Method executable by user equipment, apparatus, and storage medium

Abstract

Disclosed is a first device, which receives a signal from a second device, calculates a channel matrix for the signal, estimates angle information of an angle of arrival (AoA) and / or an angle of departure (AoD) on the basis of the channel matrix, and transmits the at least one angle information to the second device.

Description

Method, device, and storage medium by user device

[0001] This specification relates to a wireless communication system.

[0002] Various devices and technologies, such as machine-to-machine (M2M) communication, machine type communication (MTC), and devices requiring high data transmission rates like smartphones and tablet PCs (Personal Computers), are emerging and becoming widespread. Consequently, the amount of data required to be processed in cellular networks is increasing very rapidly. To satisfy this rapidly increasing demand for data processing, technologies such as carrier aggregation and cognitive radio are being developed to efficiently utilize more frequency bands, while technologies such as multi-antenna technology and multi-BS cooperation are being developed to increase the data capacity transmitted within a limited frequency range.

[0003] As more communication devices require greater communication capacity, the need for enhanced mobile broadband (eMBB) communication is emerging compared to legacy radio access technology (RAT). In addition, massive machine type communication (mMTC), which connects multiple devices and objects to provide various services anytime and anywhere, is one of the key issues to consider in next-generation communication.

[0004] In addition, discussions are underway regarding communication systems to be designed with user equipment (UE) in mind, which is sensitive to reliability and latency. The introduction of next-generation wireless access technologies is being discussed with consideration of eMBB communication, mMTC, and ultra-reliable and low-latency communication (URLLC).

[0005] The technical objective is to provide a method for estimating angle information more accurately and efficiently.

[0006] The technical problems that this specification aims to solve are not limited to those mentioned above, and other technical problems not mentioned will be clearly understood by those skilled in the art related to this specification from the detailed description below.

[0007] A method by a first device according to one aspect comprises: receiving a signal from a second device; calculating a channel matrix for the signal; estimating at least one angle information among Angle of Arrival (AoA) and Angle of Departure (AoD) based on the channel matrix; and transmitting the at least one angle information to the second device; wherein the at least one angle information may be estimated through at least one estimation method determined among a first estimation method using a deep learning model and a second estimation method using a parameter estimation algorithm based on a channel state associated with the signal.

[0008] Alternatively, the first estimation method is characterized by estimating the AoA and AoD using a complex neural network model including a complex-value convolution layer and CReLU (complex activation functions).

[0009] Alternatively, the above parameter estimation algorithm is characterized by being an algorithm that estimates parameters using a predefined mathematical model based on antenna steering vectors and array geometry.

[0010] Alternatively, based on the change in the channel state being less than a first threshold, the at least one angle information is estimated using only the second estimation method among the first estimation method and the second estimation method.

[0011] Alternatively, based on the change in the channel state being greater than or equal to a second threshold, the at least one angle information is estimated using only the second estimation method among the first estimation method and the second estimation method.

[0012] Alternatively, based on the change in the channel state being greater than or equal to a first threshold and less than a second threshold, the at least one angle information is estimated using both the first estimation method and the second estimation method.

[0013] Alternatively, based on the change in the channel state being greater than or equal to a first threshold and less than a second threshold, the first device estimates first angle information using the first estimation method, estimates second angle information using the second estimation method, and corrects the first angle information based on the second angle information.

[0014] Alternatively, the above at least one angle information is characterized by including the corrected first angle information.

[0015] Alternatively, the at least one angle information is characterized by being transmitted to the second device for synchronization related to the performance of bistatic ISAC (Integrated Sensing and Communication).

[0016] Depending on other aspects, a non-transient computer-readable storage medium may be provided that records instructions for performing the method by the first device described above.

[0017] Depending on another aspect, a first device for performing the method described above may be provided.

[0018] Depending on another aspect, a processing device that controls a first device performing the method described above may be provided.

[0019] A method by a second device according to another aspect comprises: transmitting a first signal to a first device for calculating a channel matrix; receiving a second signal containing at least one angle information among an Angle of Arrival (AoA) and an Angle of Departure (AoD) calculated based on the channel matrix; and synchronizing with the first device based on the at least one angle information for performing bistatic Integrated Sensing and Communication (ISAC); wherein the at least one angle information can be estimated through at least one estimation method determined among a first estimation method using a deep learning model and a second estimation method using a parameter estimation algorithm based on a channel state associated with the signal.

[0020] Depending on other aspects, a second device for performing the method described above may be provided.

[0021] The above-mentioned problem-solving methods are merely some of the examples in this specification, and various examples reflecting the technical features of this specification can be derived and understood by a person skilled in the art based on the following detailed description.

[0022] According to some implementations of the present specification, angle information related to ISAC can be estimated more accurately and efficiently.

[0023] The effects according to this specification are not limited to those mentioned above, and other unmentioned effects will be clearly understood by those skilled in the art related to this specification from the detailed description below.

[0024] The accompanying drawings, which are included as part of the detailed description to aid in understanding the implementations of this specification, provide examples of the implementations of this specification and describe the implementations of this specification together with the detailed description:

[0025] FIG. 1 illustrates an example of a communication system 1 to which the implementations of the present specification are applied;

[0026] FIG. 2 is a block diagram illustrating examples of communication devices capable of performing the method according to the present specification;

[0027] FIG. 3 illustrates another example of a wireless device capable of performing the implementation(s) of the present specification;

[0028] FIG. 4 illustrates an example of a frame structure available in a 3rd generation partnership project (3GPP)-based wireless communication system;

[0029] FIG. 5 illustrates physical channels used in a 3rd generation partnership project (3GPP)-based communication system, which is an example of a wireless communication system, and the signal transmission / reception process using them;

[0030] FIG. 6 illustrates an arbitrary connection process that may be applied to the implementation(s) of the present specification;

[0031] FIG. 7 illustrates a perceptron structure used in an artificial neural network;

[0032] FIG. 8 illustrates a multilayer perceptron structure;

[0033] Figure 9 illustrates a convolutional neural network (CNN) structure;

[0034] FIG. 10 illustrates a filter operation in a CNN;

[0035] Figure 11 illustrates the concept of step-to-step learning with backpropagation applied;

[0036] Figure 12 is an example of a method for calculating gradients in a neural network;

[0037] Figure 13 illustrates examples of wireless sensing modes supported by ISAC.

[0038] Figures 14 and 15 show an example of how ISAC is applied to a 3GPP wireless communication system.

[0039] Figure 16 is a diagram illustrating how BS estimates AoA and AoD for UE by using a parameter estimation algorithm and a DL-based model in parallel.

[0040] Figure 17 is a diagram illustrating a method for estimating AoA / AoD using a parameter estimation algorithm.

[0041] Figure 18 is a diagram illustrating a method for estimating AoA / AoD using a complex neural network model.

[0042] FIG. 19 is a diagram illustrating a method for transmitting and receiving ISAC-related signals between a BS and a UE.

[0043] FIG. 20 is a diagram illustrating a method for a first device to estimate an AoA / AoD related to a second device.

[0044] Hereinafter, implementations according to the present specification will be described in detail with reference to the accompanying drawings. The detailed description disclosed below, together with the accompanying drawings, is intended to describe exemplary implementations of the present specification and is not intended to represent the only form in which the present specification may be practiced. The following detailed description includes specific details to provide a complete understanding of the present specification. However, those skilled in the art will know that the present specification may be practiced without such specific details.

[0045] In some cases, to avoid ambiguity of the concepts of this specification, known structures and devices may be omitted or illustrated in the form of block diagrams focusing on the core functions of each structure and device. Additionally, throughout this specification, the same reference numerals are used to describe identical components.

[0046] The techniques, devices, and systems described below can be applied to various wireless multiple access systems. Examples of multiple access systems include CDMA (code division multiple access) systems, FDMA (frequency division multiple access) systems, TDMA (time division multiple access) systems, OFDMA (orthogonal frequency division multiple access) systems, SC-FDMA (single carrier frequency division multiple access) systems, and MC-FDMA (multi carrier frequency division multiple access) systems. CDMA can be implemented in wireless technologies such as UTRA (Universal Terrestrial Radio Access) or CDMA2000. TDMA can be implemented in wireless technologies such as GSM (Global System for Mobile communication), GPRS (General Packet Radio Service), and EDGE (Enhanced Data Rates for GSM Evolution) (i.e., GERAN). OFDMA can be implemented in wireless technologies such as IEEE (Institute of Electrical and Electronics Engineers) 802.11 (WiFi), IEEE 802.16 (WiMAX), IEEE 802-20, and E-UTRA (evolved-UTRA). UTRA is part of UMTS (Universal Mobile Telecommunication System), and 3GPP (3rd Generation Partnership Project) LTE (Long Term Evolution) is part of E-UMTS that utilizes E-UTRA.3GPP LTE adopts OFDMA for the downlink (DL) and SC-FDMA for the uplink (UL). LTE-A (LTE-advanced) is an evolved form of 3GPP LTE.

[0047] For convenience of explanation, the following description assumes that this specification applies to 3GPP-based communication systems, e.g., LTE and NR. However, the technical features of this specification are not limited thereto. For example, even though the following detailed description is based on a mobile communication system corresponding to a 3GPP LTE / NR system, it is applicable to any other mobile communication system except for matters specific to 3GPP LTE / NR.

[0048] For terms and technologies used in this specification that are not specifically described, reference may be made to 3GPP-based standard documents, for example, 3GPP TS 36.211, 3GPP TS 36.212, 3GPP TS 36.213, 3GPP TS 36.321, 3GPP TS 36.300 and 3GPP TS 36.331, 3GPP TS 37.213, 3GPP TS 38.211, 3GPP TS 38.212, 3GPP TS 38.213, 3GPP TS 38.214, 3GPP TS 38.300, 3GPP TS 38.304, 3GPP TS 38.331, etc.

[0049] In the examples of this specification set forth below, the expression that the device "assumes" may mean that the entity transmitting the channel transmits the channel in accordance with said "assume." It may mean that the entity receiving the channel receives or decodes the channel in a form that conforms to said "assume," under the premise that the channel was transmitted in accordance with said "assume."

[0050] In this specification, ' / ' may mean 'and / or'. For example, cell DTX / DRX may mean cell DTX and / or cell DRX.

[0051] In this specification, a UE may be fixed or mobile and includes various devices that communicate with a base station (BS) to transmit and / or receive user data and / or various control information. A UE may be referred to as Terminal Equipment, Mobile Station (MS), Mobile Terminal (MT), User Terminal (UT), Subscribe Station (SS), wireless device, Personal Digital Assistant (PDA), wireless modem, handheld device, etc. Additionally, in this specification, a BS generally refers to a fixed station that communicates with a UE and / or other BSs, and exchanges various data and control information by communicating with a UE and other BSs. A BS may be referred to by other terms such as Advanced Base Station (ABS), Node-B (NB), eNB (evolved-NodeB), Base Transceiver System (BTS), Access Point, Processing Server (PS), etc. In particular, BSs of UTRAN are called Node-Bs, BSs of E-UTRAN are called eNBs, and BSs of new radio access technology networks are called gNBs. For convenience of explanation, BSs will be collectively referred to as BSs regardless of the type or version of the communication technology.

[0052] In this specification, the term "node" refers to a fixed point capable of transmitting / receiving wireless signals by communicating with a UE. Various types of BSs may be used as nodes regardless of their designation. For example, a BS, NB, eNB, pico-cell eNB (PeNB), home eNB (HeNB), relay, repeater, etc., may serve as a node. Additionally, a node may not be a BS. For example, it may be a radio remote head (RRH) or a radio remote unit (RRU). RRHs, RRUs, etc. generally have a power level lower than that of a BS. Since an RRH or RRU (or RRH / RRU) is generally connected to a BS via a dedicated line such as an optical cable, cooperative communication between an RRH / RRU and a BS can be performed more smoothly compared to cooperative communication between BSs connected via a wireless line. At least one antenna is installed at a node. This antenna may refer to a physical antenna, an antenna port, a virtual antenna, or an antenna group. Nodes are also referred to as points.

[0053] In this specification, the term "cell" refers to a specific geographical area where one or more nodes provide communication services. Accordingly, in this specification, communicating with a specific cell may mean communicating with a BS or node that provides communication services to said specific cell. Furthermore, the downlink / uplink signal of a specific cell refers to a downlink / uplink signal from to or to the BS or node that provides communication services to said specific cell. A cell that provides uplink / downlink communication services to a UE is specifically referred to as a serving cell. Additionally, the channel state / quality of a specific cell refers to the channel state / quality of a channel or communication link formed between the BS or node providing communication services to said specific cell and the UE. In a 3GPP-based communication system, a UE can measure the downlink channel state from a specific node using the CRS(s) transmitted by the antenna port(s) of the specific node over the CRS (Cell-specific Reference Signal) resource assigned to the specific node and / or the CSI-RS(s) transmitted over the CSI-RS (Channel State Information Reference Signal) resource.

[0054] Meanwhile, 3GPP-based communication systems use the concept of a cell to manage wireless resources, and a cell associated with wireless resources is distinguished from a cell in a geographical area.

[0055] A “cell” of a geographical area can be understood as the coverage over which a node can provide services using a carrier wave, and a “cell” of a wireless resource is associated with the bandwidth (BW), which is the frequency range configured by said carrier wave. Since downlink coverage, which is the range over which a node can transmit a valid signal, and uplink coverage, which is the range over which a valid signal can be received from a UE, depend on the carrier wave carrying the signal, the coverage of a node is also associated with the coverage of the “cell” of the wireless resource used by said node. Therefore, the term “cell” can be used to refer sometimes to the coverage of a service by a node, sometimes to a wireless resource, and sometimes to the range over which a signal using said wireless resource can reach with effective strength.

[0056] Meanwhile, 3GPP communication standards use the concept of a cell to manage radio resources. A "cell" associated with radio resources is defined as a combination of downlink resources (DL resources) and uplink resources (UL resources), that is, a combination of a DL component carrier (CC) and a UL CC. A cell can be configured as a DL resource alone or as a combination of a DL resource and a UL resource. Where carrier aggregation is supported, the linkage between the carrier frequency of a DL resource (or DL ​​CC) and the carrier frequency of a UL resource (or UL CC) can be indicated by system information. For example, the combination of DL resources and UL resources can be indicated by a System Information Block Type 2 (SIB2) linkage. Here, the carrier frequency may be equal to or different from the center frequency of each cell or CC. When Carrier Aggregation (CA) is established, the UE has only one Radio Resource Control (RRC) connection with the network. One serving cell provides Non-Access Stratum (NAS) mobility information during RRC establishment / re-establishment / handover, and one serving cell provides security input during RRC re-establishment / handover. This cell is called a primary cell (Pcell). A Pcell is a cell operating on the primary frequency where the UE performs the initial connection establishment procedure or initiates the connection re-establishment procedure.Depending on the UE capability, secondary cells (Scells) can be configured to form a set of serving cells together with Pcells. Scells can be configured after a Radio Resource Control (RRC) connection is established and are cells that provide additional radio resources in addition to the resources of special cells (SpCells). The carrier corresponding to a Pcell in the downlink is called the Downlink Primary CC (DL PCC), and the carrier corresponding to a Pcell in the uplink is called the UL Primary CC (UL PCC). The carrier corresponding to an Scell ​​in the downlink is called the DL Secondary CC (DL SCC), and the carrier corresponding to the Scell ​​in the uplink is called the UL Secondary CC (UL SCC).

[0057] In a wireless communication system, the UE receives information from the BS via the downlink (DL) and transmits information to the BS via the uplink (UL). The information transmitted and / or received by the BS and the UE includes data and various control information, and various physical channels exist depending on the type and purpose of the information they transmit and / or receive.

[0058] 3GPP-based communication standards define downlink physical channels corresponding to resource elements that carry information originating from upper layers, and downlink physical signals corresponding to resource elements used by the physical layer but not carrying information originating from upper layers. For example, the physical downlink shared channel (PDSCH), physical broadcast channel (PBCH), and physical downlink control channel (PDCCH) are defined as downlink physical channels, while the reference signal and synchronization signal are defined as downlink physical signals. The reference signal (RS), also referred to as a pilot, refers to a signal of a specific, predefined waveform known to both the BS and the UE. For example, the demodulation reference signal (DMRS), channel state information RS (CSI-RS), and positioning reference signal (PRS) are defined as downlink reference signals. 3GPP-based communication standards define uplink physical channels corresponding to resource elements that carry information originating from upper layers, and uplink physical signals corresponding to resource elements that are used by the physical layer but do not carry information originating from upper layers.For example, the physical uplink shared channel (PUSCH), physical uplink control channel (PUCCH), and physical random access channel (PRACH) are defined as uplink physical channels, and the demodulation reference signal (DMRS) for uplink control / data signals and the sounding reference signal (SRS) used for uplink channel measurement are defined.

[0059] In this specification, PDCCH (Physical Downlink Control Channel) refers to a set of time-frequency resources (e.g., resource elements (REs)) carrying DCI (Downlink Control Information), and PDSCH (Physical Downlink Shared Channel) refers to a set of time-frequency resources carrying downlink data. Additionally, PUCCH (Physical Uplink Control Channel), PUSCH (Physical Uplink Shared Channel), and PRACH (Physical Random Access Channel) respectively refer to sets of time-frequency resources carrying UCI (Uplink Control Information), uplink data, and random access signals. In the following, the expression that a user device transmits / receives PUCCH / PUSCH / PRACH is used in the same sense as transmitting / receiving uplink control information / uplink data / random access signals on or through PUCCH / PUSCH / PRACH, respectively. In addition, the expression that BS transmits / receives PBCH / PDCCH / PDSCH is used with the same meaning as transmitting broadcast information / downlink control information / downlink data on or through PBCH / PDCCH / PDSCH, respectively.

[0060] In this specification, a radio resource (e.g., time-frequency resource) scheduled or set by a BS for a UE for the transmission or reception of PUCCH / PUSCH / PDSCH is also referred to as a PUCCH / PUSCH / PDSCH resource.

[0061] Since the communication device receives a synchronization signal (SS), DMRS, CSI-RS, PRS, PBCH, PDCCH, PDSCH, PUSCH, and / or PUCCH in the form of radio signals on the cell, it is not possible to selectively receive only radio signals containing only a specific physical channel or a specific physical signal through the RF receiver, or to selectively receive only radio signals excluding only a specific physical channel or a specific physical signal through the RF receiver. In actual operation, the communication device first receives radio signals on the cell through the RF receiver, converts the radio signals, which are RF band signals, into baseband signals, and uses one or more processors to decode the physical signals and / or physical channels within the baseband signals. Accordingly, in some implementations of this specification, not receiving a physical signal and / or a physical channel may actually mean that the communication device does not receive wireless signals containing the physical signal and / or physical channel at all, but rather does not attempt to recover the physical signal and / or physical channel from the wireless signals, for example, not attempt to decode the physical signal and / or physical channel.

[0062] As more communication devices require larger communication capacities, the need for improved mobile broadband communication compared to existing radio access technology (RAT) is emerging. Furthermore, massive MTC, which connects multiple devices and objects to provide various services anytime and anywhere, is also one of the major issues to be considered in next-generation communication. In addition, communication system designs that consider reliability and latency-sensitive services / UEs are being discussed. Accordingly, the introduction of next-generation RATs that consider advanced mobile broadband communication, massive MTC, and URLLC (Ultra-Reliable and Low Latency Communication) is being discussed. Currently, 3GPP is conducting studies on next-generation mobile communication systems following the EPC. For convenience, this specification refers to the technology as new RAT (NR) or 5G RAT, and systems that use or support NR are referred to as NR systems.

[0063] FIG. 1 illustrates an example of a communication system 1 to which the implementations of the present specification apply. Referring to FIG. 1, the communication system (1) to which the present specification applies includes a wireless device, a BS, and a network. Here, the wireless device refers to a device that performs communication using wireless access technology (e.g., 5G NR (New RAT), LTE (e.g., E-UTRA)) and may be referred to as a communication / wireless / 5G device. Although not limited thereto, the wireless device may include a robot (100a), a vehicle (100b-1, 100b-2), an XR (eXtended Reality) device (100c), a hand-held device (100d), a home appliance (100e), an IoT (Internet of Thing) device (100f), and an AI device / server (400). For example, the vehicle may include a vehicle equipped with wireless communication capabilities, an autonomous vehicle, a vehicle capable of performing inter-vehicle communication, etc. Here, vehicles may include UAVs (Unmanned Aerial Vehicles) (e.g., drones). XR devices include AR (Augmented Reality) / VR (Virtual Reality) / MR (Mixed Reality) devices and may be implemented in the form of HMDs (Head-Mounted Devices), HUDs (Head-Up Displays) equipped in vehicles, televisions, smartphones, computers, wearable devices, home appliances, digital signage, vehicles, robots, etc. Portable devices may include smartphones, smartpads, wearable devices (e.g., smartwatches, smart glasses), computers (e.g., laptops, etc.). Home appliances may include TVs, refrigerators, washing machines, etc. IoT devices may include sensors, smart meters, etc. For example, BS and networks may be implemented as wireless devices, and specific wireless devices may operate as BS / network nodes to other wireless devices.

[0064] Wireless devices (100a to 100f) can be connected to a network (300) via a BS (200). Artificial Intelligence (AI) technology may be applied to the wireless devices (100a to 100f), and the wireless devices (100a to 100f) can be connected to an AI server (400) via the network (300). The network (300) can be configured using a 3G network, a 4G (e.g., LTE) network, or a 5G (e.g., NR) network. The wireless devices (100a to 100f) may communicate with each other via the BS (200) / network (300), but they may also communicate directly (e.g., sidelink communication) without using the BS / network. For example, vehicles (100b-1, 100b-2) can communicate directly (e.g., V2V (Vehicle to Vehicle) / V2X (Vehicle to everything) communication). Also, IoT devices (e.g., sensors) can communicate directly with other IoT devices (e.g., sensors) or other wireless devices (100a to 100f).

[0065] Wireless communication / connection (150a, 150b) can be established between wireless devices (100a~100f) / BS (200) and BS (200) / wireless devices (100a~100f). Here, the wireless communication / connection can be established through various wireless access technologies (e.g., 5G NR), such as uplink / downlink communication (150a) and sidelink communication (150b) (or D2D communication). Through the wireless communication / connection (150a, 150b), wireless devices and BS / wireless devices can transmit / receive wireless signals to / from each other. To this end, based on various proposals of this specification, at least some of the following may be performed: a process for setting various configuration information for transmitting / receiving wireless signals, a process for various signal processing (e.g., channel encoding / decoding, modulation / demodulation, resource mapping / demapping, etc.), and a resource allocation process.

[0066] FIG. 2 is a block diagram illustrating examples of communication devices capable of performing the method according to the present specification. Referring to FIG. 2, a first wireless device (100) and a second wireless device (200) can transmit and / or receive wireless signals through various wireless access technologies (e.g., LTE, NR). Here, {the first wireless device (100), the second wireless device (200)} may correspond to {wireless device (100x), BS (200)} and / or {wireless device (100x), wireless device (100x)} of FIG. 1.

[0067] The first wireless device (100) includes one or more processors (102) and one or more memories (104), and may additionally include one or more transceivers (106) and / or one or more antennas (108). The processor (102) controls the memory (104) and / or transceivers (106) and may be configured to implement the functions, procedures and / or methods described / suggested below. For example, the processor (102) may process information within the memory (104) to generate a first information / signal and then transmit a wireless signal containing the first information / signal through the transceiver (106). Additionally, the processor (102) may receive a wireless signal containing a second information / signal through the transceiver (106) and then store information obtained from the signal processing of the second information / signal in the memory (104). Memory (104) may be connected to the processor (102) and may store various information related to the operation of the processor (102). For example, memory (104) may store software code containing instructions for performing some or all of the processes controlled by the processor (102) or for performing the procedures and / or methods described / suggested below. Here, the processor (102) and memory (104) may be part of a communication modem / circuit / chip designed to implement wireless communication technology (e.g., LTE, NR). A transceiver (106) may be connected to the processor (102) and may transmit and / or receive wireless signals through one or more antennas (108). The transceiver (106) may include a transmitter and / or receiver. The transceiver (106) may be interchangeably used with an RF (Radio Frequency) unit. In this specification, a wireless device may mean a communication modem / circuit / chip.

[0068] The second wireless device (200) includes one or more processors (202) and one or more memories (204), and may additionally include one or more transceivers (206) and / or one or more antennas (208). The processor (202) controls the memory (204) and / or transceivers (206) and may be configured to implement the functions, procedures and / or methods described / suggested below. For example, the processor (202) may process information within the memory (204) to generate a third information / signal and then transmit a wireless signal containing the third information / signal through the transceiver (206). Additionally, the processor (202) may receive a wireless signal containing a fourth information / signal through the transceiver (206) and then store information obtained from the signal processing of the fourth information / signal in the memory (204). Memory (204) may be connected to the processor (202) and may store various information related to the operation of the processor (202). For example, memory (204) may store software code containing instructions for performing some or all of the processes controlled by the processor (202) or for performing the procedures and / or methods described / suggested below. Here, the processor (202) and memory (204) may be part of a communication modem / circuit / chip designed to implement wireless communication technology (e.g., LTE, NR). A transceiver (206) may be connected to the processor (202) and may transmit and / or receive wireless signals through one or more antennas (208). The transceiver (206) may include a transmitter and / or receiver. The transceiver (206) may be interchangeable with an RF unit. In this specification, a wireless device may mean a communication modem / circuit / chip.

[0069] The wireless communication technology implemented in the wireless device (100, 200) of this specification may include LTE, NR, and 6G, as well as Narrowband Internet of Things for low-power communication. In this case, for example, NB-IoT technology may be an example of LPWAN (Low Power Wide Area Network) technology and may be implemented according to standards such as LTE Cat NB1 and / or LTE Cat NB2, but is not limited to the names mentioned above. Additionally, or generally, the wireless communication technology implemented in the wireless device (XXX, YYY) of this specification may perform communication based on LTE-M technology. In this case, for example, LTE-M technology may be an example of LPWAN technology and may be referred to by various names such as eMTC (enhanced Machine Type Communication). For example, LTE-M technology may be implemented in 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 names mentioned above. Additionally or generally, wireless communication technology implemented in the wireless device (XXX, YYY) of this specification may include at least one of ZigBee, Bluetooth, and Low Power Wide Area Network (LPWAN) with consideration for low-power communication, and is not limited to the names mentioned above. As an example, ZigBee technology can create personal area networks (PANs) related to small / low-power digital communication based on various standards such as IEEE 802.15.4, and may be referred to by various names.

[0070] Hereinafter, hardware elements of the wireless device (100, 200) will be described in more detail. Although not limited thereto, one or more protocol layers may be implemented by one or more processors (102, 202). For example, one or more processors (102, 202) may implement one or more layers (e.g., functional layers such as a physical (PHY) layer, a medium access control (MAC) layer, a radio link control (RLC) layer, a packet data convergence protocol (PDCP) layer, a radio resource control (RRC) layer, and a service data adaptation protocol (SDAP). One or more processors (102, 202) may generate one or more protocol data units (PDU) and / or one or more service data units (SDU) according to the functions, procedures, proposals and / or methods disclosed herein. One or more processors (102, 202) may generate messages, control information, data, or information according to the functions, procedures, proposals, and / or methods disclosed in this specification. One or more processors (102, 202) may generate a signal (e.g., baseband signal) containing a PDU, SDU, message, control information, data, or information according to the functions, procedures, proposals, and / or methods disclosed in this specification and provide it to one or more transceivers (106, 206). One or more processors (102, 202) may receive a signal (e.g., baseband signal) from one or more transceivers (106, 206) and may obtain a PDU, SDU, message, control information, data, or information according to the functions, procedures, proposals, and / or methods disclosed in this specification.

[0071] One or more processors (102, 202) may be referred to as a controller, microcontroller, microprocessor, or microcomputer. One or more processors (102, 202) may be implemented by hardware, firmware, software, or a combination thereof. For example, one or more Application Specific Integrated Circuits (ASICs), one or more Digital Signal Processors (DSPs), one or more Digital Signal Processing Devices (DSPDs), one or more Programmable Logic Devices (PLDs), or one or more Field Programmable Gate Arrays (FPGAs) may be included in one or more processors (102, 202). The functions, procedures, proposals, and / or methods disclosed herein may be implemented using firmware or software, and the firmware or software may be implemented to include modules, procedures, functions, etc. Firmware or software configured to perform the functions, procedures, proposals, and / or methods disclosed in this specification may be included in one or more processors (102, 202) or stored in one or more memories (104, 204) and driven by one or more processors (102, 202). The functions, procedures, proposals, and / or methods disclosed in this specification may be implemented using firmware or software in the form of code, instructions, and / or sets of instructions.

[0072] One or more memories (104, 204) may be connected to one or more processors (102, 202) and may store various forms of data, signals, messages, information, programs, codes, instructions, and / or commands. One or more memories (104, 204) may be composed of ROM, RAM, EPROM, flash memory, hard drive, registers, cache memory, computer read storage media, and / or combinations thereof. One or more memories (104, 204) may be located inside and / or outside of one or more processors (102, 202). Additionally, one or more memories (104, 204) may be connected to one or more processors (102, 202) through various technologies such as wired or wireless connections.

[0073] One or more transceivers (106, 206) may transmit user data, control information, wireless signals / channels, etc., as mentioned in the methods and / or operation flowcharts, etc., of this specification to one or more other devices. One or more transceivers (106, 206) may receive user data, control information, wireless signals / channels, etc., as mentioned in the functions, procedures, proposals, methods and / or operation flowcharts, etc., disclosed in this specification from one or more other devices. For example, one or more transceivers (106, 206) may be connected to one or more processors (102, 202) and may transmit and / or receive wireless signals. For example, one or more processors (102, 202) may control one or more transceivers (106, 206) to transmit user data, control information, or wireless signals to one or more other devices. Additionally, one or more processors (102, 202) may control one or more transceivers (106, 206) to receive user data, control information, or wireless signals from one or more other devices. Additionally, one or more transceivers (106, 206) may be connected to one or more antennas (108, 208), and one or more transceivers (106, 206) may be configured to transmit and / or receive user data, control information, wireless signals / channels, etc., as mentioned in the functions, procedures, proposals, methods, and / or operation flowcharts disclosed herein through one or more antennas (108, 208). In this specification, one or more antennas may be a plurality of physical antennas or a plurality of logical antennas (e.g., antenna ports). One or more transceivers (106, 206) can convert the received wireless signal / channel, etc. from an RF band signal to a baseband signal in order to process the received user data, control information, wireless signal / channel, etc. using one or more processors (102, 202).One or more transceivers (106, 206) can convert user data, control information, wireless signals / channels, etc. processed using one or more processors (102, 202) from baseband signals to RF band signals. To this end, one or more transceivers (106, 206) may include (analog) oscillators and / or filters.

[0074] FIG. 3 illustrates another example of a wireless device capable of performing the implementation(s) of the present specification. Referring to FIG. 3, the wireless device (100, 200) corresponds to the wireless device (100, 200) of FIG. 2 and may be composed of various elements, components, units / parts, and / or modules. For example, the wireless device (100, 200) may include a communication unit (110), a control unit (120), a memory unit (130), and additional elements (140). The communication unit may include a communication circuit (112) and transceiver(s) (114). For example, the communication circuit (112) may include one or more processors (102, 202) and / or one or more memories (104, 204) of FIG. 2. For example, the transceiver(s) (114) may include one or more transceivers (106, 206) and / or one or more antennas (108, 208) of FIG. 2. The control unit (120) is electrically connected to the communication unit (110), the memory unit (130), and additional elements (140) and controls the general operation of the wireless device. For example, the control unit (120) may control the electrical / mechanical operation of the wireless device based on a program / code / command / information stored in the memory unit (130). Additionally, the control unit (120) may transmit information stored in the memory unit (130) to the outside (e.g., another communication device) via a wireless / wired interface through the communication unit (110), or store information received from the outside (e.g., another communication device) via a wireless / wired interface through the communication unit (110) in the memory unit (130).

[0075] The additional element (140) may be configured in various ways depending on the type of wireless device. For example, the additional element (140) may include at least one of a power unit / battery, an input / output unit (I / O unit), a driving unit, and a computing unit. Although not limited thereto, the wireless device may be implemented in the form of a robot (Fig. 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 UE for digital broadcasting, a holographic device, a public safety device, an MTC device, a medical device, a fintech device (or financial device), a security device, a climate / environment device, an AI server / device (Fig. 1, 400), a BS (Fig. 1, 200), a network node, etc. Depending on the use—e.g., service—the wireless device may be movable or used in a fixed location.

[0076] In FIG. 3, various elements, components, units / parts, and / or modules within the wireless device (100, 200) may be entirely interconnected via a wired interface, or at least a portion may be wirelessly connected via a communication unit (110). For example, within the wireless device (100, 200), the control unit (120) and the communication unit (110) may be wired, and the control unit (120) and the first unit (e.g., 130, 140) may be wirelessly connected via the communication unit (110). Additionally, each element, component, unit / part, and / or module within the wireless device (100, 200) may include one or more additional elements. For example, the control unit (120) may be composed of one or more sets of processors. For example, the control unit (120) may be composed of a set of a communication control processor, an application processor, an Electronic Control Unit (ECU), a graphics processing processor, a memory control processor, etc. As another example, the memory unit (130) may be composed of RAM (Random Access Memory), DRAM (Dynamic RAM), ROM (Read Only Memory), flash memory, volatile memory, non-volatile memory, transitory memory, non-transitory memory, and / or a combination thereof.

[0077] In this specification, at least one memory (e.g., 104 or 204) may store instructions or programs, and said instructions or programs may, when executed, cause at least one processor operablely connected to said at least one memory to perform operations according to some embodiments or implementations of this specification.

[0078] In this specification, a computer-readable (non-volatile or non-transient) storage medium may store at least one instruction or computer program, and when executed by at least one processor, said at least one instruction or computer program may cause said at least one processor to perform operations according to some embodiments or implementations of this specification.

[0079] In this specification, a processing device or apparatus may include at least one processor and at least one computer memory connectable to said at least one processor. said at least one computer memory may store instructions or programs, and said instructions or programs, when executed, may cause at least one processor operablely connected to said at least one memory to perform operations according to some embodiments or implementations of this specification.

[0080] In this specification, a computer program may include program code that is stored on at least one computer-readable (non-volatile) storage medium and, when executed, performs operations according to some implementations of this specification or causes at least one processor to perform operations according to some implementations of this specification. The computer program may be provided in the form of a computer program product. The computer program product may include at least one computer-readable (non-volatile) storage medium.

[0081] A communication device of the present specification comprises at least one processor; and at least one computer memory operably connected to the at least one processor and storing instructions that, when executed, cause the at least one processor to perform operations according to the examples(s) of the present specification described below.

[0082] Figure 4 illustrates an example of a frame structure available in a 3GPP-based wireless communication system.

[0083] The structure of the frame in Fig. 4 is merely an example, and the number of subframes, slots, and symbols in the frame can be varied. In an NR system, OFDM numerology (e.g., subcarrier spacing (SCS)) may be set differently among multiple cells aggregated to a single UE. Accordingly, the (absolute time) duration of a time resource (e.g., subframe, slot, or transmission time interval (TTI)) consisting of the same number of symbols may be set differently among the aggregated cells. Here, symbols may include OFDM symbols (or cyclic prefix-orthogonal frequency division multiplexing (CP-OFDM) symbols) and SC-FDMA symbols (or discrete Fourier transform-spread-OFDM (DFT-s-OFDM) symbols). In this specification, symbols, OFDM-based symbols, OFDM symbols, CP-OFDM symbols, and DFT-s-OFDM symbols refer to They can be substituted for each other.

[0084] Referring to Fig. 4, uplink and downlink transmissions in an NR system are organized into frames. Each frame is T f= (△f max *N f / 100)*T c = It has a duration of 10 ms and is divided into two half-frames, each with a duration of 5 ms. Here, T is the basic time unit for NR. c = 1 / (△f max *N f ) and, △f max = 480*10 3 It is Hz, and N f = 4096. For reference, T is the standard time unit for LTE. s = 1 / (△f ref *N f,ref ) and, △f ref = 15*10 3 It is Hz, and N f,ref =2048. T s Wow T c is a constant κ = T s / T c It has a relationship of = 64. Each half-frame consists of 5 subframes, and the period T of a single subframe. sf is 1ms. Subframes are further divided into slots, and the number of slots within a subframe depends on the subcarrier spacing. Each slot consists of 14 or 12 OFDM symbols based on a cyclic prefix. For a normal cyclic prefix (CP), each slot consists of 14 OFDM symbols, while for an extended CP, each slot consists of 12 OFDM symbols. The above numerology is an exponentially scalable subcarrier spacing △f = 2 u It depends on 15 kHz. The following table shows the subcarrier spacing △f = 2 for normalized CP. u *Number of OFDM symbols per slot according to 15 kHz (N slot symb ), number of slots per frame (N frame,uslot ) and the number of slots per subframe (N subframe,u slot It represents ).

[0085]

[0086] The following table shows the subcarrier spacing △f = 2 for extended CP. u This shows the number of OFDM symbols per slot, the number of slots per frame, and the number of slots per subframe according to *15 kHz.

[0087]

[0088] For a subcarrier interval setting u, the slots are arranged in increasing order n within the subframe. u s ∈ {0, ..., nsubframe,u slot - 1} and n in increasing order within the frame u s,f ∈ {0, ..., n frame,u slot - 1} is numbered.

[0089] A slot contains multiple (e.g., 14 or 12) symbols in the time domain. For each numeral (e.g., subcarrier interval) and carrier, a common resource block (CRB) N indicated by upper-layer signaling (e.g., radio resource control (RRC) signaling) start,u grid Starting from,N size,u grid,x *N RB sc individual subcarriers and N subframe,u symb A resource grid of N OFDM symbols is defined. Here, N size,u grid,x is the number of resource blocks (RB) in the resource grid, and the subscript x is DL for downlinks and UL for uplinks. RBsc is the number of subcarriers per RB, and in 3GPP-based wireless communication systems, N RB sc is typically 12. There is one resource grid for a given antenna port p, subcarrier spacing configuration u, and transmission direction (DL or UL). Carrier bandwidth N for subcarrier spacing configuration u. size,u grid This is given to the UE by upper-layer parameters (e.g., RRC parameters) from the network. Each element within the resource grid for antenna port p and subcarrier spacing u is referred to as a resource element (RE), and one complex symbol can be mapped to each resource element. Each resource element within the resource grid is uniquely identified by an index k in the frequency domain and an index l indicating the symbol position relative to a reference point in the time domain. In an NR system, RBs are defined by 12 consecutive subcarriers in the frequency domain. In an NR system, RBs can be classified into Common Resource Blocks (CRBs) and Physical Resource Blocks (PRBs). CRBs are numbered upwards from 0 in the frequency domain for subcarrier spacing u. The center of subcarrier 0 of CRB 0 for subcarrier spacing u coincides with 'Point A', which is the common reference point for the resource block grids. The PRBs for the subcarrier spacing setting u are defined within the bandwidth part (BWP), and range from 0 to N size,u BWP,i Numbered up to -1, where i is the number of the above bandwidth part. Common resource block n u CRB and physical resource block n within bandwidth part i PRB The relationships between them are as follows: n u PRB = n uCRB +N start,u BWP,i , here N start,u BWP,i is a common resource block where the above bandwidth part starts relative to CRB 0. A BWP contains multiple consecutive RBs in the frequency domain. For example, a BWP is a given numerator u within a BWP i on a given carrier. i It is a subset of contiguous CRBs defined for. The carrier may contain up to N (e.g., 5) BWPs. A UE may be configured to have one or more BWPs on a given component carrier. Data communication is performed through the enabled BWPs, and only a predetermined number (e.g., 1) of the BWPs configured for the UE may be enabled on the carrier.

[0090] For each serving cell within a set of DL BWPs or UL BWPs, the network establishes at least an initial DL BWP and one initial UL BWP (if the serving cell is configured with an uplink) or two initial UL BWPs (if using a supplementary uplink). The network may also establish additional ULs and DL BWPs for the serving cell. For each DL BWP or UL BWP, the UE is provided with the following parameters for the serving cell: i) subcarrier spacing, ii) circular prefix, iii) N start BWP Assuming = 275, offset RB set and length L RB CRBN provided by the RRC parameter locationAndBandwidth, which indicates as the resource indicator value (RIV). start BWP =O carrier +RB start and the number of contiguous RBs N size BWP =LRB , and O provided by the RRC parameter offsetToCarrier for the subcarrier spacing carrier ; Index within the set of the above DL BWPs or UL BWPs; set of BWP-common parameters and set of BWP-exclusive parameters.

[0091] Virtual resource blocks (VRBs) are defined within the bandwidth part and range from 0 to N size,u BWP,i Numbered up to -1, where i is the number of the above bandwidth part. VRBs are mapped to physical resource blocks (PRBs) according to interleaved or non-interleaved mapping. In some implementations, for non-interleaved VRB-to-PRB mapping, VRB n can be mapped to PRB n.

[0092] NR frequency bands are defined as two types of frequency ranges, FR1 and FR2, where FR2 is also referred to as millimeter wave (mmW). The following table illustrates the frequency ranges in which NR can operate.

[0093] Frequency Range designationCorresponding frequency rangeSubcarrier SpacingFR1410MHz - 7125MHz15, 30, 60kHzFR224250MHz - 52600MHz60, 120, 240kHz

[0094] Figure 5 illustrates physical channels used in a 3GPP-based communication system, which is an example of a wireless communication system, and the signal transmission / reception process using them.

[0095] A UE that has been turned on again after being turned off or has lost connection with a wireless communication system first performs an initial cell search process, such as searching for a suitable cell to camp on and synchronizing with said cell or the BS of said cell (S11). During the initial cell search process, the UE receives a synchronization signal block (SSB) (also called an SSB / PBCH block) from the BS. The SSB includes a primary synchronization signal (PSS), a secondary synchronization signal (SSS), and a physical broadcast channel (PBCH). Based on the PSS / SSS, the UE synchronizes with the BS and obtains information such as the cell identifier (ID). Additionally, the UE can obtain broadcast information within the cell based on the PBCH. Meanwhile, during the initial cell search process, the UE can receive a downlink reference signal (DL RS) to check the downlink channel status.

[0096] After completing the initial cell search, the UE can camp on the cell. After camping on the cell, the UE monitors the PDCCH on the cell and receives the PDSCH according to the downlink control information (DCI) carried by the PDCCH to obtain more specific system information (S12).

[0097] Subsequently, the UE may perform a random access procedure to complete access to the BS (S13 to S16). For example, during the random access procedure, the UE may transmit a preamble through a physical random access channel (PRACH) (S13) and receive a random access response (RAR) for the preamble through a PDCCH and a corresponding PDSCH (S14). If the reception of the RAR for the UE fails, the UE may attempt to re-transmit the preamble. In the case of contention-based random access, a contention resolution procedure (S16) may be performed, which includes transmitting a PUSCH based on the UL resource allocation included in the RAR (S15) and receiving a PDCCH and a corresponding PDSCH.

[0098] A UE that has performed the procedure described above may subsequently perform the reception of PDCCH / PDSCH (S17) and the transmission of PUSCH / PUCCH (S18) as part of a general uplink / downlink signal transmission process. The control information transmitted by the UE to the BS is collectively referred to as uplink control information (UCI). UCI includes HARQ ACK / NACK (Hybrid Automatic Repeat and reQuest Acknowledgement / Negative-ACK) (also called HARQ-ACK), scheduling request (SR), channel state information (CSI), etc. CSI may include channel quality indicator (CQI), precoding matrix indicator (PMI), and / or rank indicator, etc. UCI is generally transmitted via PUCCH, but may be transmitted via PUSCH if control information and traffic data need to be transmitted simultaneously. In addition, the UE can transmit UCI atypically via PUSCH based on network requests / instructions.

[0099] FIG. 6 illustrates an arbitrary connection process that may be applied to the implementation(s) of the present specification. In particular, FIG. 6(a) illustrates a four-step arbitrary connection process, and FIG. 6(b) illustrates a two-step arbitrary connection process.

[0100] The random access process can be used for various purposes, such as initial access, uplink adjustment, resource allocation, handover, reconfiguration after a wireless link failure, and location measurement. Random access processes are classified into contention-based and dedicated (i.e., non-contention-based) processes. Contention-based random access processes are generally used for initial access, while dedicated random access processes are used for handover, when downlink data reaches the network, and when reconfiguring uplink adjustment for location measurement. In a contention-based random access process, the UE randomly selects a random access (RA) preamble. Therefore, it is possible for multiple UEs to transmit the same RA preamble simultaneously, which necessitates a subsequent contention resolution process. In contrast, in a dedicated random access process, the UE uses an RA preamble uniquely assigned to it by the BS. Consequently, the UE can perform the random access process without conflicts with other UEs.

[0101] Referring to FIG. 6(a), the contention-based random access process includes the following four steps. Hereinafter, the messages transmitted in steps 1 through 4 may be referred to as Msg1 through Msg4, respectively.

[0102] - Step 1: The UE transmits the RA preamble via PRACH.

[0103] - Step 2: The UE receives a random access response (RAR) from the BS via PDSCH.

[0104] - Step 3: The UE transmits UL data to the BS via PUSCH based on RAR. Here, the UL data includes Layer 2 and / or Layer 3 messages.

[0105] - Step 4: The UE receives a contention resolution message from the BS via PDSCH.

[0106] The UE can receive information regarding random access from the BS through system information. For example, information regarding RACH times associated with SSBs on the cell may be provided through system information. The UE can select an SSB among those received on the cell for which the reference signal received power (RSRP) measured based on the SSB exceeds a threshold, and transmit an RA preamble through the PRACH associated with the selected SSB. For example, if random access is required, the UE transmits Msg1 (e.g., preamble) to the BS on the PRACH. The BS can distinguish each random access preamble through the time / frequency resource (RA Occasion, RO) and the random access preamble index (Preamble Index, PI). When the BS receives a random access preamble from the UE, the BS transmits a RAR message to the UE on the PDSCH. To receive a RAR message, the UE monitors a CRC-masked L1 / L2 control channel (PDCCH) containing scheduling information for a RAR message within a preset time window, a RAR window (e.g., ra-ResponseWindow), which is a Random Access-RNTI (RA-RNTI). The length of the RAR window may be set by upper-level signaling, and the RAR window may start at a specific timing after a PRACH transmission (e.g., the first symbol of the fastest control resource set (CORESET) of the Type-1 PDCCH common seek space, starting at least one symbol after the PRACH time corresponding to the PRACH transmission). When scheduling information is received through the PDCCH masked by the RA-RNTI, the UE may receive a RAR message from the PDSCH indicated by the scheduling information.Subsequently, the UE determines whether there is a RAR for itself within the aforementioned RAR message. Whether a RAR for itself exists can be verified by checking whether a RAPID (Random Access preamble ID) exists for the preamble transmitted by the UE. The index of the preamble transmitted by the UE and the RAPID may be the same. A RAR includes a corresponding random access preamble index, timing offset information for UL synchronization (e.g., a timing advance command (TAC)), UL scheduling information for sending Msg3 (e.g., a UL grant), and temporary UE identification information (e.g., Temporary-C-RNTI, TC-RNTI). Upon receiving the RAR, the UE sends Msg3 via PUSCH according to the UL scheduling information and timing offset values ​​within the RAR. Msg3 may include the UE's ID (or the UE's global ID). Additionally, Msg3 may include information related to an RRC connection request for initial access to the network (e.g., an RRCSetupRequest message). After receiving Msg3, the BS sends Msg4, a contention resolution message, to the UE. If the UE receives the contention resolution message and the contention is successfully resolved, TC-RNTI is changed to C-RNTI. Msg4 includes the UE's ID and / or It may include information related to the RRC connection (e.g., RRCSetup message). If the information transmitted via Msg3 does not match the information received via Msg4, or if Msg4 is not received for a certain period of time, the UE may report that the contention resolution failed and retransmit Msg3.

[0107] Meanwhile, the dedicated random access process includes the following three steps. Hereinafter, the messages transmitted in steps 0 to 2 may be referred to as Msg0 to Msg2, respectively. The dedicated random access process may be triggered by the UE by the BS using a PDCCH (hereinafter referred to as the PDCCH order) for commanding the transmission of an RA preamble.

[0108] - Step 0: BS assigns the RA preamble to the UE via dedicated signaling.

[0109] - Step 1: The UE transmits the RA preamble via PRACH.

[0110] - Step 2: The UE receives the RAR via the PDSCH from the BS.

[0111] The operations of steps 1 to 2 of a dedicated random access process may be the same as steps 1 to 2 of a contention-based random access process.

[0112] In NR systems, lower latency than in existing systems may be required. Additionally, a four-stage random access process may not be desirable, particularly for latency-sensitive services such as URLLC. A low-latency random access process may be required within various scenarios of NR systems. When the implementation(s) of this specification are performed with a random access process, to reduce latency in the random access process, the implementation(s) of this specification may be performed with the following two-stage random access process.

[0113] Referring to FIG. 6(b), the two-stage random access process may consist of two stages: the transmission of MsgA from the UE to the BS and the transmission of MsgB from the BS to the UE. The transmission of MsgA may include the transmission of an RA preamble via PRACH and the transmission of a UL payload via PUSCH. In the transmission of MsgA, PRACH and PUSCH may be transmitted using time division multiplexing (TDM). Alternatively, in the transmission of MsgA, PRACH and PUSCH may be transmitted using frequency division multiplexing (FDM).

[0114] A BS that receives MsgA may transmit MsgB to a UE. MsgB may include a RAR for said UE. After MsgA transmission, said UE monitors for a response from the network within a time window to monitor for a RAR for a two-stage random access process. The length of said time window may be set by upper-layer signaling, and said time window may start at a specific timing after MsgA transmission (e.g., the first symbol of the fastest CORESET of the Type-1 PDCCH common seek space starting at least one symbol after the last symbol of the PUCCH time corresponding to the PRACH transmission of said MsgA transmission).

[0115] A message related to an RRC connection request (e.g., RRCSetupRequest message) requesting to establish a connection between the RRC layer of the BS and the RRC layer of the UE may be transmitted by being included in the payload of MsgA. In this case, MsgB may be used to transmit RRC connection-related information (e.g., RRCSetup message). Alternatively, the RRC connection request message (e.g., RRCSetupRequest message) may be transmitted via PUSCH transmitted based on a UL grant within MsgB. In this case, the RRC connection-related information (e.g., RRCSetup message) related to the RRC connection request may be transmitted via PDSCH associated with said PUSCH transmission after the PUSCH transmission based on MsgB.

[0116] Below, physical channels that can be used in 3GPP-based wireless communication systems are described in more detail.

[0117] A PDCCH carries a DCI. For example, a PDCCH (i.e., a DCI) carries the transmission format and resource allocation of the downlink shared channel (DL-SCH), resource allocation information for the uplink shared channel (UL-SCH), paging information for the paging channel (PCH), system information on the DL-SCH, resource allocation information for control messages of the layer above the physical layer (hereinafter referred to as the upper layer) among the protocol stacks of the UE / BS, such as random access response (RAR) transmitted on the PDSCH, transmission power control commands, and the activation / deactivation of configured scheduling (CS). A DCI containing resource allocation information for the DL-SCH is also called a PDSCH scheduling DCI, and a DCI containing resource allocation information for the UL-SCH is also called a PUSCH scheduling DCI. The DCI includes a cyclic redundancy check (CRC), and the CRC is masked / scrambled with various identifiers (e.g., radio network temporary identifier (RNTI)) depending on the owner or use of the PDCCH. For example, if the PDCCH is for a specific UE, the CRC is masked with the UE identifier (e.g., cell RNTI (C-RNTI)). If the PDCCH is for paging, the CRC is masked with the paging RNTI (P-RNTI). If the PDCCH is for system information (e.g., system information block (SIB)), the CRC is masked with the system information RNTI (SI-RNTI). If the PDCCH is for a random access response, the CRC is masked with the random access RNTI (RA-RATI).

[0118] The scheduling of a PDCCH on one serving cell to a PDSCH or PUSCH on another serving cell is called cross-carrier scheduling. Cross-carrier scheduling using a carrier indicator field (CIF) may allow a PDCCH on a serving cell to schedule resources on another serving cell. Meanwhile, the scheduling of a PDSCH or PUSCH on a serving cell to a serving cell is called self-carrier scheduling. When cross-carrier scheduling is used in a cell, the BS may provide the UE with information regarding the cell scheduling said cell. For example, the BS may provide the UE with whether the serving cell is scheduled by a PDCCH on another (scheduling) cell or by said serving cell, and if said serving cell is scheduled by another (scheduling) cell, which cell signals downlink assignments and uplink grants for said serving cell. In this specification, a cell carrying a PDCCH is referred to as a scheduling cell, and a cell in which the transmission of a PUSCH or PDSCH is scheduled by a DCI included in the PDCCH, that is, a cell carrying a PUSCH or PDSCH scheduled by the PDCCH, is referred to as a scheduled cell.

[0119] PDSCH is a physical layer DL channel for DL ​​data transport. PDSCH carries downlink data (e.g., DL-SCH transport blocks) and applies modulation methods such as QPSK (Quadrature Phase Shift Keying), 16 QAM (Quadrature Amplitude Modulation), 64 QAM, and 256 QAM. Codewords are generated by encoding transport blocks (TB). PDSCH can carry up to two codewords. Scrambling and modulation mapping are performed for each codeword, and the modulation symbols generated from each codeword can be mapped to one or more layers. Each layer is mapped to a radio resource along with DMRS to generate an OFDM symbol signal, which is then transmitted through the corresponding antenna port.

[0120] The UE must have uplink resources available to it for UL-SCH data transmission and downlink resources available to it for DL-SCH data reception. Uplink resources and downlink resources are assigned to the UE through resource allocation by the BS. Resource allocation may include time domain resource allocation (TDRA) and frequency domain resource allocation (FDRA). In this specification, uplink resource allocation is also referred to as uplink grant, and downlink resource allocation is also referred to as downlink assignment. Uplink grant is dynamically received by the UE on the PDCCH or within the RAR, or is semi-persistently set to the UE by RRC signaling from the BS. Downlink assignment is dynamically received by the UE on the PDCCH, or is semi-persistently set to the UE by RRC signaling from the BS.

[0121] In UL, the BS can dynamically allocate uplink resources to the UE via PDCCH(s) addressed to a cell radio network temporary Identifier (C-RNTI). The UE monitors the PDCCH(s) to identify potential uplink grant(s) for UL transmission. Additionally, the BS can allocate uplink resources to the UE using a configured grant (CG). Two types of configured grants, Type 1 and Type 2, may be used. In the case of Type 1, the BS directly provides the configured uplink grant (including periodicity) via RRC signaling. In the case of Type 2, the BS sets the period of an RRC-configured uplink grant through RRC signaling and can signal and activate or deactivate the said uplink grant through a PDCCH (PDCCH addressed to CS-RNTI) addressed to a configured scheduling RNTI (CS-RNTI). For example, in the case of Type 2, the PDCCH addressed to CS-RNTI indicates that the said uplink grant may be implicitly reused according to the period set by RRC signaling until it is deactivated.

[0122] In DL, the BS can dynamically allocate downlink resources to the UE via PDCCH(s) addressed by C-RNTI. The UE monitors the PDCCH(s) to identify potential downlink assignments. Additionally, the BS can allocate downlink resources to the UE using semi-static scheduling (SPS). The BS can set the period of the configured downlink assignments via RRC signaling and signal and enable or disable the configured downlink assignments via PDCCHs addressed by CS-RNTI. For example, a PDCCH addressed by CS-RNTI indicates that the corresponding downlink assignment may be implicitly reused according to the period set by RRC signaling until it is disabled.

[0123] A control resource set (CORESET), which is a set of time-frequency resources that allows the UE to monitor PDCCH, may be defined and / or configured. The CORESET consists of a set of physical resource blocks (PRBs) having a duration of one to three OFDM symbols. The PRBs constituting the CORESET and the CORESET duration may be provided to the UE through upper layer (e.g., RRC) signaling. Within the configured CORESET(s), a set of PDCCH candidates is monitored according to the corresponding search space sets. As specified herein, monitoring implies decoding (also known as blind decoding) each PDCCH candidate according to the monitored DCI formats.

[0124] The set of PDCCH candidates monitored by the UE is defined in terms of PDCCH search space sets. A search space set can be a common search space (CSS) set or a UE-specific search space (USS) set. Each CORESET setting is associated with one or more search space sets, and each search space set is associated with one CORESET setting.

[0125] A set of PDCCH candidates can be monitored in one or more CORESETs on an active DL BWP on each active serving cell where PDCCH monitoring is configured, wherein monitoring implies receiving each PDCCH candidate and decoding it according to the monitored DCI formats.

[0126] Based on the CORESET / scan space set configuration, the UE can monitor PDCCH candidates from one or more SS sets within the slot. The occasion (e.g., time / frequency resources) when PDCCH candidates must be monitored is defined as a PDCCH (monitoring) time. One or more PDCCH (monitoring) times can be configured within the slot.

[0127] Wireless communication systems are being widely deployed to provide various types of communication services, such as voice and data. There is an increasing demand for higher data rates to accommodate incoming new services and / or scenarios where the virtual and real worlds are intermingled. To handle these endless demands, new communication technologies beyond 5G are required. New communication technologies beyond 6G (hereinafter 6G) systems are intended to provide (i) very high data rates per device, (ii) a very large number of connected devices, (iii) global connectivity, (iv) very low latency, (v) reduced energy consumption of battery-free IoT devices, (vi) ultra-reliable connectivity, and (vii) connected intelligence with machine learning capabilities. The use of the following technologies is being considered in 6G systems: artificial intelligence (AI), terahertz (THX) communication, optical wireless communication (OWC), free space optics (FSO) backhaul networks, massive multiple-input multiple-output (MIMO) technology, blockchain, 3D networking, quantum communication, unmanned aerial vehicles (UAVs), cell-free communication, integration of wireless information and energy transmission, integration of sensing and communication, integration of access backhaul networks, holographic beamforming, big data analytics, and large intelligent surface (LIS).

[0128] In particular, attempts to integrate artificial intelligence (AI) into communication systems are surging. The approaches being attempted in relation to AI can be broadly categorized into AI for communications (AI4C), which utilizes AI to improve communication performance, and communications for AI (C4AI), which advances communication technology to support AI. In the field of AI4C, there are attempts to design systems by replacing the roles of channel encoders / decoders, modulators / demodulators, or channel equalizers with end-to-end autoencoders or neural networks. In the field of C4AI, there is a method utilizing federated learning—a technique of distributed learning—to update a common prediction model while protecting personal information by sharing only the model's weights or gradients with the server, without sharing raw device data. Additionally, there is a method to distribute the load across devices, network edges, and cloud servers through split inference.

[0129] The introduction of AI into communications can streamline and enhance real-time data transmission. AI can use numerous analyses to determine how complex target tasks are performed. In other words, AI can increase efficiency and reduce processing latency.

[0130] Time-consuming tasks such as handover, network selection, and resource scheduling can be performed instantly by using AI. AI can also play a significant role in machine-to-machine, machine-to-human, and human-to-machine communication. AI-based communication systems can be supported by metamaterials, intelligent structures, intelligent networks, intelligent devices, intelligence cognitive radio, self-sustaining wireless networks, and machine learning.

[0131] Until recently, attempts to integrate AI into wireless communication systems have focused on the application layer and network layer, particularly on wireless resource management and allocation. However, research on integrating AI into wireless communication systems is increasingly advancing toward the MAC layer and physical layer, with attempts emerging specifically to combine deep learning with wireless transmission at the physical layer. AI-based physical layer transmission refers to the application of signal processing and communication mechanisms based on AI drivers, rather than traditional communication frameworks, regarding fundamental signal processing and communication mechanisms. Examples 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.

[0132] Machine learning (ML) can be used for channel estimation and channel tracking, and in the physical layer of DL for power allocation, interference cancellation, etc. In addition, machine learning can be used for antenna selection, power control, symbol detection, etc. in MIMO systems.

[0133] However, applying deep neural networks (DNNs) for transmission at the physical layer may have the following problems.

[0134] Deep learning-based AI algorithms require a vast amount of training data to optimize training parameters. However, due to limitations in acquiring training data from specific channel environments, a large amount of offline training data is used. Consequently, static training on training data from specific channel environments can lead to contradictions between the dynamic characteristics and diversity of wireless channels.

[0135] Furthermore, current deep learning primarily targets real signals. However, signals at the physical layer of wireless communication are complex signals. Further research is needed on neural networks that detect complex domain signals to match the characteristics of wireless communication signals.

[0136] Below, we will examine machine learning in more detail.

[0137] Machine learning refers to a series of operations for training machines to create machines capable of performing tasks that humans can or find difficult to do. Machine learning requires data and learning models. Data learning methods in machine learning can be broadly classified into three types: supervised learning, unsupervised learning, and reinforcement learning.

[0138] The purpose of neural network training is to minimize output errors. It is a process that repeatedly inputs training data into a neural network, calculates the error between the network's output and the target for the training data, and updates the weights of each node by backpropagating the network's error from the output layer to the input layer in a direction that reduces the error.

[0139] Supervised learning uses training data with labeled correct answers, whereas unsupervised learning may not have the correct answers labeled in the training data. For example, in the case of supervised learning regarding data classification, the training data may consist of data where each training data point is labeled with a category. Labeled training data is input into a neural network, and an error can be calculated by comparing the network's output (e.g., category) with the labels of the training data. The calculated error is backpropagated within the neural network (i.e., from the output layer to the input layer), and this backpropagation can update the connection weight(s) of each node in each layer of the neural network. 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 epoch. The learning rate can be applied differently depending on the number of iterations of the neural network's learning epoch. For example, in the early stages of training a neural network, a high learning rate can be used to allow the neural network to quickly achieve a certain level of performance, thereby increasing efficiency, and in the later stages of training, a low learning rate can be used to increase accuracy.

[0140] The learning method may vary depending on the characteristics of the data. For example, if the goal is to accurately predict data transmitted from the transmitting end at the receiving end in a communication system, it is desirable to perform learning using supervised learning rather than unsupervised learning or reinforcement learning.

[0141] A learning model corresponds to the human brain, and one can consider the most basic linear model. The machine learning paradigm that uses highly complex neural network structures, such as artificial neural networks, as learning models is called deep learning.

[0142] The neural network cores used for learning methods mainly include deep neural networks (DNN), convolutional neural networks (CNN), and recurrent neural networks (RNN).

[0143] Figure 7 illustrates a perceptron structure used in an artificial neural network.

[0144] An artificial neural network can be implemented by connecting multiple perceptrons. Referring to Fig. 7, the input vector x = (x1, x2, ..., x d If ) is input, weight w=(w1,w2, ...,w d The entire process of multiplying by ) and summing all the results, and then applying the activation function σ(·), is called a perceptron. In a large artificial neural network structure, the simplified perceptron structure exemplified in Fig. 7 can be extended and applied to multi-dimensional perceptrons with different input vectors.

[0145] Figure 8 illustrates a multilayer perceptron structure.

[0146] The perceptron structure exemplified in FIG. 7 can be extended into a multilayer perceptron structure having a total of three layers based on input and output values. An artificial neural network having H (d+1) dimensional perceptrons between the first and second layers and K (H+1) dimensional perceptrons between the second and third layers can be represented by the multilayer perceptron structure exemplified in FIG. 8.

[0147] The layer where the input vector is located is called the input layer, the layer where the final output value(s) are located is called the output layer, and all layers located between the input layer and the output layer are called hidden layers. Although the example in Fig. 8 includes three layers, the input layer is excluded when counting the actual number of layers in an artificial neural network; therefore, the artificial neural network based on the multilayer perceptron structure of Fig. 8 can be viewed as consisting of two layers. An artificial neural network is constructed by connecting the perceptrons of the basic blocks in a two-dimensional manner.

[0148] In a neural network, a layer consists of small individual units called neurons. In a neural network, a neuron receives input from other neurons, performs some processing, and produces an output. The region within the previous layer where each neuron receives input is called the receptive field. Each neuron calculates an output value by applying a specific function to the input values ​​received from the receptive field within the previous layer. The specific function applied to the input values ​​is determined by i) a vector of weights and ii) a bias. In a neural network, learning is performed by iteratively adjusting these biases and weights. The vector of weights and the bias are called filters and represent particular features of the input.

[0149] The aforementioned input layer, hidden layer, and output layer can be applied not only to multilayer perceptrons but also to various artificial neural network structures such as CNNs, which will be described later. As the number of hidden layers increases, the artificial neural network becomes deeper, and a machine learning paradigm that uses a sufficiently deep artificial neural network as a learning model is called deep learning. In addition, the artificial neural network used for deep learning is called a deep neural network (DNN).

[0150] The above multilayer perceptron structure is referred to as a fully-connected neural network. In a fully-connected neural network, there are no connections between neurons located in the same layer, and connections exist only between neurons located in adjacent layers. A DNN possesses a fully-connected neural network structure and is composed of a combination of multiple hidden layers and activation functions; it can be usefully applied to identify correlation characteristics between inputs and outputs. Here, correlation characteristics may refer to the joint probability of the input and output.

[0151] Meanwhile, depending on how multiple perceptrons are connected to each other, various artificial neural network structures different from DNNs can be formed.

[0152] Figure 9 illustrates a convolutional neural network (CNN) structure.

[0153] In a DNN, neurons within a single layer are arranged in a one-dimensional manner. However, referring to Fig. 9, in a CNN, it can be assumed that there are w neurons horizontally and h neurons vertically arranged in a two-dimensional manner. In this case, since a weight is applied for each connection leading from an input neuron to a hidden layer, a total of h × w weights must be considered. Since there are h × w neurons in the input layer, there are a total of h between two adjacent layers. 2 w 2 A number of weights are required.

[0154] Figure 10 illustrates a filter operation in a CNN.

[0155] The CNN exemplified in Fig. 6 has a problem in which the number of weights increases exponentially depending on the number of connections, so instead of considering the connections of all neurons between adjacent layers, it is assumed that there are small filters, and as exemplified in Fig. 10, weighted sum operations and activation function operations are performed on the parts where filters overlap.

[0156] A single filter has weights corresponding to its size, and the weights can be trained to extract and output a specific feature on the image as a factor. In Fig. 10, a 3×3 filter is applied to the top-left 3×3 area of ​​the input layer, and the output value obtained by performing a weighted sum operation and an activation function operation on the corresponding neuron is z 22 Save to.

[0157] The filter above scans the input layer, moves by a fixed interval horizontally and vertically, performs weighted sum operations and activation function operations, and places the output value at the current filter position. This method of operation is similar to the convolutional operation on images in the field of computer vision, so a deep neural network with this structure is called a CNN, and the hidden layer generated as a result of the convolutional operation is called a convolutional layer. In addition, a neural network containing multiple convolutional layers is called a deep convolutional neural network (DCNN).

[0158] In a convolutional layer, the number of weights can be reduced by calculating a weighted sum that includes only the neuron(s) located within the area covered by the current filter. As a result, a single filter can be utilized to focus on features of a local area. Accordingly, CNNs can be effectively applied to image data processing where physical distance in a 2-dimensional area serves as an important criterion for judgment. Meanwhile, in a CNN, multiple filters can be applied immediately before the convolutional layer, and multiple output results can be generated through the convolution operation of each filter.

[0159] A fully connected layer connects every neuron in one layer to every neuron in another layer.

[0160] NN systems have the advantage of being able to solve difficult problems or optimize based on non-linearity. For NN-based wireless communication systems, end-to-end learning has been proposed to simultaneously optimize channel coding, modulation, and filtering at the transmitting end, as well as channel estimation and signal detection algorithms at the receiving end.

[0161] End-to-end communication can expect high performance gains compared to existing communication systems, where individual blocks and the transmitter and receiver are optimized separately, because the transmitter NN and receiver NN are trained jointly. Research is underway to solve problems such as CSI acquisition, pilot signal design, data transmission, and beamforming by applying end-to-end communication.

[0162] However, to maximize the performance of end-to-end communication, the transmitter and receiver must be trained to adapt appropriately to the channel environment. For example, an end-to-end communication system can implement the transmitter, channel, and receiver as NN(s) and jointly optimize the trainable parameters of the transmitter and receiver for a specific channel model. However, unlike a system that considers an NN only at the receiver, training of end-to-end communication (hereinafter referred to as end-to-end learning) involves not only transmitting a training signal from the transmitter to the receiver for weight calculation but also requiring feedback from the receiver to the transmitter, which leads to a problem of significantly increased signaling overhead. To solve this, offline learning or methods to train only the NN at the receiver can be considered. However, offline learning has the disadvantage that end-to-end communication cannot operate adaptively on the channel, and training only the receiver NN is a sub-optimal method because the transmitter cannot be fine-tuned as the transmitter NN cannot be trained.

[0163] Figure 11 illustrates the concept of step-to-step learning with backpropagation. In particular, Figure 11 is a conceptual diagram of the training signal and feedback of step-to-step learning with backpropagation. In Figure 11, ∇ t,pis the gradient for the p-th training signal of the t-th batch, and H t,p is the channel experienced by the p-th training signal of the t-th batch, and X t,p is the p-th training signal of the t-th batch, and L t,p is the loss for the p-th training signal of the t-th batch.

[0164] Referring to FIG. 11, backpropagation is performed from the loss value of the receiver calculated from the training signal x input from the transmitter to the receiver. Therefore, to secure optimal performance of end-to-end communication, research is required on an efficient method to jointly train the transmitter and receiver of end-to-end communication. Below, several implementations of the present specification applying end-to-end learning strategies and transfer learning are described.

[0165] Figure 12 shows an example of a method for calculating gradients in a neural network.

[0166] In AI, the learning of NNs is performed by calculating gradients, which are calculated through backpropagation. Backpropagation, that is, the backward propagation of errors, is an algorithm for supervised learning of artificial neural networks using gradient descent. Backpropagation is calculated through the chain rule, as exemplified in Fig. 12. When a specific training signal is given as input, the gradient value for each layer is obtained from the feedforward value calculated as the corresponding training signal passes through each layer of the NN.

[0167] In step-to-step learning as well, gradients can be calculated through backpropagation. Referring to Fig. 12, w tSince the terms differentiated with respect to include the differential terms at the receiver, backpropagation calculated values ​​must be transmitted from the receiver to the transmitter. This significantly increases signaling overhead in end-to-end learning. In Fig. 12, each differential term expressed by the chain rule is a value related to the part connected by an arrow on the transmit / receive path. Additionally, in Fig. 12, t and k represent the t-th batch and the k-th training symbol, respectively, and w nm Eunw t It is an element of my n-th row and m-th column.

[0168] Although NN systems have various advantages, to maximize the performance of NN systems, NNs must be trained to adapt appropriately to the channel environment. Rapid training affects the performance of communication systems to which NNs are applied.

[0169] Below, ISAC is explained in detail.

[0170] Figure 13 illustrates examples of wireless sensing modes supported by ISAC.

[0171] Referring to Fig. 13, when considering the transmission and reception operations in the 3GPP standard and the nodes participating therein, the sensing mode can be broadly classified as follows.

[0172] (a) BS mono-static sensing mode: The BS that transmitted the radio wave receives the reflected signal.

[0173] (b) BS-to-BS bi-static sensing mode: Another BS receives the reflected signal of a radio wave transmitted by a specific BS.

[0174] (c) BS-to-UE bi-static sensing mode: The UE receives the signal reflected from the radio wave transmitted by the BS.

[0175] (d) BS mono-static sensing mode: The UE that transmitted the radio wave receives the reflected signal

[0176] (e) UE-to-UE bi-static sensing mode: Another UE receives the reflected signal of a radio wave transmitted by a specific transmitting UE

[0177] (f) UE-to-BS bi-static sensing mode: The BS receives the reflected signal of the radio wave transmitted by the transmitting UE.

[0178] However, in addition to the six use cases mentioned above, a sensing mode including multiple transmitting / receiving nodes may be referred to using the term multi-static sensing mode.

[0179] Wireless sensing via ISAC / JCAS is being considered for application in various scenarios. Generally, wireless sensing is considered for the purpose of acquiring information about targets that do not have a communication module (or are independent of a communication module), and for example, the scenarios that can be considered can be broadly classified into three types.

[0180] (1) Object detection and tracking: This is a scenario for sensing target objects or people or tracking location information. Representative scenarios include intruder sensing in indoor / outdoor situations, location tracking of UAVs or AGVs, and support for autonomous driving.

[0181] (2) Environment monitoring: This is a scenario intended to collect information about the environment around the transmitting / receiving nodes. Examples of scenarios to consider include rainfall information observation and flood sensing.

[0182] (3) Motion monitoring: This is a scenario for sensing the motion of a target, and typical examples include scenarios for distinguishing human movements or gestures.

[0183] The performance metrics and levels required for each of the above scenarios are diverse and may differ from one another. To design an ISAC / JCAS suitable for the quality of service required in each scenario, various key performance requirements need to be considered. The 3GPP standard TS 22.137 document defines the positioning estimation accuracy, velocity estimation accuracy, confidence level, sensing resolution, missed detection probability, false alarm probability, maximum latency of the sensing service, and refreshing rate as key performance requirements for each service scenario, and the required levels for each key performance requirement may vary depending on the service scenario.

[0184] Radio frequency sensing capabilities can provide services for determining object locations without devices because they do not require connecting to objects via devices within a network. The ability to obtain range, velocity, and angle information from radio frequency signals can provide a wide range of new functions, such as various object sensing, object recognition (e.g., vehicles, humans, animals, UAVs), and high-precision localization, tracking, and activity recognition. Radio sensing services can provide information to various industries (e.g., unmanned aerial vehicles, smart homes, V2X, factories, railways, public safety, etc.) that enable applications such as intruder sensing, assisted vehicle steering and navigation, trajectory tracking, collision avoidance, traffic management, and health and traffic management. In some cases, radio sensing may utilize non-3GPP type sensors (e.g., radar, cameras) to further support 3GPP-based sensing. For example, the operation of a radio sensing service—that is, the sensing operation—may rely on the transmission, reflection, and scattering processing of radio sensing signals. Therefore, wireless sensing can provide an opportunity to enhance existing communication systems from communication networks to wireless communication and sensing networks.

[0185] FIGS. 14 and 15 illustrate an example of an application of ISAC to a 3GPP radio communication system. The embodiments of FIGS. 14 and 15 may be combined with various embodiments of the present disclosure. Specifically, FIG. 14 illustrates an example of sensing using a sensing receiver and a sensing transmitter located at the same location (e.g., monostatic sensing), and FIG. 15 illustrates an example of sensing using a separated sensing receiver and a sensing transmitter (e.g., bistatic sensing).

[0186] For example, in the sensing process of ISAC, information about the surrounding environment can be collected by analyzing how the transmitted signal is reflected, scattered, or diffracted. In the case of signal transmission, a transmitter may transmit or radiate a signal for ISAC. As described above, the signal for ISAC may include data and RS. The signal for ISAC may be received by a receiver of a receiving device (e.g., a terminal or a base station). At this time, the receiver may receive the signal for ISAC and the reflected wave of the signal. In this case, the receiving device may simultaneously analyze the received signal and the reflected signal.

[0187] For example, the receiving device can decode the data through a direct path signal (LoS, Line of Sight) and extract the distance, speed, and direction of the object through the reflected signal or the reflected path signal (NLoS, Non-Line of Sight). For example, the receiving device can estimate the distance to the (surrounding) object and the speed of the object by applying the FMCW (Frequency-Modulated Continuous Wave) technique to the reflected signal, or detect the relative speed to the (surrounding) object by applying the Doppler Shift Analysis technique.

[0188] Monostatic and bistatic sensing

[0189] According to one example, monostatic and bistatic sensing can be defined as follows in the context of radar-based sensing using an Integrated Sensing and Communication (ISAC) system, based on the presence of a radar sensing receiver at a base station (BS).

[0190] 1. Monostatic sensing

[0191] If a BS (base station) has a radar sensing receiver, the radar transmitter and receiver may be located in the same position. For example, when a BS performs monostatic sensing, one BS can transmit a radar signal and receive a signal reflected from a target. In this case, the BS can operate independently to sense the environment.

[0192] 2. Bistatic Sensing:

[0193] If the BS does not have a radar sensing receiver, the sensing may involve at least two spatially separated entities. One entity may be for transmitting the signal and the other entity may be for receiving the signal. For example, in bistatic sensing, the BS transmits a radar signal, but another device (e.g., a UE or another BS) may receive a reflected signal of the radar signal. In this case, the BS can perform sensing through cooperation with the other entity.

[0194] In ISAC implementation, monostatic may be more suitable for scenarios where the BS can operate independently for radar detection and communication. Bistatic can often provide extended detection capabilities over a wider spatial area, and the distributed nature of the ISAC system can be utilized.

[0195] In the initial setup scenario, the sensing method to be used in the Integrated Sensing and Communication (ISAC) system, between monostatic sensing and bistatic sensing, can be determined by considering the following factors / steps. For example, the determination of the sensing method may vary depending on hardware configuration, system requirements, and operational objectives.

[0196] The first step may be a hardware capability evaluation. If a radar receiver exists in the BS, the BS can establish monostatic sensing in relation to the ISAC. Conversely, if a radar receiver does not exist in the BS, the BS can establish bistatic sensing in relation to the ISAC. Regarding UE support, the BS can determine whether the UE or another nearby entity can act as a receiver for bistatic sensing. For example, the BS can determine whether the UE or another entity has compatible hardware for receiving reflected signals of radar signals.

[0197] The second step may be the analysis of communication-sensing integration goals. First, regarding coverage, monostatic sensing may be established in relation to the ISAC if the target requires sensing in a local and limited area around the BS. Conversely, if extended spatial sensing is required, bistatic sensing may be established to allow for broader coverage by utilizing multiple entities. Regarding target location determination, monostatic sensing simplifies signal processing but may not be an effective means for multi-target or Non-Line-of-Sight (NLOS) scenarios. In contrast, bistatic sensing can improve location accuracy by utilizing various angles and paths.

[0198] The third step may involve considering operational constraints. For example, operational constraints related to latency requirements and power / processing loads may be considered. Regarding latency requirements, monostatic sensing generally has low latency because it operates independently. In contrast, bistatic sensing involves signal synchronization between entities, which may result in latency. Regarding power and processing loads, monostatic sensing can centralize power and processing at the BS. Conversely, bistatic sensing distributes the load but may require a stable coordination mechanism.

[0199] The fourth step may involve considering the feasibility of the communication protocol. For example, the feasibility of the communication protocol may relate to synchronization and signal sharing. Regarding synchronization, bistatic sensing requires that precise time and phase synchronization between the transmitter and the receiver be ensured. In contrast, monostatic sensing may not require such precise synchronization. Regarding signal sharing, bistatic sensing may require verification of whether the communication protocol supports the sharing of sensing data between the relevant entities.

[0200] Step 5 involves evaluating the deployment environment. For example, monostatic sensing may be appropriate in relatively static environments (e.g., urban infrastructure sensing). In contrast, bistatic sensing may offer greater flexibility and coverage in dynamic environments (e.g., vehicle networks). Regarding Line of Sight (LOS) conditions, monostatic sensing may be optimal. Conversely, bistatic sensing may be appropriate for NLOS conditions or for sensing beyond obstacles.

[0201] Hybrid Method for Joint AoA and AoD Estimation in ISAC Systems.

[0202] In the following, we propose a hybrid method that improves accuracy and computational efficiency in Angle of Arrival (AoA) and Angle of Departure (AoD) estimation for bistatic ISAC systems (or monostatic ISAC systems). By combining a Deep Learning (DL)-based approach with a parameterized estimation algorithm (hereinafter referred to as a parameterized estimation algorithm), this method enables accurate angle estimation while reducing processing time by utilizing complex neural networks and known model parameters.

[0203] Here, the parameter estimation algorithm may have the following advantages.

[0204] - High accuracy: Theoretical It can provide accurate AoA and AoD estimates close to (CRB).

[0205] - Model utilization: Complete knowledge of the system model, including antenna array geometries and steering vectors, or mathematical models may be utilized.

[0206] - Robustness: The estimation performance of AoA / AoD can be consistently maintained in low-noise and static environment scenarios.

[0207] In the case of parameter estimation algorithms, the following disadvantages may exist.

[0208] - High computational complexity: Complex computational processes such as eigenvalue decomposition, matrix transformations, and iterative calculations may be required.

[0209] - Real-time tasks: Due to computational overhead, it may not scale properly in scenarios with many antennas or dynamic environments.

[0210] - Dependency on Model Assumptions: Due to a strong dependency on accurate system modeling, the above model assumptions may not hold true in actual scenarios (e.g., clutter or multipath interference).

[0211] Deep learning-based approaches may have the following advantages.

[0212] - Computational efficiency: Real-time implementation is possible due to the small processing overhead compared to existing methods (e.g., parameter estimation algorithms).

[0213] - Flexibility: Since explicit system modeling is not required, it can be suitable for environments with model inaccuracies or unknown variables.

[0214] - Generalization: By learning patterns from data, it can rapidly adapt to various noise levels, antenna configurations, and environmental conditions.

[0215] Deep learning-based approaches may have the following disadvantages.

[0216] - Training overhead: A large amount of data may be required for training to achieve generalization, especially in various scenarios.

[0217] - Performance variability: If the training dataset is not representative of typical situations, performance may degrade under specific environmental conditions.

[0218] - Interpretability: The decision-making process of a neural network can be difficult to debug and optimize due to its opacity.

[0219] Considering the advantages and disadvantages of these two estimation methods, the proposed method offers an adaptive hybrid AoA / AoD estimation method for a combination of the two methods that can maximize their benefits. Below, we describe in detail a method for estimating AoA / AoD by dynamically switching between a parameterized algorithm and a deep learning-based estimation method, and / or a combination of the two outputs, while taking into account the analysis of the surrounding environment, the analysis of channel states, and the need for real-time operation.

[0220] First, the following adaptive design framework can be considered in relation to the combination of the two estimation methods.

[0221] (1) Environmental analysis module

[0222] The environment analysis module can monitor factors related to the surrounding environment, such as noise levels, environmental dynamics, and computational resource availability. The environment analysis module can analyze and determine whether the surrounding environment is in a state among predefined categories: dynamic, static, and hybrid.

[0223] (2) Decision Engine / Decision Module

[0224] The decision module can determine a scenario corresponding to the surrounding environment based on the surrounding environment analysis results of the environment analysis module. For example, if the surrounding environment is in a static state (e.g., when the change in channel state is below a first threshold), the decision module may select a static scenario that prioritizes the output result according to the parameter estimation algorithm (e.g., estimated AoA / AoD) in order to improve the estimation accuracy of AoA / AoD. Alternatively, if the surrounding environment is in a dynamic state (e.g., when the change in channel state is above a second threshold), the decision module may activate a deep learning-based estimation method to ensure the efficiency, real-time capability, and adaptability of AoA / AoD estimation, and select a dynamic scenario that prioritizes the output result according to the deep learning-based estimation method (e.g., estimated AoA / AoD). Alternatively, in the case where the surrounding environment is in a hybrid state where it is difficult to clearly distinguish between a static state and a dynamic state (e.g., when the channel state change is greater than or equal to a first threshold and less than a second threshold), the decision module may select a hybrid scenario in which the AoA / AoD estimation value according to the deep learning estimation method and the AoA / AoD estimation value according to the parameter estimation algorithm are combined / fused. For example, in the hybrid scenario, the output result according to the deep learning estimation method is prioritized for real-time prediction of AoA / AoD, but the accuracy of the AoA / AoD estimation value according to the deep learning estimation method may be further supplemented by correcting / supplementing the AoA / AoD estimation value according to the deep learning estimation method through the AoA / AoD estimation value according to the parameter estimation algorithm.

[0225] (3) Real-time operation

[0226] Based on real-time feedback from the environment analysis module, a transition mechanism for dynamic switching between deep learning estimation methods and estimation methods based on parameter estimation algorithms can be applied. Additionally, in the case of hybrid scenarios, the estimation results of the two estimation methods can be combined to improve robustness and accuracy.

[0227] (4) Learning and Optimization Module

[0228] The learning and optimization module can continuously update the deep learning model by collecting new data from the environment so that the proposed method can adapt to evolving conditions. Alternatively, the learning and optimization module can improve the parameter estimation algorithm by integrating insights obtained from real-world deployment.

[0229] For example, the BS or control unit may determine / select one scenario among a dynamic scenario, a static scenario, and a hybrid scenario based on the analysis results of the environment analysis module. If the channel environment is analyzed as being in a static state by the environment analysis module (e.g., low noise, channel state change below a first threshold), the BS or control unit may estimate AoA / AoD using only the estimation method based on the parameter estimation algorithm among the deep learning estimation method and the estimation method based on the parameter estimation algorithm. If the channel environment is analyzed as being in a dynamic state by the environment analysis module (e.g., high noise, channel state change above a second threshold), the BS or control unit may estimate AoA / AoD using only the deep learning estimation method among the deep learning estimation method and the estimation method based on the parameter estimation algorithm. Alternatively, if the channel environment is analyzed as being in a hybrid state by the environment analysis module (e.g., urban area, channel state change that is above the first threshold and below the second threshold), the BS or control unit may prioritize the deep learning estimation method to ensure real-time performance, but may periodically correct / improve the estimation results based on the deep learning estimation method based on the estimation results based on the parameter estimation algorithm calculated in parallel periodically.

[0230] Such an adaptive design can leverage the strengths of both estimation methods while mitigating their weaknesses. By dynamically adapting to the surrounding environment and channel conditions, this adaptive design can ensure optimal performance across a wide range of ISAC applications, from autonomous vehicles to industrial sensing.

[0231] The aforementioned hybrid estimation method may consider the following system configuration, hybrid AoA and AoD estimation procedures, computational efficiency, real-time implementation, and gains and applications.

[0232] 1. System Settings

[0233] Based on a bistatic ISAC configuration in which the BS transmits a signal and the BS and UE receive reflections for sensing purposes, the base station (BS) and the user equipment (UE) can operate. In such a system, joint AoA and AoD estimation may be performed to facilitate accurate target location identification and channel state recognition.

[0234] 2. Hybrid AoA and AoD Estimation Procedure

[0235] The hybrid AoA and AoD estimation procedure can be performed as follows.

[0236] (1) Step 1 - Initial communication and channel matrix setup

[0237] BS can instruct the UE to initiate a communication session with the UE and prepare for joint sensing and data exchange for communication.

[0238] The UE can transmit an initial data frame to the BS so that the BS can estimate the channel matrix.

[0239] (2) Step 2 - Dual Processing Approaches

[0240] BS can use a dual-processing approach to estimate AoA and AoD for optimal accuracy and efficiency. Specifically, BS can estimate / predict AoA / AoD from data / signals received from the UE by utilizing i) a deep learning (DL)-based AoA / AoD estimation method and an AoA and AoD estimation method using a parameterized estimation algorithm in parallel. Meanwhile, the parameterized estimation algorithm is a method that estimates AoA / AoD using a predefined mathematical model to estimate AoA / AoD based on a channel matrix, and the deep learning model may be a method that estimates AoA / AoD by learning the interrelationship between AoA / AoD and the channel matrix based on data learning.

[0241] i) Deep learning-based AoA and AoD estimation

[0242] - The above deep learning model can process the channel matrix through a complex neural network model.

[0243] - A deep learning model can extract key spatial features related to AoA and AoD by using a structured configuration of complex-valued convolutional layers and linear layers. For example, BS can extract key spatial features related to AoA and AoD through a deep learning model that utilizes a structured configuration of complex-valued convolutional and linear layers. For example, BS can input a channel matrix (or a pre-processed channel matrix) corresponding to an initial data frame received from the UE into the deep learning model to obtain information about the AoA and AoD estimated / predicted by the deep learning model.

[0244] Deep learning models can achieve accurate estimation while maintaining low computational complexity by applying a complex activation function (CReLU) and minimizing the Mean Squared Error (MSE).

[0245] ii) Parameter estimation algorithms for AoA and AoD estimation

[0246] - In parallel with the deep learning-based AoA and AoD estimation method described above, BS can improve / refine angle estimation related to AoA and AoD by using a parameter estimation algorithm that utilizes predefined model knowledge (e.g., mathematical models) such as antenna steering vectors and array geometries. For example, BS can estimate AoA and AoD for a received initial data frame using a parameter estimation algorithm for AoA and AoD estimation from the UE.

[0247] As such, the AoA and AoD estimation method using parameter estimation algorithms can generate AoA and AoD estimates using matrix transformation, eigenvalue decomposition, and LS (least squares) fitting, and utilizes the entire model details to CRB ( It is possible to achieve high accuracy close to ).

[0248] (3) Step 3: Estimation Fusion and Output

[0249] The above-described estimation system for AoA and AoD can fuse outputs (e.g., AoA and AoD estimated values) according to a deep learning model and a parameter estimation algorithm, thereby prioritizing computational efficiency according to the estimation method based on the deep learning model while also ensuring high accuracy according to the parameter estimation algorithm.

[0250] In this case, the BS can support optimized localization and synchronized communication in the UE by transmitting the final AoA and AoD values, calculated by combining / fusion of estimated AoA and AoD values ​​according to a deep learning model and a parameter estimation algorithm, to the UE. For example, the BS can provide the final AoA and AoD values ​​to the UE, and the UE can estimate its own location based on the AoA and AoD and effectively perform synchronization with the base station / cell.

[0251] 3. Computational Efficiency and Real-Time Implementation

[0252] The proposed system (e.g., a hybrid AoA and AoD estimation system) can reduce the computational load by more than 80% compared to existing maximum likelihood estimators, making it suitable for real-time implementation. In addition, the proposed system can provide a balanced solution that meets the requirements of modern ISAC applications by utilizing a pre-trained model for deep learning components and model knowledge for parameterized components.

[0253] 4. Benefits and Application

[0254] The proposed method can provide an AoA and AoD estimation scheme ideal for ISAC applications in high-density urban environments, vehicle networks, and autonomous systems that require high precision and low latency. Furthermore, the proposed method enables efficient real-time joint AoA and AoD estimation, thereby enhancing network synchronization, localization, and environment sensing capabilities.

[0255] Below, the aforementioned hybrid AoA and AoD estimation systems are explained in more detail.

[0256] FIG. 16 is a diagram illustrating how BS estimates AoA and AoD for UE by using a parameter estimation algorithm and a DL (Deep Learning) based model (or, complex neural network model) in parallel.

[0257] 1. System Elements

[0258] In a system that estimates AoA and AoD for a UE by using a parameter estimation algorithm (e.g., an algorithm based on a mathematical model) and a complex neural network model in parallel, the BS and UE may include the following configurations. Meanwhile, the configurations of the BS and UE described below are presented as examples for convenience of explanation and are not limited thereto.

[0259] - The BS may include multiple antennas in a Uniform Linear Array (ULA) configuration. The BS may receive Orthogonal Frequency-Division Multiplexing (OFDM) symbols transmitted by the UE and receive signals reflected from said OFDM symbols for sensing. Alternatively, the BS may transmit Orthogonal Frequency-Division Multiplexing (OFDM) symbols to the UE and receive signals reflected from said OFDM symbols for sensing. Additionally, the BS may host both a deep learning-based estimation module and a parameter estimation algorithm-based estimation module for AoA and AoD estimation.

[0260] - The UE can be configured to receive OFDM symbols / signals (or downlink signals) from the BS and transmit initial data for channel estimation to the BS. The UE can optimize positioning while enhancing channel state information by cooperating with the BS to perform final angle estimation (e.g., AoA and AoD).

[0261] - The control unit (or BS) can perform data processing by centrally controlling a deep learning-based estimation module and a parameter estimation algorithm-based estimation module to integrate the output from the deep learning model and the output from the parameter estimation algorithm. In addition, the control unit can be responsible for managing communication between the BS and the UE, initiating processing, and fusion of results for optimal angle estimation (e.g., fusion of the output from the deep learning model and the output from the parameterized method). Meanwhile, the control unit may be a component included in the BS (or UE).

[0262] 2. Data Flow and Processing Steps

[0263] (1) Initial Communication Setup (S161)

[0264] The BS can perform an initial communication setup procedure with the UE associated with the ISAC. For example, the BS can start an ISAC session and signal the UE to start data exchange with the UE. In response, the UE can transmit a signal (or initial data frame) containing pilot symbols to the BS so that the BS can estimate the channel matrix.

[0265] For example, the BS may initiate an ISAC session and signal the UE to begin data exchange with the UE. In response, the UE may transmit a signal (or initial data frame) containing pilot symbols to the BS so that the BS can estimate the channel matrix.

[0266] (2) Channel Matrix Estimation and Preprocessing (S162)

[0267] BS can estimate a channel matrix associated with said UE using a signal (or initial data frame, or data) containing pilot symbols received from said UE. Meanwhile, BS can reduce the size and complexity of said channel matrix through a preprocessing step of applying coarse time estimation to focus on relevant features.

[0268] For example, BS can estimate the (approximate) start time of the received signal / data through preprocessing based on coarse time estimation, and can extract only valid signal components or major signal components from the estimated channel matrix based on the estimated start time. For example, BS can preprocess the channel matrix by removing elements / components corresponding to noise and interference signals, etc., from the channel matrix through preprocessing via coarse time estimation, so that only valid / major signal components are included in the channel matrix. Accordingly, the size and complexity of the input data for channel matrix estimation can be significantly reduced.

[0269] (3) Parallel processing (S163): Deep learning-based and parameter estimation algorithm-based

[0270] BS can perform parallel processing on the estimated channel matrix using a complex neural network model and a parameter estimation algorithm, respectively. The BS or control unit according to the proposed method can input the channel matrix (or preprocessed channel matrix) calculated in the preceding step into a deep learning model and a parameter estimation algorithm, respectively, and obtain values ​​for AoA and AoD estimated from the deep learning model and the parameter estimation algorithm, respectively.

[0271] 1) Deep learning model-based AoA and AoD estimation

[0272] A deep learning model for estimating AoA and AoD may include an input layer, a feature extraction layer, and an output layer. The deep learning model may be a pre-trained model capable of estimating AoA and AoD based on an input channel matrix (e.g., a preprocessed channel matrix). For example, the deep learning model may be trained using methods such as adjusting layer-specific parameters to predict / estimate the correlation between the channel matrix and AoD / AoA based on training data that includes a training channel matrix and label information for AoA and AoD corresponding to the training channel matrix. For example, the deep learning model may be trained based on the training data to minimize the value of a loss function based on its output information and a loss function for the label information. Here, the deep learning model may be a complex neural network model capable of estimating AoA and AoD based on a channel matrix.

[0273] - Input Layer: The transformed channel matrix can be input through the input layer of a pre-trained complex neural network model.

[0274] - Feature extraction layer(s): A complex convolutional layer extracts spatial features based on the input channel matrix and can non-linearly transform the extracted spatial features through CReLU (complex rectified linear units).

[0275] - Output layer: The output layer of the deep learning model can output predicted / estimated AoA and AoD with minimal computational complexity optimized for real-time scenarios. For example, the deep learning model can estimate AoA and AoD based on the non-linear transformed spatial features and output the estimated AoA and AoD through the output layer.

[0276] 2) Parameterized AoA and AoD estimation algorithms (or parameter estimation algorithms)

[0277] - Input transformation: A channel matrix (or a preprocessed channel matrix) can be transformed based on a known array geometry. For example, the known array geometry may refer to the spatial arrangement (array shape, array spacing) of an antenna array such as a BS. For example, a BS can define an array response vector based on the spatial arrangement of the antenna array and separate the array response (e.g., separate the transmit array response vector associated with AoA and the transmit array response vector associated with AoD) through the channel matrix and the array response vector. For example, the input channel matrix can be transformed into a transmit array response vector and a transmit array response vector based on the known array geometry.

[0278] - Eigenvalue Decomposition and Least Squares (LS) Fitting: Eigenvalues ​​for the channel matrix (or transmit array response vector) can be calculated by utilizing model knowledge or array geometry (e.g., antenna steering vector), and improved AoA and AoD estimates can be output through LS fitting of the calculated eigenvalues. In this case, theoretical benchmarks (e.g., High-accuracy output close to ) can be generated.

[0279] (4) Fusion and Optimization (S164)

[0280] The processing and control unit can combine the output of a deep learning model with the output of a parameter estimation algorithm. In this fusion stage, a balance can be achieved between computational efficiency (through the output of the deep learning model) and precision (through the output of the parameter estimation algorithm).

[0281] For example, the processing and control unit (or BS) can adaptively utilize the output of the deep learning model and the output according to the parameter estimation algorithm according to the current channel state through the environment analysis module as described above. For example, when the channel environment is in a static state (e.g., a change in channel state below a first threshold), the processing and control unit (or BS) can estimate AoA / AoD based on the static scenario by using only the estimation method according to the parameter estimation algorithm among the estimation method according to the deep learning model and the estimation method according to the parameter estimation algorithm. Or, when the channel environment is in a dynamic state (e.g., a change in channel state above a second threshold), the processing and control unit (or BS) can estimate AoA / AoD based on the dynamic scenario by using only the estimation method according to the deep learning model among the estimation method according to the deep learning model and the estimation method according to the parameter estimation algorithm. Alternatively, if the channel environment is in a hybrid state (e.g., a channel state change that is above a first threshold and below a second threshold), the processing and control unit (or BS) may periodically correct / supplement the estimation results of the deep learning model based on the hybrid scenario, based on the estimation results of the parameter estimation algorithm calculated periodically. For example, the processing and control unit (or BS) may estimate AoA / AoD by using the deep learning model's estimation method and the parameter estimation algorithm's estimation method in parallel, and may prioritize providing the AoA / AoD estimated according to the deep learning model's estimation method to the UE, and when the estimation of AoA / AoD according to the parameter estimation algorithm's estimation method is completed, it may supplement / correct the AoA / AoD estimated according to the deep learning model's estimation method based on the AoA / AoD estimated according to the parameter estimation algorithm's estimation method.For example, the processing and control unit (or BS) can correct / supplement the first AoA / AoD by performing a weighted sum of the first AoA / AoD estimated using a deep learning model and the second AoA / AoD estimated according to the estimation method of the parameter estimation algorithm. For example, the processing and control unit (or BS) can effectively combine the first AoA / AoD and the second AoA / AoD by applying a predefined first weight to the first AoA / AoD and a predefined second weight to the second AoA / AoD. Here, the first weight and the second weight may be values ​​determined according to changes in the channel state. For example, in the case where the channel state changes rapidly (e.g., when the channel environment changes above a certain threshold), the first weight may be set to a higher value than the second weight to increase the weight of the output of the deep learning model that is quickly calculated in response to rapid channel changes.

[0282] (5) Final output and synchronization (Synchronization; S165)

[0283] The control unit (or BS) can transmit AoA and AoD estimates optimized for alignment and environment awareness to the UE. For example, the BS can transmit AoA and AoD estimates optimized for time alignment (e.g., synchronization) and / or sensing of the surrounding environment to the UE.

[0284] In this case, the UE can achieve high-precision position measurement and accurate synchronization based on the estimated values ​​of optimal AoA and AoD provided by the BS. This method may be suitable for urban, vehicle, and other dynamic environments.

[0285] 3. Key Functional Requirements

[0286] - Efficient Channel Matrix Estimation: High-precision channel estimation from UE response data

[0287] - Complex Neural Network Architecture: Designed to process complex-value inputs with low latency

[0288] - Eigenvalue and LS Calculation: Achieve accurate angle estimation through parameter estimation algorithms based on antenna array structure (or model properties).

[0289] - Fusion Module: Balancing the efficiency of deep learning models or complex neural network architectures with the accuracy of parameter estimation algorithms to provide optimal AoA and AoD estimation.

[0290] - Real-time communication: Result transmission and low-latency processing for rapid synchronization with the UE

[0291] 4. Key Benefits

[0292] - Computational efficiency: Deep learning model-based methods significantly reduce computational requirements compared to existing methods (parameter estimation algorithms).

[0293] - Accuracy: The parameter estimation algorithm maintains high accuracy and closely matches theoretical limits.

[0294] - Scalability: The hybrid design can adapt to various ISAC scenarios and antenna configurations, making it suitable for high-demand environments.

[0295] Below, the method of estimating AoA / AoD through a parameter estimation algorithm and the method of estimating AoA / AoD through a complex neural network model, which is a deep learning model, are each explained in more detail.

[0296] FIG. 17 is a diagram illustrating a method for estimating AoA / AoD using a parameter estimation algorithm, and FIG. 18 is a diagram illustrating a method for estimating AoA / AoD using a complex neural network model.

[0297] A parameter estimation algorithm can estimate / calculate AoA and AoD associated with an input channel matrix (or a pre-processed channel matrix) by utilizing array steering vectors and model-based transformations. For example, the parameter estimation algorithm can estimate the AoA / AoD corresponding to the acquired channel matrix by using a mathematical model that parameterizes the relationship between the channel matrix and AoA / AoD based on array steering vectors, array geometry, etc.

[0298] Specifically, referring to FIG. 17, the BS can estimate a channel matrix based on a signal received from the UE and perform preprocessing (S171). For example, the BS can identify / confirm time delays associated with the received signal through a coarse time estimation technique and remove unnecessary components / elements from the estimated channel matrix based on the confirmed / confirmed time delays. For example, the BS can separate key data for estimating AoA / AoD from the estimated channel matrix through a coarse time estimation technique.

[0299] Next, the BS can define array geometry and define / apply integration parameters (S172). For example, the BS can define array geometry and integration parameters to use a parameter estimation algorithm. For example, the BS can define array geometry based on the ULA structure for the transmitting and receiving antenna arrays and integrate / define known parameters including antenna steering vectors, OFDM subcarrier structures, etc., for setting the channel model. Alternatively, the BS can define array geometry based on the transmitting and receiving antenna structures and integrate / define parameters such as antenna steering vectors, OFDM subcarrier structures, etc., related to the estimation of AoA / AoD, or define a mathematical model that integrates the parameters. Here, the antenna steering vector is a mathematical model that predicts the phase difference of a signal received in an antenna array, defines the phase difference of a signal received at each antenna of the antenna array according to the angle of arrival (AoA), and can be a vector / parameter that can be used to estimate the direction (AoA) of the received signal through said phase difference.

[0300] Next, the BS can perform signal processing for estimating AoA and AoD (S173). For example, to simplify the process of estimating AoA and AoD, the BS can perform matrix transformation according to sub-array sizes on the channel matrix for the received signal. Subsequently, the BS can estimate AoD / AoA by calculating eigenvalues ​​for the transformed channel matrix using an induced Hankel structure. Here, the Hankel structure can provide a mathematical model that reflects the temporal and spatial characteristics of the received signal. For example, the Hankel structure can be used for feature analysis of time-series signals or signal prediction by reflecting phase information or temporal changes of the signal. Additionally, the BS can estimate the angle of arrival (AoA) based on the transformed channel matrix by applying LS fitting based on the least squares method.

[0301] Next, BS can calculate a CRB related to the estimated AoD / AoA and, based on the calculated CRB, determine whether the estimated AoD / AoD satisfies the required accuracy (e.g., Performance Assessment of the estimated AoD / AoD) (S174). For example, BS can evaluate the accuracy of the estimation method of the parameter estimation algorithm using the CRB as a benchmark. For example, BS can verify whether the estimated AoA and AoD values ​​satisfy the requirement of accuracy close to the CRB at a specific SNR setting. Here, the CRB is a parameter / value related to the theoretical limit that presents the minimum variance achievable by an unbiased estimator (e.g., a lower bound that theoretically limits the minimum variance of the parameter estimation), and as an important criterion for determining the accuracy of the parameter estimation, it may be a value defined / calculated based on the Fisher information matrix derived from information theory. For example, the CRB can provide a criterion necessary to identify the performance limit when estimating parameters using given data.

[0302] Next, BS can analyze the computational complexity of the estimation of AoD / AoD through the parameter estimation algorithm (S175). For example, BS can evaluate the number of multiplications and additions required at each step for the estimation of AoD / AoD. In this case, BS can evaluate the efficiency of the parameter estimation algorithm by comparing the computational requirements of the parameter estimation algorithm based on the evaluated computational complexity with the complexity of the method based on the artificial neural network-based model (or deep learning model) described above.

[0303] Next, BS can output estimated / predicted AoA and AoD values ​​using a structured approach of a parameter estimation algorithm based on whole model knowledge (S176).

[0304] Below, we will explain in detail how BS calculates / obtains predicted AoA / AoD values ​​based on a received signal using a deep learning model based on a Complex Neural Network (hereinafter referred to as the Complex Neural Network model).

[0305] Referring to FIG. 18, the BS can estimate / calculate a channel matrix based on signals / data received from the UE and perform preprocessing on the estimated / calculated channel matrix (S181). As described above, the BS can perform preprocessing on the estimated / calculated channel matrix using a course time estimation technique.

[0306] BS initializes a complex neural network model and can input the preprocessed channel matrix as a complex-value input vector into a complex neural network-based model (S182). Here, the complex neural network model can be defined as a neural network model including complex-value layers (including complex-value convolutional layers and linear layers). For example, BS can input the preprocessed channel matrix as a complex-value input vector (flattened to streamline into the Complex Neural Network model) into the initialized complex neural network model.

[0307] The above BS can extract spatial features related to AoA and AoD from the input complex-value input vector (or channel matrix) using the hidden layers of the complex neural network model (S183). For example, the complex neural network model can extract spatial features related to AoA and AoD from the input complex-value input vector (or channel matrix) using the hidden layers. Here, the hidden layers may include complex convolutional layer(s), hidden layers with an activation function (CReLU) added, etc. The complex convolutional layer can extract key spatial features related to AoA and AoD by applying complex convolutions to the channel matrix. For example, the complex convolutional layer can generate a feature map related to AoA and AoD by applying multiple filters through convolution operations. The activation function (CReLU) can enhance spatial features (extracted from each of the complex convolutional layers) by using / applying the CReLU (Complex Rectified Linear Unit) activation function to the output of each of the complex convolutional layers. For example, the activation function can add nonlinearity to the output by applying CReLU to the output of each of the complex convolutional layers. Additional hidden layers (e.g., three hidden layers) can progressively refine feature mappings related to AoA (angle of arrival) and AoD (angle of departure). For example, the additional hidden layers can progressively refine the outputs (e.g., the outputs of the complex convolutional layers) to which the nonlinearity has been added.

[0308] The above BS can obtain predicted / estimated values ​​of the final AoA and AoD from the final complex linear layer of the complex neural network model (S184). For example, the final complex linear layer of the complex neural network model can convert the output of the final additional hidden layer into AoA and AoD values. In this way, the above BS can obtain predicted AoA and AoD values ​​based on the channel matrix by inputting the channel matrix of the received signal into the complex neural network model. In this case, the above BS can obtain the AoA and AoD values ​​quickly estimated through the complex neural network model, which has lower computational complexity than the method using the parameter change algorithm.

[0309] Meanwhile, the complex neural network model may be a model trained using training data that includes a channel matrix and actual AoA / AoD values ​​corresponding to the channel matrix (e.g., label information). For example, the complex neural network model may be trained to minimize the error between the AoA / AoD values ​​predicted for the channel matrix and the actual AoA / AoD values. For example, the complex neural network model may be trained to minimize the error between the AoA / AoD values ​​predicted for the channel matrix and the actual AoA / AoD values ​​through an objective function that uses the Mean Squared Error (MSE) as the loss function.

[0310] For example, the training data may include information on channel matrices simulated with various SNRs to ensure the generalization of the complex neural network model. Additionally, the complex neural network model may be trained using backpropagation and learning rate scheduling methods to prevent overfitting. For example, backpropagation can optimize weights and biases in the complex neural network model. For instance, a loss function can be used to calculate the error between the predicted value and the actual value of the complex neural network model. In this case, the gradient for each weight can be calculated by propagating the calculated error from the output layer to the input layer through the backpropagation method, and the complex neural network can be optimized by updating each weight using the gradient. The accuracy and efficiency of the complex neural network model trained in this way can be verified (e.g., performance evaluation) by comparing it with benchmarks such as the CRB lower bound.

[0311] Below, the method of transmitting and receiving signals between the BS and the UE related to the estimation of the aforementioned AoA / AoD is explained in detail.

[0312] FIG. 19 is a diagram illustrating a method for transmitting and receiving ISAC-related signals between a BS and a UE.

[0313] BS and UE can apply joint estimation of AoA and AoD in bistatic ISAC systems through complex neural network models and parameter estimation algorithms.

[0314] Referring to FIG. 19, BS can transmit a signal for an initial request to the UE (S191). For example, BS can transmit a message to the UE containing information such as the following.

[0315] -Hello, UE. Initiating Integrated Sensing and Communication (ISAC) for enhanced connection and environment sensing. This session will facilitate AoA and AoD estimation to optimize positioning and communication precision. Prepare for OFDM symbol exchange.

[0316] Next, the BS may receive a response message from the UE for acknowledgment and channel matrix setup (S192). For example, the BS may receive a response message from the UE containing an initial data frame for acknowledging the initial request and for obtaining / estimating the channel matrix. For example, the BS may receive a message from the UE containing information such as the following.

[0317] -Acknowledged, B.S. Channel is ready for symbol exchange. Preparing transmission parameters for optimized AoA and AoD estimation. Sending initial data frames to estimate channel matrix.

[0318] Alternatively, BS may send a message to the UE notifying it that the estimation of the channel matrix based on the above response message is complete. For example, the message may include information related to the following.

[0319] - "Channel matrix is ​​successfully received. We will employ both DL and parameterized methods for accuracy and computational efficiency. Please ensure steady transmission for accurate preprocessing."

[0320] Next, the BS can preprocess a channel matrix for the signal / data received from the UE and perform a transformation of the signal / data (S193). For example, the BS can preprocess a channel matrix calculated / estimated for the received signal / data and remove unnecessary elements on the channel matrix by applying coarse timing estimation. For example, the BS can estimate the accurate start time of the signal through coarse timing estimation and remove elements corresponding to noise parts (and / or signal elements / components according to the multipath environment) on the channel matrix based on the estimated start time. In this case, the preprocessed channel matrix can be input into a complex neural network model including a plurality of hidden layers. Additionally, regarding the parameter estimation algorithm, the BS can apply a matrix transformation based on the ULA structure to the preprocessed channel matrix to improve AoA and AoD precision.

[0321] Alternatively, when such preprocessing and matrix transformation are completed, BS may send a message to the UE indicating that the operation related to preprocessing has been completed. For example, BS may send a message to the UE containing information related to the following content.

[0322] -" Preprocessing is complete. Initial angle estimates are being computed for both Deep Learning-based and parameterized methods. Data quality is sufficient; continuing to estimate AoA and AoD."

[0323] Next, the BS can estimate AoA and / or AoD based on the preprocessing and matrix transformation (S194). Specifically, the BS can estimate AoA and AoD for the preprocessed channel matrix using a complex neural network model trained to estimate AoA and AoD based on the preprocessed channel matrix. Alternatively, the BS can determine the accuracy of the result output by the complex neural network model by comparing the MSE and CRB benchmark in the estimation of AoA and AoD using the complex neural network model. Additionally, the BS can perform an AoA and AoD estimation operation using a parameter estimation algorithm in parallel with the AoA and AoD estimation operation using the complex neural network model. For example, BS can estimate AoA and AoD that satisfy CRB values ​​by performing eigenvalue calculation and LS fitting on channel data (or, channel matrix and / or channel matrix after matrix transformation) (e.g., using a mathematical model defined based on array geometry, antenna clause vector, etc.).

[0324] Alternatively, when the estimation of AoA / AoD is completed by using such complex neural network models and parameter estimation algorithms in parallel, BS may send a message to the UE notifying it that the execution of the related operation has been completed. For example, BS may send a message to the UE containing information related to the following content.

[0325] - AoA and AoD estimation completed. Deep Learning -based estimation shows optimal efficiency with reduced complexity. Parameterized method confirms close accuracy to theoretical CRB. "Preparing to transmit estimated angles."

[0326] Next, BS can transmit information about AoA / AoD and complete the handshake (S195). For example, BS can transmit a message to the UE containing information about the following:

[0327] - AoA and AoD estimations finalized. Estimated angles transmitted for your alignment and resource optimization. You are now synchronized with enhanced precision for communication and sensing."

[0328] Alternatively, when the UE receives the message from the BS described above, it may send a message to the BS containing information about the following contents.

[0329] - "Received AoA and AoD estimations. Synchronization complete with enhanced channel state awareness. Ready for optimized transmission and environment sensing."

[0330] FIG. 20 is a diagram illustrating a method for a first device to estimate an AoA / AoD related to a second device.

[0331] Here, the first device may be the BS or control unit described above, and the second device may be a UE. In order to perform efficient and accurate bistatic ISAC operations with the second device, the first device may estimate the AoA / AoD for a signal received from the second device using at least one of the two estimation methods described above, and provide the estimated AoA / AoD to the second device. In this case, the second device may perform accurate synchronization and positioning operations with the first device based on the provided AoA / AoD, and the accuracy of the bistatic ISAC operation may be greatly improved through accurate synchronization and positioning operations.

[0332] Specifically, referring to FIG. 20, the first device can receive a signal from the second device (S201). As described above, the signal of the second device may be a signal in response to the reception of a request message (initial setup request message) transmitted by the first device to estimate / calculate a channel matrix associated with the second device.

[0333] Next, the first device can calculate / estimate a channel matrix based on a signal received from the second device (S203). As described above, the first device can perform preprocessing on the channel matrix.

[0334] Next, the first device can estimate at least one angle information among AoA (Angle of Arrival) and AoD (Angle of Departure) based on the channel matrix (S205). As described above, the first device can estimate the AoA / AoD corresponding to the channel matrix using at least one of a first estimation method that estimates AoA / AoD using a deep learning model based on the channel state associated with the second device and a second estimation method that estimates AoA / AoD using a parameter estimation algorithm. Here, the first estimation method is a method of estimating the AoA and AoD using a complex neural network model including a complex-value convolution layer and CReLU (complex activation functions) as described above, and the second estimation method is a method of estimating the AoA and AoD using the parameter estimation algorithm described above, and may be a method of estimating the AoA / AoD for the channel matrix through an algorithm using a mathematical model defined based on antenna steering vectors and array geometry.

[0335] Specifically, if the change in the channel state is less than a first threshold, the first device may estimate the AoA / AoD (e.g., at least one angle information) corresponding to the channel matrix using only the second estimation method among the first estimation method and the second estimation method. Alternatively, if the change in the channel state is greater than or equal to a second threshold, the first device may estimate the AoA / AoD (e.g., at least one angle information) corresponding to the channel matrix using only the first estimation method among the first estimation method and the second estimation method. Here, the second threshold may be a value higher than the first threshold. Alternatively, if the change in the channel state is greater than or equal to a first threshold but less than a second threshold, the first device may estimate the AoA / AoD (e.g., at least one angle information) corresponding to the channel matrix using both the first estimation method and the second estimation method in parallel. For example, the first device can estimate first angle information using the first estimation method, estimate second angle information using the second estimation method, and correct the first angle information based on the second angle information.

[0336] Next, the first device can transmit the at least one angle information to the second device (S207). In this case, the second device can perform synchronization with the first device and perform positioning using the at least one angle information.

[0337] As such, the proposed invention can effectively improve the accuracy of an ISAC system through a hybrid AoA and AoD estimation method based on a parameter estimation algorithm and a deep learning model. Furthermore, the proposed invention can effectively mitigate the disadvantages of each estimation method while utilizing their strengths by appropriately combining an angle estimation method based on a parameter estimation algorithm and an angle estimation method based on a deep learning model based on channel state changes to estimate AoA / AoD. Additionally, the proposed invention can ensure estimation efficiency, real-time capability, and accuracy above a certain level for AoA and AoD through the hybrid AoA and AoD estimation method.

[0338] The first device may perform operations according to some embodiments of the present specification. The first device may include at least one transceiver; at least one processor; and at least one computer memory operably connected to the at least one processor and, when executed, storing instructions that cause the at least one processor to perform operations according to some embodiments of the present specification. A processing device for the first device may include at least one processor; and at least one computer memory operably connected to the at least one processor and, when executed, storing instructions that cause the at least one processor to perform operations according to some embodiments of the present specification. A computer-readable (non-volatile or non-transient) storage medium may store at least one computer program including instructions that cause the at least one processor to perform operations according to some embodiments of the present specification when executed by at least one processor. A computer program or computer program product is written on at least one computer-readable (non-volatile or non-transient) storage medium and may include instructions that, when executed, cause (at least one processor) to perform operations according to some implementations of this specification.

[0339] In a method performed by the first device, or in the first device, the processing device, the computer-readable (non-volatile) storage medium, and / or the computer program product, the operations may include: receiving a signal from the second device and calculating a channel matrix for the signal; estimating at least one angle information among AoA (Angle of Arrival) and AoD (Angle of Departure) based on the channel matrix; and transmitting the at least one angle information to the second device, and the at least one angle information may be estimated through at least one estimation method determined based on the channel state associated with the signal, among a first estimation method using a parameter estimation algorithm and a second estimation method using a deep learning model.

[0340] A second device may perform operations according to some embodiments of the present specification. The second device may include at least one transceiver; at least one processor; and at least one computer memory operably connected to the at least one processor and, when executed, storing instructions that cause the at least one processor to perform operations according to some embodiments of the present specification. A processing device for the second device may include at least one processor; and at least one computer memory operably connected to the at least one processor and, when executed, storing instructions that cause the at least one processor to perform operations according to some embodiments of the present specification. A computer-readable (non-volatile or non-transient) storage medium may store at least one computer program including instructions that cause the at least one processor to perform operations according to some embodiments of the present specification when executed by at least one processor. A computer program or computer program product is written on at least one computer-readable (non-volatile or non-transient) storage medium and may include instructions that, when executed, cause (at least one processor) to perform operations according to some implementations of this specification.

[0341] In a method performed by the second device, or in the first device, the processing device, the computer-readable (non-volatile) storage medium, and / or the computer program product, the operations may include: transmitting a first signal to the first device for calculating a channel matrix; receiving a second signal containing at least one angle information among an Angle of Arrival (AoA) and an Angle of Departure (AoD) calculated based on the channel matrix; and synchronizing with the first device based on the at least one angle information for performing bistatic Integrated Sensing and Communication (ISAC), and the at least one angle information may be estimated through at least one estimation method determined based on the channel state associated with the signal, among a first estimation method using a parameter estimation algorithm and a second estimation method using a deep learning model.

[0342] The embodiments described above are combinations of the components and features of the present invention in a specific form. Each component or feature should be considered optional unless otherwise explicitly stated. Each component or feature may be implemented in a form not combined with other components or features. Additionally, it is possible to construct embodiments of the present invention by combining some components and / or features. The order of operations described in the embodiments of the present invention 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 obvious that embodiments may be constructed by combining claims that do not have an explicit citation relationship in the claims, or that new claims may be included by amendment after filing.

[0343] In this document, embodiments of the present invention are described primarily with a focus on the signal transmission and reception relationship between a terminal and a base station. This transmission and reception relationship is extended in the same or similar manner to signal transmission and reception between a terminal and a relay or between a base station and a relay. Specific operations described in this document as being performed by a base station may, in some cases, be performed by an upper node. That is, it is self-evident that various operations performed for communication with a terminal in a network consisting of multiple network nodes including a base station may be performed by the base station or other network nodes other than the base station. The base station may be replaced by terms such as fixed station, Node B, eNode B (eNB), and access point. Additionally, the terminal may be replaced by terms such as User Equipment (UE), Mobile Station (MS), and Mobile Subscriber Station (MSS).

[0344] Embodiments according to the present invention may be implemented by various means, for example, hardware, firmware, software, or a combination thereof. In the case of implementation by hardware, one embodiment of the present invention may be implemented by one or more ASICs (application specific integrated circuits), DSPs (digital signal processors), DSPDs (digital signal processing devices), PLDs (programmable logic devices), FPGAs (field programmable gate arrays), processors, controllers, microcontrollers, microprocessors, etc.

[0345] In the case of implementation by firmware or software, an embodiment of the present invention may be implemented in the form of a module, procedure, function, etc., that performs the functions or operations described above. Software code may be stored in a memory unit and executed by a processor. The memory unit may be located inside or outside the processor and may exchange data with the processor by various means already known.

[0346] It is obvious to those skilled in the art that the present invention may be embodied in other specific forms without departing from the features of the invention. Accordingly, the foregoing detailed description should not be interpreted restrictively in all respects but should be considered exemplary. The scope of the invention shall be determined by a reasonable interpretation of the appended claims, and all modifications within the equivalent scope of the invention are included within the scope of the invention.

[0347] The embodiments of the present invention as described above can be applied to various mobile communication systems.

Claims

1. In the method using the first device, A step of receiving a signal from a second device; A step of calculating a channel matrix for the above signal; A step of estimating at least one angle information among AoA (Angle of Arrival) and AoD (Angle of Departure) based on the above channel matrix; and The method includes the step of transmitting at least one angle information to the second device; A method in which at least one angle information is estimated through at least one estimation method determined among a first estimation method using a deep learning model and a second estimation method using a parameter estimation algorithm based on a channel state associated with the signal.

2. In Paragraph 1, A method characterized in that the above-mentioned first estimation method is a method of estimating the AoA and AoD using a complex neural network model including a complex-value convolution layer and CReLU (complex activation functions).

3. In Paragraph 1, A method characterized in that the above parameter estimation algorithm is an algorithm that estimates parameters using a predefined mathematical model based on antenna steering vectors and array geometry.

4. In Paragraph 1, A method characterized by estimating at least one angle information using only the second estimation method among the first estimation method and the second estimation method, based on the change in the channel state being less than a first threshold.

5. In Paragraph 1, A method characterized by estimating at least one angle information using only the second estimation method among the first estimation method and the second estimation method, based on the change in the channel state being greater than or equal to a second threshold.

6. In Paragraph 1, A method characterized by estimating at least one angle information using both the first estimation method and the second estimation method based on the change in the channel state being greater than or equal to a first threshold and less than a second threshold.

7. In Paragraph 1, A method characterized in that, based on the change in the channel state being greater than or equal to a first threshold and less than a second threshold, the first device estimates first angle information using the first estimation method, estimates second angle information using the second estimation method, and corrects the first angle information based on the second angle information.

8. In Paragraph 7, A method characterized in that the above at least one angle information includes the above corrected first angle information.

9. In Paragraph 1, A method characterized in that at least one angle information is transmitted to the second device for synchronization related to the performance of bistatic ISAC (Integrated Sensing and Communication).

10. In Paragraph 1, A method characterized in that the first device is a base station and the second device is a terminal.

11. A computer-readable recording medium storing a program for performing the method described in paragraph 1.

12. In the first device, RF (Radio Frequency) transceiver; and It includes a processor connected to the above RF transceiver, and The processor controls the RF transceiver to receive a signal from a second device, calculates a channel matrix for the signal, estimates at least one angle information among Angle of Arrival (AoA) and Angle of Departure (AoD) based on the channel matrix, and transmits the at least one angle information to the second device. A first device in which the above at least one angle information is estimated through at least one estimation method determined among a first estimation method using a deep learning model and a second estimation method using a parameter estimation algorithm based on a channel state associated with the signal.

13. In a processing device that controls the first device, At least one processor; and The first device comprises at least one memory connected to the at least one processor and storing instructions, wherein the instructions are executed by the at least one processor. Receives a signal from a second device, calculates a channel matrix for the signal, estimates at least one angle information among AoA (Angle of Arrival) and AoD (Angle of Departure) based on the channel matrix, and transmits the at least one angle information to the second device. A processing device in which the above at least one angle information is estimated through at least one estimation method determined among a first estimation method using a deep learning model and a second estimation method using a parameter estimation algorithm based on a channel state associated with the signal.

14. In the method using the second device, A step of transmitting a first signal to a first device for calculating a channel matrix; A step of receiving a second signal including at least one angle information among AoA (Angle of Arrival) and AoD (Angle of Departure) calculated based on the above channel matrix; and The method includes the step of synchronizing with the first device based on at least one angle information for performing bistatic ISAC (Integrated Sensing and Communication); A method in which at least one angle information is estimated through at least one estimation method determined among a first estimation method using a deep learning model and a second estimation method using a parameter estimation algorithm based on a channel state associated with the signal.

15. In the second device, RF (Radio Frequency) transceiver; and It includes a processor connected to the above RF transceiver, and The processor controls the RF transceiver to transmit a first signal to a first device for calculating a channel matrix, receives a second signal including at least one angle information among Angle of Arrival (AoA) and Angle of Departure (AoD) calculated based on the channel matrix, and synchronizes with the first device based on the at least one angle information for performing bistatic Integrated Sensing and Communication (ISAC). A second device in which the above at least one angle information is estimated through at least one estimation method determined among a first estimation method using a deep learning model and a second estimation method using a parameter estimation algorithm based on a channel state associated with the signal.

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