control unit (420) may control the electrical / mechanical operation of the wireless device based on the program / code / command / information stored in the memory unit (430). In addition, the control unit (420) may transmit information stored in the memory unit (430) to an external device (e.g., another communication device) via a wireless / wired interface through the communication unit (410), or store information received from an external device (e.g., another communication device) via a wireless / wired interface in the memory unit (430).

[0090] The additional element (440) may be configured in various ways depending on the type of the wireless device. For example, the additional element (440) may include at least one of a power unit / battery, an input / output unit, a driving unit, and a computing unit. Although not limited thereto, the wireless device (400) may be implemented in the form of a robot (Fig. 1, 100a), a vehicle (Fig. 1, 100b-1, 100b-2), an XR device (Fig. 1, 100c), a portable device (Fig. 1, 100d), a home appliance (Fig. 1, 100e), an IoT device (Fig. 1, 100f), a digital broadcasting terminal, a hologram device, a public safety device, an MTC device, a medical device, a fintech device (or a financial device), a security device, a climate / environmental device, an AI server / device (Fig. 1, 140), a base station (Fig. 1, 120), a network node, etc. Wireless devices may be mobile or stationary depending on the use / service.

[0091] In FIG. 4, various elements, components, units / parts, and / or modules within the wireless device (400) may be entirely interconnected via a wired interface, or at least some may be wirelessly connected via a communication unit (410). For example, within the wireless device (400), the control unit (420) and the communication unit (410) may be wired, and the control unit (420) and a first unit (e.g., 430, 440) may be wirelessly connected via the communication unit (410). In addition, each element, component, unit / part, and / or module within the wireless device (400) may further include one or more elements. For example, the control unit (420) may be composed of a set of one or more processors. For example, the control unit (420) may be composed of a set of a communication control processor, an application processor, an electronic control unit (ECU), a graphics processing processor, a memory control processor, etc. As another example, the memory unit (430) may be composed of RAM, DRAM (dynamic RAM), ROM, flash memory, volatile memory, non-volatile memory, and / or a combination thereof.

[0092] Mobile devices to which this specification applies

[0093] FIG. 5 is a drawing illustrating an example of a mobile device to which the present specification applies.

[0094] Figure 5 illustrates an example of a mobile device applicable to the present specification. The mobile device may include a smartphone, a smart pad, a wearable device (e.g., a smart watch, smart glasses), or a portable computer (e.g., a laptop, etc.). The mobile device may be referred to as a mobile station (MS), a user terminal (UT), a mobile subscriber station (MSS), a subscriber station (SS), an advanced mobile station (AMS), or a wireless terminal (WT).

[0095] Referring to FIG. 5, the portable device (500) may include an antenna unit (508), a communication unit (510), a control unit (520), a memory unit (530), a power supply unit (540a), an interface unit (540b), and an input / output unit (540c). The antenna unit (508) may be configured as a part of the communication unit (510). Blocks 510 to 530 / 540a to 540c correspond to blocks 410 to 430 / 440 of FIG. 4, respectively.

[0096] The communication unit (510) can transmit and receive signals (e.g., data, control signals, etc.) with other wireless devices and base stations. The control unit (520) can control components of the portable device (500) to perform various operations. The control unit (520) can include an AP (application processor). The memory unit (530) can store data / parameters / programs / codes / commands required for operating the portable device (500). In addition, the memory unit (530) can store input / output data / information, etc. The power supply unit (540a) supplies power to the portable device (500) and can include a wired / wireless charging circuit, a battery, etc. The interface unit (540b) can support connection between the portable device (500) and other external devices. The interface unit (540b) can include various ports (e.g., audio input / output ports, video input / output ports) for connection with external devices. The input / output unit (540c) can input or output video information / signals, audio information / signals, data, and / or information input from a user. The input / output unit (540c) may include a camera, a microphone, a user input unit, a display unit (540d), a speaker, and / or a haptic module.

[0097] For example, in the case of data communication, the input / output unit (540c) obtains information / signals (e.g., touch, text, voice, image, video) input by the user, and the obtained information / signals can be stored in the memory unit (530). The communication unit (510) can convert the information / signals stored in the memory into wireless signals, and transmit the converted wireless signals directly to other wireless devices or to a base station. In addition, the communication unit (510) can receive wireless signals from other wireless devices or base stations, and then restore the received wireless signals to the original information / signals. The restored information / signals can be stored in the memory unit (530) and then output in various forms (e.g., text, voice, image, video, haptic) through the input / output unit (540c).

[0098] Physical channels and general signal transmission

[0099] In a wireless access system, a terminal can receive information from a base station via the downlink (DL) and transmit it to the base station via the uplink (UL). The information transmitted and received between the base station and the terminal includes general data and various control information, and various physical channels exist depending on the type and purpose of the information being transmitted and received.

[0100] FIG. 6 is a diagram illustrating physical channels applicable to this specification and a signal transmission method using them.

[0101] When a terminal is powered on again from a powered-off state or newly enters a cell, it performs an initial cell search operation, such as synchronizing with the base station, in step S611. To this end, the terminal receives a primary synchronization channel (P-SCH) and a secondary synchronization channel (S-SCH) from the base station to synchronize with the base station and obtain information such as the cell ID.

[0102] After that, the terminal can obtain broadcast information within the cell by receiving a physical broadcast channel (PBCH) signal from the base station. Meanwhile, the terminal can check the downlink channel status by receiving a downlink reference signal (DL RS) in the initial cell search phase. After completing the initial cell search, the terminal can obtain more specific system information by receiving a physical downlink control channel (PDCCH) and a physical downlink shared channel (PDSCH) based on the physical downlink control channel information in step S612.

[0103] Thereafter, the terminal may perform a random access procedure such as steps S613 to S616 to complete connection to the base station. To this end, the terminal may transmit a preamble through a physical random access channel (PRACH) (S613) and receive a random access response (RAR) for the preamble through a physical downlink control channel and a physical downlink shared channel corresponding thereto (S614). The terminal may transmit a physical uplink shared channel (PUSCH) using scheduling information in the RAR (S615) and perform a contention resolution procedure such as receiving a physical downlink control channel signal and a physical downlink shared channel signal corresponding thereto (S616).

[0104] A terminal that has performed the procedure described above can then perform reception of a physical downlink control channel signal and / or a physical downlink shared channel signal (S617) and transmission of a physical uplink shared channel (PUSCH) signal and / or a physical uplink control channel (PUCCH) signal (S618) as a general uplink / downlink signal transmission procedure.

[0105] Control information transmitted from a terminal to a base station is collectively referred to as uplink control information (UCI). UCI includes hybrid automatic repeat and request acknowledgment / negative ACK (HARQ-ACK / NACK), scheduling request (SR), channel quality indication (CQI), precoding matrix indication (PMI), rank indication (RI), and beam indication (BI) information. UCI is generally transmitted periodically through PUCCH, but depending on the embodiment (e.g., when control information and traffic data must be transmitted simultaneously), it may be transmitted through PUSCH. In addition, the terminal may transmit UCI aperiodically through PUSCH upon request / instruction from the network.

[0106] Figure 7 is a diagram illustrating the structure of a wireless frame applicable to this specification.

[0107] Uplink and downlink transmissions based on the NR system can be based on frames such as those in FIG. 7. At this time, one radio frame has a length of 10 ms and can be defined as two 5 ms half-frames (HF). One half-frame can be defined as five 1 ms subframes (SF). One subframe is divided into one or more slots, and the number of slots within a subframe can depend on the subcarrier spacing (SCS). At this time, each slot can contain 12 or 14 OFDM (A) symbols depending on the cyclic prefix (CP). When a normal CP is used, each slot can contain 14 symbols. When an extended CP is used, each slot can contain 12 symbols. Here, the symbol may include an OFDM symbol (or CP-OFDM symbol), an SC-FDMA symbol (or DFT-s-OFDM symbol).

[0108] Table 1 shows the number of symbols per slot, the number of slots per frame, and the number of slots per subframe according to SCS when a general CP is used, and Table 2 shows the number of symbols per slot, the number of slots per frame, and the number of slots per subframe according to SCS when an extended CSP is used.

[0109] [Table 1]

[0110]

[0111] [Table 2]

[0112]

[0113] In Table 1 and Table 2 above, N slot symb represents the number of symbols in the slot, and N frame,μ slot represents the number of slots in the frame, and N subframe,μ slotcan indicate the number of slots within a subframe.

[0114] Additionally, in a system to which the present specification is applicable, OFDM(A) numerologies (e.g., SCS, CP length, etc.) may be set differently between multiple cells merged into a single terminal. Accordingly, the (absolute time) interval of a time resource (e.g., SF, slot, or TTI) (conveniently referred to as TU (time unit)) consisting of the same number of symbols may be set differently between the merged cells.

[0115] NR can support multiple numerologies (or subcarrier spacing (SCS)) to support various 5G services. For example, a 15 kHz SCS supports wide areas in traditional cellular bands; a 30 kHz / 60 kHz SCS supports dense urban areas, lower latency, and wider carrier bandwidth; and a 60 kHz or higher SCS can support bandwidths greater than 24.25 GHz to overcome phase noise.

[0116] The NR frequency band is defined by two types of frequency ranges (FR1 and FR2). FR1 and FR2 can be configured as shown in the table below. FR2 can also refer to millimeter wave (mmW).

[0117] [Table 3]

[0118]

[0119] In addition, as an example, the numerology described above may be set differently in a communication system to which the present specification is applicable. For example, a terahertz wave (THz) band may be used as a frequency band higher than the FR2 described above. In the THz band, the SCS may be set to be larger than in the NR system, and the number of slots may also be set differently, and is not limited to the above-described embodiment. The THz band will be described later.

[0120] Figure 8 is a drawing illustrating a slot structure applicable to this specification.

[0121] A single slot contains multiple symbols in the time domain. For example, a slot contains seven symbols in a regular CP, but a slot may contain six symbols in an extended CP. A carrier contains multiple subcarriers in the frequency domain. A Resource Block (RB) can be defined as multiple (e.g., 12) consecutive subcarriers in the frequency domain.

[0122] Additionally, a Bandwidth Part (BWP) is defined as multiple consecutive (P)RBs in the frequency domain, and can correspond to one numerology (e.g., SCS, CP length, etc.).

[0123] A carrier can contain up to N (e.g., 5) BWPs. Data communication is performed through an activated BWP, and only one BWP can be activated per terminal. Each element in the resource grid is referred to as a Resource Element (RE), to which a single complex symbol can be mapped.

[0124] 6G communication system

[0125] The 6G (wireless communication) system aims to achieve (i) very high data rates per device, (ii) a very large number of connected devices, (iii) global connectivity, (iv) very low latency, (v) low energy consumption for battery-free IoT devices, (vi) ultra-reliable connectivity, and (vii) connected intelligence with machine learning capabilities. The vision of the 6G system can be divided into four aspects: "intelligent connectivity," "deep connectivity," "holographic connectivity," and "ubiquitous connectivity," and the 6G system can satisfy the requirements as shown in Table 4 below. In other words, Table 4 is a table showing the requirements of the 6G system.

[0126] [Table 4]

[0127]

[0128] 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.

[0129] FIG. 9 is a diagram illustrating an example of a communication structure that can be provided in a 6G system applicable to this specification.

[0130] Referring to Figure 9, 6G systems are expected to have 50 times higher simultaneous wireless communication connectivity than 5G wireless communication systems. URLLC, a key feature of 5G, is expected to become a more prominent technology in 6G communications by providing end-to-end latency of less than 1 ms. Furthermore, 6G systems will have significantly better volumetric spectral efficiency, unlike the commonly used area spectral efficiency. 6G systems can offer extremely long battery life and advanced battery technologies for energy harvesting, eliminating the need for separate charging for mobile devices in 6G systems. Furthermore, new network characteristics in 6G may include:

[0131] - Satellite integrated network: 6G is expected to integrate with satellites to provide a global mobile network. The integration of terrestrial, satellite, and airborne networks into a single wireless communications system could be crucial for 6G.

[0132] 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).

[0133] - 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.

[0134] - Ubiquitous super 3-dimension connectivity: Access to networks and core network functions from drones and very low Earth orbit satellites will create super 3-dimension connectivity in 6G ubiquitous.

[0135] Some general requirements for the new network characteristics of 6G, such as the above, may be as follows:

[0136] - 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.

[0137] 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.

[0138] 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.

[0139] - 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.

[0140] - 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.

[0141] Core implementation technology of 6G systems

[0142] - Artificial Intelligence (AI)

[0143] The most crucial and newly introduced technology for 6G systems is AI. 4G systems did not involve AI. 5G systems will support partial or very limited AI. However, 6G systems will fully support AI for automation. Advances in machine learning will create more intelligent networks for real-time communications in 6G. Incorporating AI into communications can streamline and improve real-time data transmission. AI can use numerous analyses to determine how complex target tasks should be performed. In other words, AI can increase efficiency and reduce processing delays.

[0144] Time-consuming tasks such as handover, network selection, and resource scheduling can be performed instantly using AI. AI can also play a crucial role in machine-to-machine (M2M), machine-to-human, and human-to-machine communications. Furthermore, AI can facilitate rapid communication in brain-computer interfaces (BCIs). AI-based communication systems can be supported by metamaterials, intelligent structures, intelligent networks, intelligent devices, intelligent cognitive radios, self-sustaining wireless networks, and machine learning.

[0145] Recent attempts to integrate AI into wireless communication systems have focused on the application layer and network layer, particularly deep learning in wireless resource management and allocation. However, this research is increasingly evolving to the MAC layer and physical layer, with attempts to combine deep learning with wireless transmission, particularly in the physical layer. AI-based physical layer transmission refers to applying AI-based signal processing and communication mechanisms, rather than traditional communication frameworks, in the fundamental signal processing and communication mechanisms. For example, this may include deep learning-based channel coding and decoding, deep learning-based signal estimation and detection, deep learning-based multiple input multiple output (MIMO) mechanisms, and AI-based resource scheduling and allocation.

[0146] Machine learning can be used for channel estimation and channel tracking, as well as for power allocation and interference cancellation at the physical layer of the downlink (DL). Machine learning can also be used for antenna selection, power control, and symbol detection in MIMO systems.

[0147] However, the application of DNN for transmission at the physical layer may have the following problems.

[0148] Deep learning-based AI algorithms require a large amount of training data to optimize training parameters. However, due to limitations in obtaining training data from specific channel environments, a large amount of training data is used offline. This means that static training on training data in specific channel environments can lead to conflicts with the dynamic characteristics and diversity of the wireless channel.

[0149] Furthermore, current deep learning primarily targets real-world signals. However, signals at the physical layer of wireless communications are complex signals. Further research is needed on neural networks capable of detecting complex domain signals to match the characteristics of wireless communication signals.

[0150] Below, we will look at machine learning in more detail.

[0151] Machine learning refers to a series of operations that train machines to perform tasks that humans can or cannot perform. Machine learning requires data and a learning model. In machine learning, data learning methods can be broadly categorized into three types: supervised learning, unsupervised learning, and reinforcement learning.

[0152] Neural network training aims to minimize output errors. It involves repeatedly inputting training data into a neural network, calculating the neural network output and target error for the training data, and backpropagating the neural network error from the output layer to the input layer to update the weights of each node in the neural network to reduce the error.

[0153] Supervised learning uses labeled training data, while unsupervised learning may not have labeled training data. For example, in the case of supervised learning for data classification, the training data may be data in which each training data category is labeled. The labeled training data is input to a neural network, and the error can be calculated by comparing the output (categories) of the neural network with the training data labels. The calculated error is backpropagated through the neural network in the backward direction (i.e., from the output layer to the input layer), and the connection weights of each node in each layer of the neural network can be updated through backpropagation. The amount of change in the connection weights of each updated node can be determined by the learning rate. The neural network's calculation of the input data and the backpropagation of the error can constitute a learning cycle (epoch). The learning rate can be applied differently depending on the number of iterations of the neural network's learning cycle. For example, in the early stages of training a neural network, a high learning rate can be used to quickly allow the network to achieve a certain level of performance, thereby increasing efficiency. In the later stages of training, a low learning rate can be used to increase accuracy.

[0154] Learning methods may vary depending on the characteristics of the data. For example, if the goal is to accurately predict data transmitted from a device (10) in a communication system at the receiving end, it is preferable to perform learning using supervised learning rather than unsupervised learning or reinforcement learning.

[0155] The learning model corresponds to the human brain, and the most basic linear model can be thought of, but the machine learning paradigm that uses highly complex neural network structures, such as artificial neural networks, as learning models is called deep learning.

[0156] The neural network cores used in learning methods are largely divided into deep neural networks (DNN), convolutional deep neural networks (CNN), and recurrent Boltzmann machines (RNN), and these learning models can be applied.

[0157] An artificial neural network is an example of a network of multiple perceptrons.

[0158] Figure 10 shows an example of a perceptron structure.

[0159] Referring to Fig. 10, when the input vector x=(x1,x2,...,xd) is input, each component is multiplied by the weight (W1,W2,...,Wd), and all the results are added up, and then the activation function σ( ) is called a perceptron. A large artificial neural network structure can extend the simplified perceptron structure shown in Fig. 10 to apply input vectors to different multi-dimensional perceptrons. For convenience of explanation, input values ​​or output values ​​are called nodes.

[0160] Meanwhile, the perceptron structure illustrated in Fig. 10 can be explained as consisting of a total of three layers based on input and output values. An artificial neural network in which there are H perceptrons of (d+1) dimensions between the 1st layer and the 2nd layer, and K perceptrons of (H+1) dimensions between the 2nd layer and the 3rd layer can be expressed as in Fig. 4. Fig. 11 shows an example of a multilayer perceptron structure.

[0161] The layer where the input vector is located is called the input layer, the layer where the final output value is located is called the output layer, and all layers located between the input layer and the output layer are called hidden layers. The example in Fig. 4 discloses three layers, but when counting the number of layers in an actual artificial neural network, the input layer is excluded, so it can be viewed as a total of two layers. An artificial neural network is composed of perceptrons, which are basic blocks, connected in two dimensions.

[0162] The aforementioned input, hidden, and output layers can be applied jointly not only to multilayer perceptrons but also to various artificial neural network structures, such as CNNs and RNNs, which will be described later. The greater the number of hidden layers, the deeper the artificial neural network. The machine learning paradigm that uses sufficiently deep artificial neural networks as learning models is called deep learning. Furthermore, the artificial neural network used for deep learning is called a deep neural network (DNN).

[0163] The deep neural network illustrated in Figure 12 is a multilayer perceptron consisting of eight hidden layers and eight output layers. The multilayer perceptron structure is referred to as a fully connected neural network. In a fully connected neural network, there is no connection between nodes located in the same layer, and there is a connection only between nodes located in adjacent layers. DNN has a fully connected neural network structure and is composed of a combination of multiple hidden layers and activation functions, and can be usefully applied to identify correlation characteristics between inputs and outputs. Here, the correlation characteristic can mean the joint probability of inputs and outputs.

[0164] Meanwhile, depending on how multiple perceptrons are connected to each other, various artificial neural network structures different from the aforementioned DNN can be formed.

[0165] In DNN, nodes located within a single layer are arranged in a one-dimensional vertical direction. However, Fig. 13 can assume a case where nodes are arranged two-dimensionally, with w nodes in width and h nodes in height (convolutional neural network structure of Fig. 6). In this case, since a weight is added to each connection in the connection process from one input node to the hidden layer, a total of h×w weights must be considered. Since there are h×w nodes in the input layer, a total of h2w2 weights are required between two adjacent layers.

[0166] The convolutional neural network of Fig. 13 has a problem in that the number of weights increases exponentially according to the number of connections. Therefore, instead of considering the connections of all modes between adjacent layers, it assumes that there are small filters, and performs weighted sum and activation function operations on the overlapping portions of the filters, as in Fig. 7.

[0167] Each filter has a weight corresponding to its size, and weight learning can be performed to extract and output a specific feature on the image as a factor. In Fig. 14, a 3×3 filter is applied to the upper left 3×3 region of the input layer, and the output value resulting from performing weighted sum and activation function operations on the corresponding node is stored in z22.

[0168] The above filter performs weighted sum and activation function operations while moving at a certain horizontal and vertical interval while scanning the input layer, and places the output value at the current filter position. This operation method is similar to the convolution operation for images in the field of computer vision, so a deep neural network with this structure is called a convolutional neural network (CNN), and the hidden layer generated as a result of the convolution operation is called a convolutional layer. In addition, a neural network with multiple convolutional layers is called a deep convolutional neural network (DCNN).

[0169] In the convolutional layer, the number of weights can be reduced by calculating a weighted sum that includes only the nodes located in the area covered by the filter, starting from the node where the current filter is located. This allows a single filter to focus on features within a local area. Accordingly, CNNs can be effectively applied to image data processing where physical distance in a two-dimensional area is an important criterion for judgment. Meanwhile, CNNs can apply multiple filters immediately before the convolutional layer, and can generate multiple output results through the convolution operation of each filter.

[0170] Meanwhile, depending on the data properties, there may be data for which sequence characteristics are important. Considering the length variability and chronological relationship of such sequence data, a structure that applies a method of inputting one element of the data sequence at each timestep and inputting the output vector (hidden vector) of the hidden layer output at a specific timestep together with the immediately following element in the sequence is called a recurrent neural network structure.

[0171] Figure 15 shows an example of a neural network structure in which a recurrent loop exists.

[0172] Referring to Figure 15, a recurrent neural network (RNN) is a structure that inputs elements (x1(t), x2(t), ,..., xd(t)) of a data sequence at a time point t into a fully connected neural network, and then inputs the hidden vectors (z1(t-1), z2(t-1),..., zH(t-1)) of the immediately preceding time point t-1 together and applies a weighted sum and activation function. The reason for transmitting the hidden vector to the next time point in this way is because the information in the input vectors of the preceding time points is considered to be accumulated in the hidden vector of the current time point.

[0173] Figure 16 shows an example of the operating structure of a recurrent neural network.

[0174] Referring to Figure 16, the recurrent neural network operates in a predetermined order of time for the input data sequence.

[0175] When the input vector (x1(t), x2(t), ,..., xd(t)) at time point 1 is input to the recurrent neural network, the hidden vector (z1(1), z2(1),..., zH(1)) is input together with the input vector (x1(2), x2(2),..., xd(2)) at time point 2, and the vector (z1(2), z2(2),..., zH(2)) of the hidden layer is determined through a weighted sum and an activation function. This process is repeatedly performed until time points 2, 3, ,,, T.

[0176] Meanwhile, when multiple hidden layers are placed within a recurrent neural network, it is called a deep recurrent neural network (DRNN). Recurrent neural networks are designed to be useful for processing sequence data (e.g., natural language processing).

[0177] It is a neural network core used in a learning manner, and includes various deep learning techniques such as DNN, CNN, RNN, Restricted Boltzmann Machine (RBM), Deep Belief Network (DBN), and Deep Q-Network, and can be applied to fields such as computer vision, speech recognition, natural language processing, and speech / signal processing.

[0178] Recent attempts to integrate AI into wireless communication systems have focused on the application layer, network layer, and especially deep learning in wireless resource management and allocation. However, this research is increasingly evolving to the MAC layer and physical layer, with attempts to combine deep learning with wireless transmission, particularly at the physical layer. AI-based physical layer transmission refers to the application of AI-based signal processing and communication mechanisms, rather than traditional communication frameworks, in the fundamental signal processing and communication mechanisms. For example, this may include deep learning-based channel coding and decoding, deep learning-based signal estimation and detection, deep learning-based MIMO mechanisms, and AI-based resource scheduling and allocation.

[0179] THz (Terahertz) communication

[0180] THz communications can be applied in 6G systems. For example, data transmission rates can be increased by increasing bandwidth. This can be achieved by using sub-THz communications with wide bandwidths and applying advanced massive MIMO technology.

[0181] Figure 17 is a diagram illustrating the electromagnetic spectrum applicable to the present specification. For example, referring to Figure 17, THz waves, also known as sub-millimeter radiation, generally represent a frequency band between 0.1 THz and 10 THz with a corresponding wavelength ranging from 0.03 mm to 3 mm. The 100 GHz to 300 GHz band (Sub-THz band) is considered a major portion of the THz band for cellular communications. Adding the Sub-THz band to the mmWave band will increase the capacity of 6G cellular communications. Among the defined THz bands, 300 GHz to 3 THz is in the far infrared (IR) frequency band. Although the 300 GHz to 3 THz band is part of the optical band, it is at the boundary of the optical band and immediately follows the RF band. Therefore, this 300 GHz to 3 THz band exhibits similarities to RF.

[0182] Key characteristics of THz communications include (i) the widely available bandwidth to support very high data rates and (ii) the high path loss that occurs at high frequencies (requiring highly directional antennas). The narrow beamwidths generated by highly directional antennas reduce interference. The small wavelength of THz signals allows for a significantly larger number of antenna elements to be integrated into devices and base stations operating in this band. This enables the use of advanced adaptive array technologies to overcome range limitations.

[0183] optical wireless technology

[0184] Optical wireless communication (OWC) technology is planned for 6G communications, in addition to RF-based communications for all possible device-to-access networks. These networks connect to network-to-backhaul / fronthaul networks. OWC technology has already been used in 4G communication systems, but it will be used more widely to meet the demands of 6G communication systems. OWC technologies such as light fidelity, visible light communication, optical camera communication, and wideband-based free space optical (FSO) communication are already well-known. Optical wireless communication can provide very high data rates, low latency, and secure communications. Light detection and ranging (LiDAR) can also be used for ultra-high-resolution 3D mapping in 6G communications based on wideband technology.

[0185] FSO backhaul network

[0186] The transmitter and receiver characteristics of an FSO system are similar to those of a fiber-optic network. Therefore, data transmission in an FSO system is similar to that of a fiber-optic system. Therefore, FSO can be a promising technology for providing backhaul connectivity in 6G systems, in conjunction with fiber-optic networks. Using FSO, ultra-long-distance communications are possible, even over distances exceeding 10,000 km. FSO supports high-capacity backhaul connectivity for remote and non-remote areas, such as the ocean, space, underwater, and isolated islands. FSO also supports cellular base station connections.

[0187] Massive MIMO technology

[0188] One of the key technologies for improving spectral efficiency is the application of MIMO technology. As MIMO technology improves, spectral efficiency also improves. Therefore, massive MIMO technology will be crucial in 6G systems. Because MIMO technology utilizes multiple paths, multiplexing technology must be considered to ensure that data signals can be transmitted along more than one path, as well as beam generation and operation technologies suitable for the THz band.

[0189] Blockchain

[0190] Blockchain will become a crucial technology for managing massive amounts of data in future communication systems. Blockchain is a form of distributed ledger technology. A distributed ledger is a database distributed across numerous nodes or computing devices. Each node replicates and stores an identical copy of the ledger. Blockchains are managed by a peer-to-peer (P2P) network and can exist without being managed by a central authority or server. Data on a blockchain is collected and organized into blocks. Blocks are linked together and protected using cryptography. Blockchain perfectly complements large-scale IoT with its inherently enhanced interoperability, security, privacy, reliability, and scalability. Therefore, blockchain technology offers several features, such as interoperability between devices, traceability of large amounts of data, autonomous interaction with other IoT systems, and the massive connectivity stability of 6G communication systems.

[0191] 3D networking

[0192] 6G systems integrate terrestrial and airborne networks to support vertically expanded user communications. 3D BS will be provided via low-orbit satellites and UAVs. Adding a new dimension in altitude and associated degrees of freedom, 3D connections differ significantly from existing 2D networks.

[0193] Quantum communication

[0194] Unsupervised reinforcement learning holds promise in the context of 6G networks. Supervised learning approaches cannot label the massive amounts of data generated by 6G networks. Unsupervised learning does not require labeling. Therefore, this technology can be used to autonomously build representations of complex networks. Combining reinforcement learning and unsupervised learning allows for truly autonomous network operation.

[0195] drone

[0196] Unmanned aerial vehicles (UAVs), or drones, will be a key element in 6G wireless communications. In most cases, high-speed wireless connections will be provided using UAV technology. Base stations are installed on UAVs to provide cellular connectivity. UAVs offer specific capabilities not found in fixed base station infrastructure, such as easy deployment, robust line-of-sight links, and controlled mobility. During emergencies such as natural disasters, deploying terrestrial communications infrastructure is not economically feasible, and sometimes, volatile environments make it impossible to provide services. UAVs can easily handle these situations. UAVs will become a new paradigm in wireless communications. This technology facilitates three fundamental requirements for wireless networks: enhanced mobile broadband (eMBB), URLLC, and mMTC. UAVs can also support various purposes, such as enhancing network connectivity, fire detection, disaster emergency services, security and surveillance, pollution monitoring, parking monitoring, and accident monitoring. Therefore, UAV technology is recognized as one of the most important technologies for 6G communications.

[0197] cell-free communication

[0198] Tight integration of multiple frequencies and heterogeneous communication technologies is crucial in 6G systems. As a result, users will be able to seamlessly move from one network to another without requiring any manual configuration on their devices. The best network will be automatically selected from available communication technologies. This will break the limitations of the cell concept in wireless communications. Currently, user movement from one cell to another in dense networks results in excessive handovers, resulting in handover failures, handover delays, data loss, and a ping-pong effect. 6G cell-free communications will overcome all of these challenges and provide better QoS. Cell-free communications will be achieved through multi-connectivity and multi-tier hybrid technologies, as well as heterogeneous radios on devices.

[0199] Wireless Information and Energy Transfer (WIET)

[0200] WIET uses the same fields and waves as wireless communication systems. Specifically, sensors and smartphones will be charged using wireless power transfer during communication. WIET is a promising technology for extending the life of battery-powered wireless systems. Therefore, battery-less devices will be supported by 6G communications.

[0201] Integration of sensing and communication

[0202] Autonomous wireless networks are capable of continuously sensing dynamically changing environmental conditions and exchanging information between different nodes. In 6G, sensing will be tightly integrated with communications to support autonomous systems.

[0203] Integration of Access Backhaul Networks

[0204] In 6G, the density of access networks will be enormous. Each access network will be connected to backhaul connections, such as fiber optics and FSO networks. To accommodate the massive number of access networks, there will be tight integration between access and backhaul networks.

[0205] Holographic beamforming

[0206] Beamforming is a signal processing procedure that adjusts an antenna array to transmit a wireless signal in a specific direction. It is a subset of smart antennas or advanced antenna systems. Beamforming technology offers several advantages, including high signal-to-noise ratio, interference avoidance and rejection, and high network efficiency. Holographic beamforming (HBF) is a novel beamforming method that differs significantly from MIMO systems because it uses software-defined antennas. HBF will be a highly effective approach for efficient and flexible signal transmission and reception in multi-antenna communication devices in 6G.

[0207] Big data analysis

[0208] Big data analytics is a complex process for analyzing diverse, large-scale data sets, or "big data." This process uncovers hidden data, unknown correlations, and customer trends, ensuring complete data management. Big data is collected from various sources, such as video, social networks, images, and sensors. This technology is widely used to process massive amounts of data in 6G systems.

[0209] large intelligent surface (LIS)

[0210] THz band signals have strong linearity, which can create many shadow areas due to obstacles. LIS technology, which enables expanded communication coverage, enhanced communication stability, and additional value-added services by installing LIS near these shadow areas, is becoming increasingly important. LIS is an artificial surface made of electromagnetic materials that can alter the propagation of incoming and outgoing radio waves. While LIS may be viewed as an extension of Massive MIMO, it differs from Massive MIMO in its array structure and operating mechanism. Furthermore, LIS operates as a reconfigurable reflector with passive elements, passively reflecting signals without using active RF chains, which offers the advantage of low power consumption. Furthermore, because each passive reflector in LIS must independently adjust the phase shift of the incoming signal, this can be advantageous for wireless communication channels. By appropriately adjusting the phase shift via the LIS controller, the reflected signal can be collected at the target receiver to boost the received signal power.

[0211] Terahertz (THz) wireless communications

[0212] Fig. 18 is a diagram illustrating a THz communication method applicable to this specification.

[0213] Referring to Fig. 18, THz wireless communication is a wireless communication using THz waves with a frequency of approximately 0.1 to 10 THz (1 THz = 1012 Hz), and may refer to terahertz (THz) band wireless communication using a very high carrier frequency of 100 GHz or higher. THz waves are located between the RF (Radio Frequency) / millimeter (mm) and infrared bands, and (i) compared to visible light / infrared rays, they penetrate non-metallic / non-polarizable materials well, and compared to RF / millimeter waves, they have a shorter wavelength, so they have high straightness and can focus beams.

[0214] In addition, since the photon energy of THz waves is only a few meV, it has the characteristic of being harmless to the human body. The frequency band expected to be used for THz wireless communication may be the D-band (110 GHz to 170 GHz) or H-band (220 GHz to 325 GHz) bands where propagation loss due to absorption of molecules in the air is small. In addition to 3GPP, standardization discussions for THz wireless communication are being centered around the IEEE 802.15 THz WG (working group), and standard documents issued by the IEEE 802.15 TG (task group) (e.g., TG3d, TG3e) may specify or supplement the contents described in this specification. THz wireless communication can be applied to wireless cognition, sensing, imaging, wireless communication, THz navigation, etc.

[0215] Specifically, referring to FIG. 18, THz wireless communication scenarios can be categorized into macro networks, micro networks, and nanoscale networks. In macro networks, THz wireless communication can be applied to vehicle-to-vehicle (V2V) connections and backhaul / fronthaul connections. In micro networks, THz wireless communication can be applied to fixed point-to-point or multi-point connections, such as wireless connections in indoor small cells and data centers, and near-field communication, such as kiosk downloading. Table 5 below shows examples of technologies that can be utilized in THz waves.

[0216] [Table 5]

[0217]

[0218] FIG. 19 is a diagram illustrating a THz wireless communication transceiver applicable to the present specification.

[0219] Referring to Figure 19, THz wireless communication can be classified based on the method for THz generation and reception. THz generation methods can be classified into optical or electronic device-based technologies.

[0220] Here, methods for generating THz using electronic components include a method using a semiconductor component such as a resonant tunneling diode (RTD), a method using a local oscillator and a multiplier, a MMIC (monolithic microwave integrated circuit) method using an integrated circuit based on a compound semiconductor HEMT (high electron mobility transistor), and a method using an Si-CMOS-based integrated circuit. In the case of Fig. 19, a multiplier (doubler, tripler, multiplier) is applied to increase the frequency, and it passes through a subharmonic mixer and is radiated by an antenna. Since the THz band forms a high frequency, a multiplier is essential. Here, the multiplier is a circuit that has an output frequency that is N times that of the input, and matches it to the desired harmonic frequency and filters out all remaining frequencies. In addition, beamforming can be implemented by applying an array antenna or the like to the antenna of Fig. 19. In Figure 19, IF represents intermediate frequency, tripler and multipler represent multipliers, PA represents a power amplifier, LNA represents a low noise amplifier, and PLL represents a phase-locked loop.

[0221] FIG. 20 is a diagram illustrating a THz signal generation method applicable to the present specification. FIG. 21 is a diagram illustrating a wireless communication transceiver applicable to the present specification.

[0222] Referring to FIGS. 20 and 21, optical device-based THz wireless communication technology refers to a method of generating and modulating a THz signal using an optical device. The optical device-based THz signal generation technology is a technology that generates an ultra-high-speed optical signal using a laser and an optical modulator, and converts it into a THz signal using an ultra-high-speed photodetector. Compared to a technology that uses only electronic devices, this technology makes it easy to increase the frequency, enables high-power signal generation, and obtains a flat response characteristic in a wide frequency band. In order to generate a THz signal based on an optical device, a laser diode, a wideband optical modulator, and an ultra-high-speed photodetector are required, as illustrated in FIG. 20. In the case of FIG. 20, the light signals of two lasers with different wavelengths are combined to generate a THz signal corresponding to the wavelength difference between the lasers. In Fig. 20, an optical coupler refers to a semiconductor device that transmits an electrical signal using optical waves to provide electrical isolation and coupling between circuits or systems, and a uni-travelling carrier photo-detector (UTC-PD) is a type of photodetector that uses electrons as active carriers and reduces the travel time of electrons by bandgap grading. The UTC-PD is capable of detecting light at 150 GHz or higher. In Fig. 20, an erbium-doped fiber amplifier (EDFA) represents an erbium-doped fiber amplifier, a photodetector (PD) represents a semiconductor device that can convert an optical signal into an electrical signal, an OSA represents an optical sub-assembly that modularizes various optical communication functions (e.g., photoelectric conversion, electro-optical conversion, etc.) into a single component, and a DSO represents a digital storage oscilloscope.

[0223] Fig. 22 is a diagram illustrating a transmitter structure applicable to the present specification. In addition, Fig. 23 is a diagram illustrating a modulator structure applicable to the present specification.

[0224] Referring to FIGS. 22 and 23, a signal phase, etc. can generally be changed by passing an optical source of a laser through an optical wave guide. At this time, data is loaded by changing the electrical characteristics through a microwave contact, etc. Therefore, the optical modulator output is formed as a modulated waveform. An opto-electrical (O / E) converter can generate a THz pulse according to an optical rectification operation by a nonlinear crystal, an opto-electrical conversion by a photoconductive antenna, an emission from a bunch of relativistic electrons, etc. A terahertz pulse (THz pulse) generated in the above manner can have a length in units of femtoseconds to picoseconds. An optical / electronic converter (O / E converter) performs down conversion by utilizing the non-linearity of the device.

[0225] Considering the THz spectrum usage, it is likely that THz systems will use multiple contiguous gigahertz bands for fixed or mobile service purposes. Based on the outdoor scenario criteria, the available bandwidth can be classified based on the oxygen attenuation of 10^2 dB / km in the spectrum up to 1 THz. Accordingly, a framework in which the available bandwidth is composed of multiple band chunks can be considered. As an example of the above framework, if the THz pulse length for one carrier is set to 50 ps, ​​the bandwidth (BW) becomes approximately 20 GHz.

[0226] Effective down-conversion from the infrared band to the terahertz band (THz band) depends on how to utilize the nonlinearity of the optical / electrical converter (O / E converter). In other words, to down-convert to the desired terahertz band (THz band), it is necessary to design an O / E converter with the most ideal non-linearity for transferring to the corresponding terahertz band (THz band). If an O / E converter that is not suitable for the target frequency band is used, errors are likely to occur in the amplitude and phase of the corresponding pulse.

[0227] In a single-carrier system, a terahertz transmission and reception system can be implemented using a single optical-to-electrical converter. Depending on the channel environment, in a multi-carrier system, the number of optical-to-electrical converters may be equal to the number of carriers. This phenomenon will be particularly noticeable in a multi-carrier system that utilizes multiple broadbands according to the aforementioned spectrum usage plan. In this regard, a frame structure for the multi-carrier system may be considered. A signal down-converted using an optical-to-electrical converter may be transmitted in a specific resource region (e.g., a specific frame). The frequency domain of the specific resource region may include a plurality of chunks. Each chunk may be composed of at least one component carrier (CC).

[0228] Here, the wireless communication technology implemented in the wireless devices (200a, 200b) of the present specification may include not only LTE, NR, and 6G, but also Narrowband Internet of Things for low-power communication. At this time, for example, NB-IoT technology may be an example of LPWAN (Low Power Wide Area Network) technology, and may be implemented with standards such as LTE Cat NB1 and / or LTE Cat NB2, and is not limited to the above-described names. Additionally or alternatively, the wireless communication technology implemented in the wireless devices (XXX, YYY) of the present specification may perform communication based on LTE-M technology. At this time, for example, LTE-M technology may be an example of LPWAN technology, and may be called by various names such as eMTC (enhanced Machine Type Communication). For example, LTE-M technology can be implemented by at least one of various standards such as 1) LTE CAT 0, 2) LTE Cat M1, 3) LTE Cat M2, 4) LTE non-BL (non-Bandwidth Limited), 5) LTE-MTC, 6) LTE Machine Type Communication, and / or 7) LTE M, and is not limited to the above-described names. Additionally or alternatively, the wireless communication technology implemented in the wireless device (200a, 200b) of the present specification can include at least one of ZigBee, Bluetooth, and Low Power Wide Area Network (LPWAN) considering low-power communication, and is not limited to the above-described names. For example, ZigBee technology can create personal area networks (PAN) related to small / low-power digital communication based on various standards such as IEEE 802.15.4, and can be called by various names.

[0229] Specific embodiments of the present disclosure

[0230] FIG. 24 is a diagram illustrating an example of a device according to an embodiment of the present disclosure. FIG. 25 is a diagram illustrating an example of a multi-beam configuration according to an embodiment of the present disclosure. FIG. 26 is a diagram illustrating an example of a sensing beam pattern that minimizes channel estimation error according to an embodiment of the present disclosure.

[0231] Referring to FIGS. 24 to 25, the device (10) may include a Tx array (100), an Rx array (200), and a second Rx array (300). The device (10) may be used in a Joint Communication and Sensing (JCAS) system (10).

[0232] In a JCAS system, it is important to design a transmission multi-beam that can optimize communication and sensing. A stable and accurate pointing beam is required for good communication link quality, and a sensing beam that can improve target detection probability and estimation performance may be required. The device (10) can design communication transmission beams and sensing transmission beams for specific purposes and combine them to form a multi-beam. First, the method by which the device (10) designs a communication transmission beam is as follows.

[0233] Transmitting communication beam design

[0234] The device (10) can estimate a massive MIMO (multi-input multi-output)-OFDM (Orthogonal frequency-division multiplexing) communication channel between a base station and a terminal with high accuracy using a compressive method. The device (10) Data streams and channel estimates ( ) can obtain a transmission BF (beamforming) weight that maximizes mutual information. The device (10) can obtain a transmission covariance matrix ( )of We can obtain the BF weights in the form of the dominant eigenvectors.

[0235] For example, if we assume that there is one dominant Line of Sight (LoS) for communication, the Angle of Departure (AoD) between the base station and the terminal is The optimal transmission BF weights in a path are the eigenvectors associated with the largest eigenvalues. ) can be obtained. The method by which the device (10) designs the sensing beam is as follows.

[0236] Sensing beam design

[0237] Sensing beam design to minimize the mean squared error (MSE) of the channel

[0238] The device (10) describes a method for obtaining a BF matrix (Q) using a received signal using a transmission BF matrix (F) and a reception BF matrix (W) in a MIMO-OFDM radar. For convenience of explanation, the notation ((m,k)) is omitted.

[0239] If the statistics of n received by the device (10) are unknown (statistics are unknown), the device (10) can use the Method of Least Squares.

[0240] The device (10) is based on the least squares method. can be minimized. Here, y can represent the received signal, Q can be a beamforming matrix, and h can be a channel estimation. That is, Mathematically, it can mean the value obtained by subtracting the value obtained by multiplying the beamforming matrix by the channel estimation from the received signal. Therefore, Minimizing may mean minimizing reception error. The device (10) By minimizing , we can obtain the following mathematical expression 1.

[0241] [Mathematical Formula 1]

[0242]

[0243] In mathematical equation 1, may be the Hermetic transpose of Q.

[0244] The device (10) can obtain a unique solution based on the Uniqueness Theorem. The device (10) If it has full rank, a unique solution can be obtained. The least solution (LS) of the unique solution can be as follows: Equation 2.

[0245] [Equation 2]

[0246]

[0247] In mathematical expression 2, can be an LS solution. If N is a zero-mean random vector, The average of This can be, can be an unbiased estimator, and the corresponding MSE can be expressed as in the following mathematical expression 3.

[0248] [Equation 3]

[0249]

[0250] In mathematical expression 3, can be the MSE of the discomfort estimator, can be the square of the standard deviation. On the other hand, tr can be a function that derives the sum of the elements on the diagonal of the matrix, and therefore, Is can be the sum of the diagonal elements of the Lagrange multiplier. The minimization condition can be as follows: Equation 4.

[0251] [Equation 4]

[0252]

[0253] In mathematical equation 4, can be a beamforming matrix, can be the average power of the entire transmitted signal, may be the overall noise, may be an interference signal between the transmitting and receiving antennas. Also, can be the number of transmitting and receiving antennas, respectively, can be the number of transmit and receive beamforming vectors, respectively.

[0254] Referring to mathematical expression 4, however, The number of training beams required to become a full rank is It is necessary to satisfy. Therefore, In these large massive antenna systems, the beam training overhead can be very large. If it is not full rank There may be multiple solutions that minimize . The device (10) selects one of the multiple solutions. A minimum-norm solution can be obtained. The minimum-norm solution can be expressed as the following mathematical expression 5.

[0255] [Equation 5]

[0256]

[0257] In mathematical expression 5, may be the minimum standard solution.

[0258] If n is a 0-mean random vector, The average of It can be, The MSE of can be expressed as in the following mathematical expression 6.

[0259] [Equation 6]

[0260]

[0261] In mathematical expression 6, The MSE of It can be, Is can be an eigenvector of . Here, It can be, This may be a projection matrix into the spanning subspace. Therefore, in order to minimize the MSE of mathematical expression 6, the device (10) and It is necessary to design Q that simultaneously minimizes .

[0262] How to minimize MSE

[0263] The device (10) can perform SVD (singular vector decomposition) on BF matrices (Q, F, W). The results of performing SVD on BF matrices (Q, F, W) can be expressed as in the following mathematical expressions 7 to 9.

[0264] [Equation 7]

[0265]

[0266] [Equation 8]

[0267]

[0268] [Equation 9]

[0269]

[0270] In mathematical expressions 7 to 9, Q, F, and W may be the results of performing SVD on Q, F, and W among the BF matrices, respectively. Here, , and It could be.

[0271] [Equation 10]

[0272]

[0273] Meanwhile, if we organize the beamforming matrix Q based on the above mathematical formula 7, and according to the condition of mathematical expression 21 , can be any unitary matrix. Therefore, the beamforming matrix Q can be expressed as in the following mathematical expression 11.

[0274] [Equation 11]

[0275]

[0276] We examine the conditions for the beamforming matrix Q for estimating the target's distance, Doppler frequency, and angle. The sensing channel estimation obtained by the minimum-norm solution can be expressed as in Equation 5. The condition for minimizing the noise-related MSE of the channel estimation error can be expressed as in Equation 12.

[0277] [Equation 12]

[0278] The sufficient condition for is , It can be obtained that F and W satisfy . Therefore, F and W are arbitrary unitary matrices containing the DFT (discrete Fourier transform) matrix. , It can be an orthonormal matrix consisting of columns. Therefore, the ith row of F and W can be expressed as in the following mathematical expressions 13 and 14.

[0279] [Equation 13]

[0280]

[0281] [Equation 14]

[0282]

[0283] In mathematical expressions 13 and 14, and can be the i-th row of each of F and W, respectively, Is can be a DFT matrix for , Is can be a DFT matrix for . Here, may mean an angle offset. The method by which the device (10) designs a transmission multi-beam is as follows.

[0284] Transmitting multi-beam design

[0285] The device (10) is a sensing BF weighting ( ) and communication transmission BF weights ( ) can be combined to generate a single BF weight. In this regard, the device (10) has a soft combining method that adds two weights by adjusting only the BF gain and a commutative coherent combining method that increases the communication BF gain by adjusting the phase of the sensing BF weight. The commutative coherent combining method is a method in which a dominant AoD is generated for a perfectly-known communication channel. In this case, the phase of the sensing beam is adjusted so that the sidelobe of the sensing beam is A method for improving communication BF gain by coherently combining with a communication beam in a direction may be provided. The soft combining method and the communication-centric mutual combining method can be expressed as the following mathematical equations 15 and 16, respectively.

[0286] [Equation 15]

[0287]

[0288] [Equation 16]

[0289]

[0290] In mathematical expressions 15 and 16, It could be.

[0291] The simulation results for verifying the performance of the present disclosure are as follows. The performance evaluation can be verified using the total normalized TX power to noise ratio (TPNR).

[0292] Assuming that the power of the transmit beamforming vector is 1, i.e., In this case, the total transmission average power can be expressed as the following mathematical expression 17.

[0293] [Equation 17]

[0294]

[0295] In mathematical expression 17, It can be. Therefore, TPNR can be expressed as the following mathematical expression 18.

[0296] [Equation 18]

[0297]

[0298] In mathematical expression 18, may be noise power.

[0299] FIG. 27 is a diagram illustrating an example of a multi-beam scenario according to one embodiment of the present disclosure.

[0300] Referring to FIG. 27, the Tx array (1000) uses the communication BF weight (f) in the manner described above in FIGS. 24 to 26. c ) and sensing BF weights (f s ) can be obtained. For example, the Tx array (1000) can obtain the weight (f) of the communication transmission beam through the estimated communication channel. c ) can be obtained. The Tx array (100) can obtain the weight (f) of the communication transmission beam. c ) and the weight of the sensing beam (f s ) can be combined to obtain multi-beam weights.

[0301] The communication Rx array (2000) weights the communication reception beam (w) in the manner described above in FIGS. 24 to 26. c ) can be obtained. For example, the communication Rx array (2000) can obtain the weight (f) of the communication transmission beam through the estimated communication channel. c ) can be obtained.

[0302] The sensing Rx array (3000) can perform updates to the sensing reception BF weights (ws).

[0303] FIG. 28 is a drawing illustrating an example of a multi-beam generation method according to one embodiment of the present disclosure.

[0304] Referring to FIG. 28, the device comprises a Tx BF multibeam weighting ( ) and weighting of the Rx BF sensing beam ( ) can be provided (S2810).

[0305] The device can estimate the channel (S2820). The device (10) Tx BF multibeam weight ( ) and weighting of the Rx BF sensing beam ( ) can obtain a measurement value based on the following mathematical expression 19. The device (10) can obtain a first measurement value based on the following mathematical expression 19.

[0306] [Equation 19]

[0307]

[0308] In mathematical expression 19, may be a measurement value for the received signal, H may be a channel estimate, may be noise in the signal.

[0309] The device (10) is a Tx BF multi-beam weighting ( ) and weighting of the Rx BF sensing beam ( ) and measured values ​​( ) can generate a measurement vector based on the following mathematical expressions 20 to 21. The device (10) can generate a measurement vector based on the following mathematical expressions 20 to 21.

[0310] [Equation 20]

[0311]

[0312] [Equation 21]

[0313]

[0314] In mathematical expressions 20 and 21, can be a measurement vector for the received signal, h can be a vector for channel estimation, and n can be a vector for noise.

[0315] The device (10) can estimate a channel based on a measurement vector. The device (10) can estimate a channel based on the following mathematical expression 22.

[0316] [Equation 22]

[0317]

[0318] In mathematical expression 22, may be a channel estimation value.

[0319] The device (10) can perform an update on the BF weight (S2830). The device (10) can update the weight of the Tx BF sensing beam ( ) and weighting of the Rx BF sensing beam ( ) can obtain the LSV (left singular vector). The device (10) can obtain the LSV based on the secondary statistics of the channel. The weight of the Tx BF sensing beam ( ) and weighting of the Rx BF sensing beam ( ) can be expressed as the following mathematical expression 23.

[0320] [Equation 23]

[0321]

[0322] [Equation 24]

[0323]

[0324] In mathematical expressions 23 and 24, is the weight of the Tx BF sensing beam ( ) can be LSV for the Rx BF sensing beam, and is the weight of the Rx BF sensing beam ( ) can be LSV for, Is can be the signal / null subspace eigenvectors, Is can be the signal / null subspace eigenvectors.

[0325] The device (10) is a weighting device of the Tx BF sensing beam. ) and weighting of the Rx BF sensing beam ( ) can be obtained for the RSV (Right singular vector). The weight of the Tx BF sensing beam ( ) and weighting of the Rx BF sensing beam ( ) can be expressed as in the following mathematical equations 25 and 26.

[0326] [Equation 25]

[0327]

[0328] [Equation 26]

[0329]

[0330] In equations 25 and 26 is the weight of the Tx BF sensing beam ( ) may be RSV for, is the weight of the Rx BF sensing beam ( ) may be RSV.

[0331] The device (10) is a weighting device of the Tx BF sensing beam. ) and weighting of the Rx BF sensing beam ( ) is a singular value matrix for and can be obtained. and can be an arbitrary unitary matrix.

[0332] The device (10) can perform an update on the BF weight. The device (10) can update the weight of the Tx BF sensing beam based on the following mathematical expressions 27 to 28. ) and weighting of the Rx BF sensing beam ( ) can be updated.

[0333] [Equation 27]

[0334]

[0335] [Equation 28]

[0336]

[0337] In mathematical expressions 27 and 28, is the weight of the Tx BF sensing beam on which the update is performed ( ) can be, and the weight of the Rx BF sensing beam ( ) may be.

[0338] The device can perform a summation of the weights of the Tx BF communication beam and the weights of the TX BF sensing beam (S2840). The device (10) can obtain the weights of the Tx BF multi-beam for which an update has been performed by performing a summation of the weights of the Tx BF communication beam and the weights of the Tx BF sensing beam. The device (10) can obtain the weights of the Tx BF multi-beam for which an update has been performed based on the following mathematical expression 29.

[0339] [Equation 29]

[0340]

[0341] In mathematical expression 29, may be the weight of the Tx BF multi-beam on which the update is performed.

[0342] The device is I+1 T It can be determined whether i+1 is less than I (S2850). T If less than (e.g. S2850), the device (10) can return to S2810.

[0343] i+1 is I T If the above is true (NO in S2850), the device can estimate target parameters and communication channel capabilities (S2860). The target parameters may include the range Doppler effect of the beam or the angle of the beam, and the communication channel capabilities may include spectral efficiency.

[0344] Figures 29 to 34 are drawings illustrating the effect of the method in one embodiment of the present disclosure.

[0345] FIG. 29 is a diagram illustrating an example of a channel estimation error according to TPNR according to a conventional method and an embodiment of the present disclosure. FIG. 30 is a diagram illustrating an example of a channel estimation error according to TPNR according to a conventional method and an embodiment of the present disclosure.

[0346] The parameters used in Figures 29 to 30 are as shown in Table 6 below.

[0347] [Table 6]

[0348]

[0349] The existing scanning method estimates the channel by dividing the area of ​​interest -6° to 60° by the number of transmit and receive beamforming vectors as shown in Table 6 using the designed beam pattern, and designating the steering angle as -60°, -36°, -12°, 12°, 36°, and 60°. The channel estimation method uses a method that minimizes the MSE of the minimum standard solution of Equation 6. In the proposed method, the initial , Is and The simulation was conducted assuming that the particles were uniformly distributed.

[0350] The simulation was performed for a total of 10,000 Monte Carlo iterations for each TPNR. In Fig. 30, the Orthogonal Matching Pursuit (OMP) method was used to estimate angles from the estimated channels. Referring to Fig. 30, it can be confirmed that the angle estimation performance of the proposed method using the estimated channels is significantly better than that of the conventional method.

[0351] Fig. 31 is a diagram illustrating an example of a multi-beam pattern using a soft coupling method. Fig. 32 is a diagram illustrating an example of a multi-beam pattern using communication-centric mutual coupling.

[0352] Referring to Figures 31 and 32, one dominant communication direction and a total of 11 steering directions Among the sensing beams having the communication beam and the 8th sensing beam (14.48 ) is a beam pattern for a multi-beam combined with soft combining. Here, the soft combining method has a beam gain of 0.5721 and the communication-centric mutual combining has a beam gain of 0.6874 for the communication direction, which confirms that the communication-centric mutual combining method has a higher beamforming gain in the communication direction than the soft combining. Therefore, it means that designing a multi-beam with the communication-centric mutual combining method can form a beam pattern with a high beam gain for the communication direction.

[0353] FIG. 33 is a diagram illustrating an example of a search probability for a multi-beam according to an embodiment of the present disclosure. FIG. 34 is a diagram illustrating an example of spectral efficiency for a multi-beam according to an embodiment of the present disclosure.

[0354] Figures 33 to 34 are figures analyzing the performance of multi-beams combined using soft combining and communication-centric mutual combining methods using sensing beams designed from sensing beams that minimize the MSE described above.

[0355] Referring to FIGS. 33 and 34, the first graph (L1) and the second graph (L2) may be search probabilities and spectral efficiencies when applying the existing method, and the third graph (L3) and the fourth graph (L4) may be search probabilities and spectral efficiencies when applying the method according to one embodiment of the present disclosure. The first graph (L1) and the third graph (L3) may be cases where multi-beams combined using a soft combining method are used, and the second graph (L2) and the fourth graph (L4) may be cases where multi-beams combined using communication-centric mutual combining are used.

[0356] Referring to FIGS. 33 to 34, it can be seen that the search probability and spectral efficiency of the method according to one embodiment of the present disclosure are higher than the search probability and spectral efficiency of the method according to one embodiment of the present disclosure.

[0357] Figure 35 is a flowchart of a signal transmission and reception method according to one embodiment of the present disclosure.

[0358] In Fig. 35, the first node may be the device (10) described above.

[0359] Referring to FIG. 35, the first node can generate weights for the Tx multi-beam and weights for the Tx sensing beam (S3510).

[0360] The first node can perform channel estimation based on the weights for the Tx multi-beam and the weights for the Tx sensing beam (S3520). The first node can generate a first vector based on the weights for the Tx multi-beam and the weights for the Tx sensing beam, and estimate the channel based on the first vector.

[0361] The first node can perform an update on the weight of the Tx sensing beam (S3530). The first node can obtain the weight of the Rx sensing beam. The first node can obtain the LSV for the weight of the Tx sensing beam and the LSV for the weight of the Rx sensing beam, and the RSV for the weight of the Tx sensing beam and the RSV for the weight of the Rx sensing beam. The first node can obtain a singular value matrix for the weight of the Tx sensing beam based on the LSV and the RSV for the weight of the Tx sensing beam. The second node can obtain a singular value matrix for the weight of the Rx sensing beam based on the LSV and the RSV for the weight of the Rx sensing beam. The first node can perform an update on the weight of the Tx sensing beam based on the singular value matrix for the weight of the Tx sensing beam. The first node can perform an update on the weights of the Rx sensing beams based on the singular value matrix for the weights of the Rx sensing beams.

[0362] The first node can update the weight of the Tx multi-beam based on the Tx communication beam and the Tx sensing beam for which the update was performed (S3540). The first node can update the weight of the Tx multi-beam by performing a summation on the Tx communication beam and the Tx sensing beam for which the update was performed. Here, the summation method can be a communication-centric mutual combining method or a soft combining method, and the Tx communication beam can be related to the Tx multi-beam.

[0363] Meanwhile, if the number of updates performed for the weights of the Tx multi-beam is greater than a preset value, the first node can estimate the target parameters and communication channel capabilities.

[0364] Additionally, the first node can transmit and receive data with the second node based on the Tx multi-beam on which the update is performed.

[0365] The embodiments described above are combinations of the components and features of the present disclosure in a predetermined form. Each component or feature should be considered optional unless explicitly stated otherwise. Each component or feature may be implemented without being combined with other components or features. Furthermore, it is also possible to combine some components and / or features to form an embodiment of the present disclosure. The order of operations described in the embodiments of the present disclosure may be changed. Some components or features of one embodiment may be included in another embodiment or may be replaced with corresponding components or features of another embodiment. It is self-evident that claims that do not have an explicit citation relationship in the scope of the patent may be combined to form an embodiment or may be incorporated as a new claim through a post-application amendment.

[0366] Embodiments according to the present specification may be implemented by various means, for example, hardware, firmware, software, or a combination thereof. In the case of hardware implementation, an embodiment of the present invention may be implemented by one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, etc.

[0367] When implemented via firmware or software, an embodiment of the present specification may be implemented in the form of a module, procedure, function, or the like that performs the functions or operations described above. The software code may be stored in memory and executed by a processor. The memory may be located within or external to the processor and may exchange data with the processor via various known means.

[0368] It will be apparent to those skilled in the art that this specification may be embodied in other specific forms without departing from the essential characteristics thereof. Therefore, the foregoing detailed description should not be construed in any way as limiting but rather as illustrative. The scope of this specification should be determined by a reasonable interpretation of the appended claims, and all changes within the scope of equivalents herein are intended to be included within the scope of this specification.

Claims

1. In a method of operating a first node in a wireless communication system, A step of generating weights for Tx multi-beams and weights for Tx sensing beams; A step of performing channel estimation based on the weights for the Tx multi-beam and the weights for the Tx sensing beam; A step of performing an update of weights for the above Tx sensing beam; A step of performing an update of weights for the Tx multi-beam based on the Tx sensing beam on which the update is performed; and A method comprising the step of transmitting and receiving data based on a second node and the Tx multi-beam on which the update is performed.

2. In paragraph 1, The step of performing the channel estimation based on the weights for the Tx multi-beam and the weights for the Tx sensing beam is: The first node generates a first vector based on the weights for the Tx multi-beam and the weights for the Tx sensing beam; and A method comprising the step of estimating the channel based on the first vector.

3. In paragraph 1, The step of performing the update of the weights for the above Tx sensing beam is: Step of obtaining weights for Rx sensing beams; A step of obtaining LSV (left singular vector) and RSV (right singular vector) of weights for the above Tx sensing beam; A step of obtaining LSV and RSV of weights for the above Rx sensing beam; A step of obtaining a singular value matrix of weights for the Tx sensing beam based on the LSV and the RSV of the weights for the Tx sensing beam; A step of obtaining a singular value matrix of weights for the Rx sensing beam based on the LSV and the RSV of the weights for the Rx sensing beam; and A method further comprising the step of performing an update for said Tx sensing beam based on said singular value matrix of weights for said Tx sensing beam.

4. In paragraph 1, The step of performing an update of the weights for the Tx multi-beam based on the Tx sensing beam on which the above update is performed is as follows. A method comprising the step of combining a Tx communication beam associated with the Tx multi-beam with the Tx sensing beam on which the update is performed.

5. In paragraph 4, The step of combining the Tx communication beam associated with the Tx multi-beam with the Tx sensing beam on which the update is performed, A method performed based on either comm-centric coherent combining or soft combining.

6. In the first node of a wireless communication system, Transmitter and receiver; and Memory containing at least one instruction; comprising at least one processor for performing at least one instruction; At least one of the above commands, A step of generating weights for Tx multi-beams and weights for Tx sensing beams; A step of performing channel estimation based on the weights for the Tx multi-beam and the weights for the Tx sensing beam; A step of performing an update of weights for the above Tx sensing beam; A step of performing an update of weights for the Tx multi-beam based on the Tx sensing beam on which the update is performed; and A first node comprising a second node and a step of transmitting and receiving data based on the Tx multi-beam on which the update is performed.

7. In paragraph 6, The step of performing the channel estimation based on the weights for the Tx multi-beam and the weights for the Tx sensing beam is: The first node generates a first vector based on the weights for the Tx multi-beam and the weights for the Tx sensing beam; and A first node, comprising a step of estimating the channel based on the first vector.

8. In paragraph 6, The step of performing the update of the weights for the above Tx sensing beam is: Step of obtaining weights for Rx sensing beams; A step of obtaining LSV (left singular vector) and RSV (right singular vector) of weights for the above Tx sensing beam; A step of obtaining LSV and RSV of weights for the above Rx sensing beam; A step of obtaining a singular value matrix of weights for the Tx sensing beam based on the LSV and the RSV of the weights for the Tx sensing beam; A step of obtaining a singular value matrix of weights for the Rx sensing beam based on the LSV and the RSV of the weights for the Rx sensing beam; and A first node further comprising the step of performing an update for the Tx sensing beam based on the singular value matrix of weights for the Tx sensing beam.

9. In paragraph 6, The step of performing an update of the weights for the Tx multi-beam based on the Tx sensing beam on which the above update is performed is as follows. A first node comprising a step of combining a Tx communication beam associated with the Tx multi-beam with the Tx sensing beam on which the update is performed.

10. In paragraph 9, The step of combining the Tx communication beam associated with the Tx multi-beam with the Tx sensing beam on which the update is performed, A first node, which is performed based on either comm-centric coherent combining or soft combining methods.

11. A device comprising one or more memories and one or more processors functionally connected to the one or more memories, The above one or more processors are configured such that the device, A step of generating weights for Tx multi-beams and weights for Tx sensing beams; A step of performing channel estimation based on the weights for the Tx multi-beam and the weights for the Tx sensing beam; A step of performing an update of weights for the above Tx sensing beam; A step of performing an update of weights for the Tx multi-beam based on the Tx sensing beam on which the update is performed; and A device operative to perform a step of transmitting and receiving data based on a second node and the Tx multi-beam on which the update is performed.

12. In one or more non-transitory computer-readable media storing one or more instructions, A step of generating weights for Tx multi-beams and weights for Tx sensing beams; A step of performing channel estimation based on the weights for the Tx multi-beam and the weights for the Tx sensing beam; A step of performing an update of weights for the above Tx sensing beam; A step of performing an update of weights for the Tx multi-beam based on the Tx sensing beam on which the update is performed; and A computer-readable medium operable to perform a step of transmitting and receiving data based on a second node and the Tx multi-beam on which the update is performed.

Images