Apparatus and method for performing beam management using multiple predictions in wireless communication system

Abstract

According to various embodiments of the present disclosure, provided is a method performed by user equipment (UE) comprising the steps of: transmitting information on UE capability to a base station (BS); receiving, from the BS, configuration information for a first serving window and one or more candidate windows on the basis of the UE capability, wherein the first serving window and the one or more candidate windows include combinations of context information and beam information, and the context information includes location information of the UE; transmitting, to the BS, a report message of a beam quality measured for the first serving window and the one or more candidate windows on the basis of the configuration information; receiving, from the BS, information indicating switching from the first serving window to a second serving window, which is one of the one or more candidate windows; and transmitting or receiving a signal to or from the BS on the basis of beam information corresponding to the second serving window.

Description

Device and method for performing beam management using multiple prediction in a wireless communication system

[0001] The present disclosure relates to an apparatus and method for performing beam management using multiple predictions in a wireless communication system.

[0002]

[0003] This study aims to address the problem of high beam failure that occurs when AI / ML models in physical layer communication systems perform beam tracking via beam prediction without considering the motion probability distributions arising from the complex mobility characteristics of the terminal and the channel environment. It also seeks to resolve the issue of high signaling resource consumption between the base station and the terminal resulting from this ripple effect.

[0004] In an AI / ML model for beam prediction, the input is information that explicitly or implicitly reflects the channel conditions between the transmitter and the receiver, and the output is an output regarding the beam. Explicit information is channel estimation through a beam reference signal. Implicit information may be side information, such as location information of the transmitter and receiver terminals, that can reflect other channel information. The output of the AI / ML model is a predicted beam candidate. The beam information input may be a set of multiple beams spatially transmitted in a wireless channel, or multiple consecutive time information beams observed or estimated temporally.

[0005] The AI / ML model measures the beam within the observation window and, based on this, finds the highest quality beam within the prediction window. The elements for the observation window and the prediction window, respectively, can be configured as follows.

[0006] (1) Model Input - measurement of K (≥1) latest measurement instances

[0007] (2) L1-RSRP based on Set B

[0008] (3) L1-RSRP based on Set B + assistance information

[0009] (4) L1-RSRP based on Set B + DL Tx / Rx beam ID

[0010] (5) CIR based on Set B

[0011] (6) Model output

[0012] (7) Best (K, where K≥1) beam predictions for F future time instances where at least F=1

[0013] This proposal suggests that beam prediction can be viewed as a problem of predicting beams by taking into account terminal mobility, base station cell planning, and changes in the terrain around the base station. For specific mobile paths of UE1 and UE2 based on the cell planning of specific BS1 to BS4 base stations and beam radiation, a specific optimal beam sequence may exist along that specific path. The beam prediction problem can be viewed as a problem of estimating the probability distribution of channel beam sequences according to the mobile paths of numerous terminals.

[0014] When operating this distribution as a single window relying on the observation window, it may fail to reflect the uncertainty regarding the beam sequence probability distribution for various paths. If the probability of beam failure increases, the failure of the prediction window may increase. To leverage the advantages of the prediction window, if the base station and the terminal perform pre-agreed signaling, signaling re-establishment may be required in the event of failure. This is particularly true when the observation window is located at the terminal and the AI / ML model is at the base station, as signaling resources are required to transmit the observation window values ​​to the base station.

[0015] There is a correlation between the prediction window and the amount of signaling resources required to operate it. The larger the window and the higher the prediction accuracy, the more signaling resources can be saved between the base station and the terminal. This is because once a window is opened between the base station and the terminal, the information elements within the window are agreed upon in advance; if the prediction information matches within a long window, only event information needs to be exchanged using pre-agreed indicators. However, if a window reconstruction is performed due to a beam failure, this process must be repeated, resulting in significant signaling resource consumption. This issue becomes even more pronounced, particularly when additional information such as location data or sensor data is included within the prediction window.

[0016] Ultimately, if the prediction window fails to properly reflect the various mobility of the actual terminal, beam failure occurs, and the base station and the terminal must undergo an initial beam alignment process to form the window again, resulting in simultaneous problems of performance degradation and wasted signaling resources.

[0017]

[0018] To solve the aforementioned problems, the present disclosure provides an apparatus and method for performing beam management using multiple predictions in a wireless communication system.

[0019] The technical problems to be solved in this disclosure are not limited to those mentioned above, and other technical problems not mentioned will be clearly understood by those skilled in the art to which this disclosure belongs from the description below.

[0020]

[0021] According to various embodiments of the present disclosure, a method performed by a terminal (user equipment, UE) comprises: transmitting information of a terminal capability (UE capability) to a base station (BS); receiving configuration information for a first serving window and one or more candidate windows from the base station based on the terminal capability, wherein the first serving window and the one or more candidate windows include combinations of context information and beam information, and the context information includes location information of the terminal; transmitting a report message of measured beam quality for the first serving window and the one or more candidate windows to the base station based on the configuration information; receiving information from the base station instructing a switching from the first serving window to a second serving window, which is one of the candidate windows of the one or more candidate windows; and transmitting or receiving a signal with the base station based on beam information corresponding to the second serving window.

[0022] According to various embodiments of the present disclosure, a method performed by a base station (BS) comprises: transmitting information of a terminal capability (UE capability) to a terminal (user equipment, UE); transmitting configuration information for a first serving window and one or more candidate windows to the terminal based on the terminal capability, wherein the first serving window and the one or more candidate windows include combinations of context information and beam information, and the context information includes location information of the terminal; receiving a report message of measured beam quality for the first serving window and the one or more candidate windows from the terminal based on the configuration information; transmitting information instructing the terminal to switch from the first serving window to a second serving window, which is one of the candidate windows of the one or more candidate windows; and transmitting or receiving a signal to the terminal based on beam information corresponding to the second serving window.

[0023] According to various embodiments of the present disclosure, a terminal (user equipment, UE) comprises a transceiver, at least one processor, and at least one memory operably connected to said at least one processor and storing instructions for performing operations when executed by said at least one processor, wherein the operations include all steps of a method of operating the terminal according to various embodiments of the present disclosure.

[0024] According to various embodiments of the present disclosure, a base station (BS) is provided, comprising a transceiver, at least one processor, and at least one memory operably connected to said at least one processor and storing instructions for performing operations when executed by said at least one processor, wherein the operations include all steps of a method of operating a base station according to various embodiments of the present disclosure.

[0025] According to various embodiments of the present disclosure, a control device for controlling a terminal (user equipment, UE) comprises at least one processor and at least one memory operably connected to said at least one processor, said at least one memory stores instructions for performing operations based on execution by said at least one processor, said operations include all steps of a method of operating a terminal according to various embodiments of the present disclosure.

[0026] According to various embodiments of the present disclosure, a control device for controlling a base station (BS) comprises at least one processor and at least one memory operably connected to said at least one processor, said at least one memory stores instructions for performing operations based on execution by said at least one processor, said operations include all steps of a method of operating a base station according to various embodiments of the present disclosure.

[0027] According to various embodiments of the present disclosure, one or more non-transitory computer-readable media storing one or more instructions, wherein the one or more instructions perform operations based on execution by one or more processors, and said operations include all steps of a method of operation of a terminal (user equipment, UE) according to various embodiments of the present disclosure.

[0028] According to various embodiments of the present disclosure, one or more non-transitory computer-readable media storing one or more instructions, wherein the one or more instructions perform operations based on execution by one or more processors, and said operations include all steps of a method of operating a base station (BS) according to various embodiments of the present disclosure.

[0029]

[0030] To solve the aforementioned problems, the present disclosure may provide an apparatus and method for performing beam management using multiple predictions in a wireless communication system.

[0031]

[0032] The drawings attached below are intended to aid in understanding the present disclosure and may provide embodiments of the present disclosure together with the detailed description. However, the technical features of the present disclosure are not limited to specific drawings, and the features disclosed in each drawing may be combined with one another to form new embodiments. Reference numerals in each drawing may denote structural elements.

[0033] Figure 1 is a diagram illustrating physical channels used in 3GPP systems and an example of typical signal transmission.

[0034] Figure 2 is a diagram illustrating the system structure of a New Generation Radio Access Network (NG-RAN).

[0035] Figure 3 is a diagram illustrating the functional division between NG-RAN and 5GC.

[0036] Figure 4 is a diagram illustrating an example of a 5G usage scenario.

[0037] Figure 5 is a diagram illustrating an example of a communication structure that can be provided in a 6G system.

[0038] Figure 6 is a schematic diagram illustrating an example of a perceptron structure.

[0039] Figure 7 is a schematic diagram illustrating an example of a multilayer perceptron structure.

[0040] Figure 8 is a schematic diagram illustrating an example of a deep neural network.

[0041] Figure 9 is a schematic diagram illustrating an example of a convolutional neural network.

[0042] Figure 10 is a schematic diagram illustrating an example of a filter operation in a convolutional neural network.

[0043] Figure 11 is a schematic diagram illustrating an example of a neural network structure in which a recurrent loop exists.

[0044] Figure 12 is a schematic diagram illustrating an example of the operational structure of a recurrent neural network.

[0045] Figure 13 is a diagram illustrating an example of an electromagnetic spectrum.

[0046] Figure 14 is a diagram illustrating an example of a THz communication application.

[0047] FIG. 15 is a diagram illustrating an example of an electronic device-based THz wireless communication transceiver.

[0048] FIG. 16 is a diagram illustrating an example of a method for generating a THz signal based on an optical element.

[0049] FIG. 17 is a diagram illustrating an example of a THz wireless communication transceiver based on an optical element.

[0050] FIG. 18 is a diagram illustrating the structure of a photonic source-based transmitter.

[0051] Figure 19 is a diagram illustrating the structure of an optical modulator.

[0052] FIG. 20 is a diagram illustrating an example of an AI / ML model for beam prediction in a system applicable to the present disclosure.

[0053] FIG. 21 is a diagram illustrating an example of an AI / ML model for beam prediction in a system applicable to the present disclosure.

[0054] FIG. 22 is a diagram illustrating an example of a process for estimating the probability distribution of a channel's beam sequence according to the movement path of a terminal in a system applicable to the present disclosure.

[0055] FIG. 23 is a diagram illustrating an example of an AI / ML model for beam prediction in a system applicable to the present disclosure.

[0056] FIG. 24 is a diagram illustrating an example of a process for obtaining channel estimation values ​​based on a pre-trained generative model in a system applicable to the present disclosure.

[0057] FIG. 25 is a drawing illustrating an example of downlink beam measurement in a system applicable to the present disclosure.

[0058] FIG. 26 is a diagram illustrating an example of a process of performing downlink beam management using an SSB in a system applicable to the present disclosure.

[0059] FIG. 27 is a diagram illustrating an example of a process for performing downlink beam management using CSI-RS in a system applicable to the present disclosure.

[0060] FIG. 28 is a diagram illustrating an example of a process for performing downlink beam management using CSI-RS in a system applicable to the present disclosure.

[0061] FIG. 29 is a diagram illustrating an example of a receiving beam determination process of a terminal in a system applicable to the present disclosure.

[0062] FIG. 30 is a diagram illustrating an example of a transmission beam determination process of a base station in a system applicable to the present disclosure.

[0063] FIG. 31 is a diagram illustrating an example of resource allocation in the time and frequency domains related to the receiving beam determination process of a terminal in a system applicable to the present disclosure.

[0064] FIG. 32 is a diagram illustrating an example of a process in which a base station determines a receiving beam using an SRS in a system applicable to the present disclosure.

[0065] FIG. 33 is a diagram illustrating an example of a process in which a terminal determines a transmission beam using an SRS in a system applicable to the present disclosure.

[0066] FIG. 34 is a diagram illustrating an example of an uplink beam management process using SRS in a system applicable to the present disclosure.

[0067] FIG. 35 is a drawing illustrating an example of a multi-window structure for beam tracking in a system applicable to the present disclosure.

[0068] FIG. 36 is a drawing illustrating an example of a multi-window structure for beam tracking in a system applicable to the present disclosure.

[0069] FIG. 37 is a drawing illustrating an example of a multi-window structure for beam tracking in a system applicable to the present disclosure.

[0070] FIG. 38 is a diagram illustrating an example of a signaling process between a terminal and a base station in a system applicable to the present disclosure.

[0071] FIG. 39 is a drawing illustrating an example of a site-specific beam forming process in a system applicable to the present disclosure.

[0072] FIG. 40 is a diagram illustrating an example of a multi-window generation process using a generative AI / ML model in a system applicable to the present disclosure.

[0073] FIG. 41 is a diagram illustrating an example of a multi-window generation process using a multi-task AI / ML model in a system applicable to the present disclosure.

[0074] FIG. 42 is a diagram illustrating an example of a multi-window generation process using a multi-task AI / ML model in a system applicable to the present disclosure.

[0075] FIG. 43 is a drawing illustrating an example of a switching structure between multiple beam prediction windows in a system applicable to the present disclosure.

[0076] FIG. 44 is a diagram illustrating an example of a process for improving the probability of beam failure by serving window switching in a system applicable to the present disclosure.

[0077] FIG. 45 is a diagram illustrating an example of the operation process of a terminal in a system applicable to the present disclosure.

[0078] FIG. 46 is a diagram illustrating an example of the operation process of a base station in a system applicable to the present disclosure.

[0079] FIG. 47 illustrates a communication system (1) applicable to various embodiments of the present disclosure.

[0080] FIG. 48 illustrates a wireless device that can be applied to various embodiments of the present disclosure.

[0081] FIG. 49 illustrates another example of a wireless device that can be applied to various embodiments of the present disclosure.

[0082] FIG. 50 illustrates a signal processing circuit for a transmission signal.

[0083] FIG. 51 shows another example of a wireless device applicable to various embodiments of the present disclosure.

[0084] FIG. 52 illustrates a portable device applicable to various embodiments of the present disclosure.

[0085] FIG. 53 illustrates a vehicle or autonomous vehicle applicable to various embodiments of the present disclosure.

[0086] FIG. 54 illustrates a vehicle applicable to various embodiments of the present disclosure.

[0087] FIG. 55 illustrates an XR device applied to various embodiments of the present disclosure.

[0088] FIG. 56 illustrates a robot applicable to various embodiments of the present disclosure.

[0089] FIG. 57 illustrates an AI device applicable to various embodiments of the present disclosure.

[0090]

[0091] In various embodiments of the present disclosure, "A or B" may mean "only A," "only B," or "both A and B." Alternatively, in various embodiments of the present disclosure, "A or B" may be interpreted as "A and / or B." For example, in various embodiments of the present disclosure, "A, B or C" may mean "only A," "only B," "only C," or "any combination of A, B and C."

[0092] In various embodiments of the present disclosure, a slash ( / ) or a comma used may mean "and / or." For example, "A / B" may mean "A and / or B." Accordingly, "A / B" may mean "only A," "only B," or "both A and B." For example, "A, B, C" may mean "A, B or C."

[0093] In various embodiments of the present disclosure, "at least one of A and B" may mean "only A," "only B," or "both A and B." Additionally, in various embodiments of the present disclosure, the expressions "at least one of A or B" or "at least one of A and / or B" may be interpreted as synonymous with "at least one of A and B."

[0094] Additionally, in various embodiments of the present disclosure, “at least one of A, B and C” may mean “only A,” “only B,” “only C,” or “any combination of A, B and C.” Also, “at least one of A, B or C” or “at least one of A, B and / or C” may mean “at least one of A, B and C.”

[0095] Additionally, parentheses used in various embodiments of the present disclosure may mean "for example." Specifically, when indicated as "control information (PDCCH)," "PDCCH" may be proposed as an example of "control information." In other words, the "control information" of various embodiments of the present disclosure is not limited to "PDCCH," and "PDDCH" may be proposed as an example of "control information." Furthermore, even when indicated as "control information (i.e., PDCCH)," "PDCCH" may be proposed as an example of "control information."

[0096] Technical features described individually within one drawing in various embodiments of the present disclosure may be implemented individually or simultaneously.

[0097]

[0098] The following technologies can be used in various wireless access systems such as CDMA, FDMA, TDMA, OFDMA, and SC-FDMA. CDMA can be implemented using wireless technologies such as UTRA (Universal Terrestrial Radio Access) or CDMA2000. TDMA can be implemented using wireless technologies such as GSM (Global System for Mobile Communications), GPRS (General Packet Radio Service), and EDGE (Enhanced Data Rates for GSM Evolution). OFDMA can be implemented using wireless technologies such as IEEE 802.11 (Wi-Fi), IEEE 802.16 (WiMAX), IEEE 802-20, and E-UTRA (Evolved UTRA). UTRA is part of the UMTS (Universal Mobile Telecommunications System). 3GPP (3rd Generation Partnership Project) LTE (Long Term Evolution) is part of E-UMTS (Evolved UMTS) using E-UTRA, and LTE-A (Advanced) / LTE-A pro is an evolved version of 3GPP LTE. 3GPP NR (New Radio or New Radio Access Technology) is an evolved version of 3GPP LTE / LTE-A / LTE-A pro. 3GPP 6G may be an evolved version of 3GPP NR.

[0099]

[0100] For clarity of explanation, the description is based on 3GPP communication systems (e.g., LTE, NR, etc.), but the technical scope of this disclosure is not limited thereto. LTE refers to technology from 3GPP TS 36.xxx Release 8 onwards. Specifically, LTE technology from 3GPP TS 36.xxx Release 10 onwards is referred to as LTE-A, and LTE technology from 3GPP TS 36.xxx Release 13 onwards is referred to as LTE-A pro. 3GPP NR refers to technology from TS 38.xxx Release 15 onwards. 3GPP 6G may refer to technology from TS Release 17 and / or Release 18 onwards. "xxx" indicates a specific standard document number. LTE / NR / 6G may be collectively referred to as 3GPP systems. Regarding background technology, terms, abbreviations, etc. used in the description of this disclosure, reference may be made to matters described in standard documents published prior to this disclosure. For example, the following documents may be referenced.

[0101]

[0102] 3GPP LTE

[0103] - 36.211: Physical channels and modulation

[0104] - 36.212: Multiplexing and channel coding

[0105] - 36.213: Physical layer procedures

[0106] - 36.300: Overall description

[0107] - 36.331: Radio Resource Control (RRC)

[0108] 3GPP NR

[0109] - 38.211: Physical channels and modulation

[0110] - 38.212: Multiplexing and channel coding

[0111] - 38.213: Physical layer procedures for control

[0112] - 38.214: Physical layer procedures for data

[0113] - 38.300: NR and NG-RAN Overall Description

[0114] - 38.331: Radio Resource Control (RRC) protocol specification

[0115]

[0116] Physical Channel and Frame Structure

[0117] Physical channels and general signal transmission

[0118] Figure 1 is a diagram illustrating physical channels used in 3GPP systems and an example of typical signal transmission.

[0119] In a wireless communication system, a terminal receives information from a base station via a downlink (DL) and transmits information to the base station via an uplink (UL). The information transmitted and received by the base station and the terminal includes data and various control information, and various physical channels exist depending on the type and purpose of the information they transmit and receive.

[0120]

[0121] When the terminal is powered on or enters a new cell, it performs an initial cell search operation, such as synchronizing with the base station (S11). To do this, the terminal receives a Primary Synchronization Signal (PSS) and a Secondary Synchronization Signal (SSS) from the base station to synchronize with the base station and obtain information such as a cell ID. After that, the terminal receives a Physical Broadcast Channel (PBCH) from the base station to obtain broadcast information within the cell. Meanwhile, during the initial cell search phase, the terminal receives a Downlink Reference Signal (DL RS) to check the downlink channel status.

[0122]

[0123] A terminal that has completed initial cell search can obtain more specific system information by receiving a Physical Downlink Control Channel (PDCCH) and a Physical Downlink Control Channel (PDSCH) according to the information carried on the PDCCH (S12).

[0124]

[0125] Meanwhile, when connecting to a base station for the first time or when there are no wireless resources available for signal transmission, the terminal may perform a Random Access Procedure (RACH) with respect to the base station (S13 to S16). To this end, the terminal transmits a specific sequence as a preamble through a Physical Random Access Channel (PRACH) (S13 and S15), and may receive a response message (RAR (Random Access Response) message) for the preamble through a PDCCH and a corresponding PDSCH. In the case of a contention-based RACH, a Contention Resolution Procedure may additionally be performed (S16).

[0126]

[0127] A terminal that has performed the procedure described above may subsequently perform PDCCH / PDSCH reception (S17) and Physical Uplink Shared Channel (PUSCH) / Physical Uplink Control Channel (PUCCH) transmission (S18) as a general uplink / downlink signal transmission procedure. In particular, the terminal may receive Downlink Control Information (DCI) through the PDCCH. Here, the DCI includes control information such as resource allocation information for the terminal, and the format may be applied differently depending on the purpose of use.

[0128]

[0129] Meanwhile, control information transmitted by the terminal to the base station via the uplink or received by the terminal from the base station may include downlink / uplink ACK / NACK signals, CQI (Channel Quality Indicator), PMI (Precoding Matrix Index), RI (Rank Indicator), etc. The terminal may transmit the control information such as the above-mentioned CQI / PMI / RI via PUSCH and / or PUCCH.

[0130]

[0131] Structure of uplink and downlink channels

[0132] Downlink Channel Structure

[0133] The base station transmits a relevant signal to the terminal through the downlink channel described below, and the terminal receives the relevant signal from the base station through the downlink channel described below.

[0134]

[0135] (1) Physical Downlink Sharing Channel (PDSCH)

[0136] PDSCH carries downlink data (e.g., DL-shared channel transport block, DL-SCH TB), and modulation methods such as QPSK (Quadrature Phase Shift Keying), 16 QAM (Quadrature Amplitude Modulation), 64 QAM, and 256 QAM are applied. Codewords are generated by encoding the TB. PDSCH can carry multiple codewords. Scrambling and modulation mapping are performed for each codeword, and the modulation symbols generated from each codeword are mapped to one or more layers (Layer mapping). Each layer is mapped to a resource along with the DMRS (Demodulation Reference Signal) to generate an OFDM symbol signal, which is then transmitted through the corresponding antenna port.

[0137]

[0138] (2) Physical Downlink Control Channel (PDCCH)

[0139] A PDCCH carries downlink control information (DCI) and applies methods such as QPSK modulation. A single PDCCH consists of 1, 2, 4, 8, or 16 Control Channel Elements (CCEs) depending on the Aggregation Level (AL). A single CCE consists of 6 Resource Element Groups (REGs). A single REG is defined by one OFDM symbol and one (P)RB.

[0140] The terminal obtains the DCI transmitted over the PDCCH by performing decoding (also known as blind decoding) on ​​a set of PDCCH candidates. The set of PDCCH candidates decoded by the terminal is defined as the PDCCH Search Space set. The Search Space set may be a common search space or a UE-specific search space. The terminal may obtain the DCI by monitoring PDCCH candidates within one or more Search Space sets configured by the MIB or upper-layer signaling.

[0141]

[0142] Uplink Channel Structure

[0143] The terminal transmits a relevant signal to the base station through the uplink channel described below, and the base station receives the relevant signal from the terminal through the uplink channel described below.

[0144] (1) Physical uplink shared channel (PUSCH)

[0145] PUSCH carries uplink data (e.g., UL-shared channel transport block, UL-SCH TB) and / or uplink control information (UCI) and is transmitted based on a CP-OFDM (Cyclic Prefix - Orthogonal Frequency Division Multiplexing) waveform, a DFT-s-OFDM (Discrete Fourier Transform - spread - Orthogonal Frequency Division Multiplexing) waveform, etc. When PUSCH is transmitted based on a DFT-s-OFDM waveform, the terminal applies transform precoding to transmit PUSCH. For example, if transform precoding is not possible (e.g., transform precoding is disabled), the terminal transmits PUSCH based on a CP-OFDM waveform, and if transform precoding is enabled (e.g., transform precoding is enabled), the terminal can transmit PUSCH based on a CP-OFDM waveform or a DFT-s-OFDM waveform. PUSCH transmissions can be dynamically scheduled by UL grants within DCI or semi-statically scheduled based on upper layer (e.g., RRC) signaling (and / or Layer 1 (L1) signaling (e.g., PDCCH)) configured grants. PUSCH transmissions can be performed in a codebook-based or non-codebook-based manner.

[0146] (2) Physical uplink control channel (PUCCH)

[0147] A PUCCH carries uplink control information, HARQ-ACK and / or scheduling request (SR), and can be divided into multiple PUCCHs depending on the PUCCH transmission length.

[0148]

[0149] The following describes new radio access technology (new RAT, NR).

[0150] As more communication devices require larger communication capacities, the need for enhanced mobile broadband communication compared to existing radio access technology (RAT) is emerging. Furthermore, Massive Machine Type Communications (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 services / terminals sensitive to reliability and latency are being discussed. Thus, the introduction of next-generation radio access technology considering enhanced mobile broadband communication, massive MTC, and Ultra-Reliable and Low Latency Communication (URLLC) is being discussed, and for convenience in the various embodiments of this disclosure, such technology is referred to as new RAT or NR.

[0151]

[0152] Figure 2 is a diagram illustrating the system structure of a New Generation Radio Access Network (NG-RAN).

[0153] Referring to FIG. 2, the NG-RAN may include gNBs and / or eNBs that provide user plane and control plane protocol termination to terminals. FIG. 1 illustrates a case where only gNBs are included. The gNBs and eNBs are connected to each other via Xn interfaces. The gNBs and eNBs are connected to the 5G Core Network (5GC) via NG interfaces. More specifically, they are connected to the access and mobility management function (AMF) via NG-C interfaces and to the user plane function (UPF) via NG-U interfaces.

[0154]

[0155] Figure 3 is a diagram illustrating the functional division between NG-RAN and 5GC.

[0156] Referring to FIG. 3, the gNB can provide functions such as Inter Cell RRM, RB control, Connection Mobility Control, Radio Admission Control, Measurement Configuration & Provision, and Dynamic Resource Allocation. The AMF can provide functions such as NAS security and idle state mobility processing. The UPF can provide functions such as Mobility Anchoring and PDU processing. The SMF (Session Management Function) can provide functions such as terminal IP address allocation and PDU session control.

[0157]

[0158] Figure 4 is a diagram illustrating an example of a 5G usage scenario.

[0159] The 5G usage scenario illustrated in FIG. 4 is merely exemplary, and the technical features of various embodiments of the present disclosure may be applied to other 5G usage scenarios not illustrated in FIG. 4.

[0160] Referring to FIG. 4, the three major requirement areas of 5G include (1) enhanced mobile broadband (eMBB), (2) massive machine type communication (mMTC), and (3) ultra-reliable and low latency communications (URLLC). Some use cases may require multiple areas for optimization, while others may focus on only one key performance indicator (KPI). 5G supports these various use cases in a flexible and reliable manner.

[0161] eMBB focuses on overall improvements in data speed, latency, user density, and the capacity and coverage of mobile broadband access. eMBB aims for a throughput of approximately 10 Gbps. eMBB far surpasses basic mobile internet access and covers media and entertainment applications ranging from rich interactive tasks to cloud or augmented reality. Data is one of the core drivers of 5G, and dedicated voice services may not be seen for the first time in the 5G era. In 5G, voice is expected to be processed simply as an application using the data connection provided by the communication system. The main causes of the increased traffic volume are the growing size of content and the increase in the number of applications requiring high data transfer rates. Streaming services (audio and video), interactive video, and mobile internet connectivity will become more widely used as more devices connect to the internet. Many of these applications require always-on connectivity to push real-time information and notifications to users. Cloud storage and applications are growing rapidly on mobile communication platforms, applicable to both business and entertainment. Cloud storage is a specific use case driving the growth of uplink data transfer rates. 5G is also used for remote work in the cloud, requiring much lower end-to-end latency to maintain an excellent user experience when haptic interfaces are used. In entertainment, for example, cloud gaming and video streaming are another key factor increasing the demand for mobile broadband capabilities. Entertainment is essential on smartphones and tablets anywhere, including in highly mobile environments such as trains, cars, and airplanes. Other use cases include augmented reality for entertainment and information retrieval. Here, augmented reality requires very low latency and instantaneous data volumes.

[0162] mMTC is designed to enable communication between a large number of low-cost, battery-powered devices and is intended to support applications such as smart metering, logistics, field, and body sensors. mMTC aims for approximately 10 years of battery life and / or one million devices per square kilometer. mMTC enables seamless connectivity of embedded sensors across all sectors and is one of the most anticipated use cases for 5G. Potentially, the number of IoT devices is projected to reach 20.4 billion by 2020. Industrial IoT is one of the areas where 5G plays a key role in enabling smart cities, asset tracking, smart utilities, agriculture, and security infrastructure.

[0163] URLLC is ideal for automotive communications, industrial control, factory automation, remote operation, smart grids, and public safety applications by enabling devices and machines to communicate with high reliability, very low latency, and high availability. URLLC aims for a latency of approximately 1ms. URLLC encompasses new services that will transform industries through ultra-reliable / low-latency links, such as remote control of critical infrastructure and autonomous vehicles. Levels of reliability and latency are essential for smart grid control, industrial automation, robotics, and drone control and coordination.

[0164] Next, we will examine in more detail the multiple usage examples included within the triangle of Fig. 4.

[0165] 5G can complement Fiber-to-the-Home (FTTH) and cable-based broadband (or Docsis) as a means of providing streams rated at hundreds of megabits per second to gigabits per second. These high speeds may be required for virtual reality (VR) and augmented reality (AR), as well as for delivering TV at resolutions of 4K or higher (6K, 8K, and above). VR and AR applications include near-immersive sports matches. Certain applications may require special network configurations. For example, in the case of VR games, game companies may need to integrate core servers with the network operator's edge network servers to minimize latency.

[0166] The automotive sector is expected to become a significant new driving force for 5G, with numerous use cases for mobile communications within vehicles. For example, passenger entertainment requires both high capacity and high mobile broadband simultaneously. This is because future users will continue to expect high-quality connectivity regardless of their location or speed. Another use case in the automotive sector is the augmented reality dashboard. Through an augmented reality contrast board, drivers can identify objects in the dark overlaid on what they are seeing through the windshield. The augmented reality dashboard overlays information to inform the driver about the distance and movement of objects. In the future, wireless modules will enable communication between vehicles, information exchange between vehicles and supporting infrastructure, and information exchange between vehicles and other connected devices (e.g., devices accompanying pedestrians). Safety systems will allow drivers to drive more safely by guiding them to alternative courses of action, thereby reducing the risk of accidents. The next step will be remotely controlled vehicles or autonomous vehicles. This requires highly reliable and very fast communication between different autonomous vehicles and / or between vehicles and infrastructure. In the future, autonomous vehicles will perform all driving activities, allowing drivers to focus only on traffic anomalies that the vehicle itself cannot identify. The technical requirements for autonomous vehicles demand ultra-low latency and ultra-high reliability to increase traffic safety to a level that is unattainable by humans.

[0167] Smart cities and smart homes, referred to as a smart society, will be embedded with high-density wireless sensor networks. Distributed networks of intelligent sensors will identify conditions for maintaining the cost-effective and energy-efficient maintenance of the city or home. A similar setup can be implemented for each household. Temperature sensors, window and heating controllers, burglar alarms, and home appliances are all wirelessly connected. Many of these sensors typically require low data transmission rates, low power consumption, and low cost. However, for example, real-time HD video may be required by certain types of devices for surveillance.

[0168] The consumption and distribution of energy, including heat or gas, are becoming highly decentralized, requiring automated control of distributed sensor networks. Smart grids interconnect these sensors using digital information and communication technologies to collect information and act accordingly. Since this information may include the behavior of suppliers and consumers, smart grids can improve efficiency, reliability, economic viability, production sustainability, and the automated distribution of fuels such as electricity. Smart grids can also be viewed as other sensor networks with low latency.

[0169] The health sector possesses numerous applications that can benefit from mobile communications. Communication systems can support telemedicine, providing clinical care from remote locations. This helps reduce distance barriers and improves access to medical services that are not consistently available in remote rural areas. It is also used to save lives during critical medical care and emergencies. Mobile communication-based wireless sensor networks can provide remote monitoring and sensors for parameters such as heart rate and blood pressure.

[0170] Wireless and mobile communications are becoming increasingly important in industrial applications. Wiring involves high installation and maintenance costs. Therefore, the potential to replace cables with reconfigurable wireless links presents an attractive opportunity for many industries. However, achieving this requires wireless connections to operate with latency, reliability, and capacity comparable to cables, while also simplifying their management. Low latency and a very low probability of error are new requirements that 5G needs to meet.

[0171] Logistics and cargo tracking are important use cases for mobile communications that use location-based information systems to enable the tracking of inventory and packages anywhere. Use cases for logistics and cargo tracking typically require low data rates but may require wide coverage and reliable location information.

[0172] Hereinafter, examples of next-generation communication (e.g., 6G) that can be applied to the embodiments of various embodiments of the present disclosure will be described.

[0173]

[0174] 6G System General

[0175] The 6G (wireless communication) system aims for (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 vision of the 6G system can be seen in four aspects: intelligent connectivity, deep connectivity, holographic connectivity, and ubiquitous connectivity, and the 6G system can satisfy the requirements shown in Table 1 below. In other words, Table 1 is a table showing an example of the requirements for a 6G system.

[0176]

[0177] Per device peak data rate1TbpsE2E latency1msMaximum spectral efficiency100bps / HzMobility supportUp to 1000km / hrSatellite integrationFullyAIFullyAutonomous vehicleFullyXRFullyHaptic CommunicationFully

[0178] 6G systems can have key factors such as enhanced mobile broadband (eMBB), ultra-reliable low latency communications (URLLC), massive machine-type communication (mMTC), AI integrated communication, tactile internet, high throughput, high network capacity, high energy efficiency, low backhaul and access network congestion, and enhanced data security.

[0179] Figure 5 is a diagram illustrating an example of a communication structure that can be provided in a 6G system.

[0180] 6G systems are expected to have 50 times higher simultaneous wireless connectivity than 5G wireless communication systems. URLLC, a key feature of 5G, will become an even more dominant technology in 6G communication by providing end-to-end latency of less than 1ms. Unlike the frequently used area spectrum efficiency, 6G systems will exhibit significantly superior volume spectrum efficiency. 6G systems can provide very long battery life and advanced battery technologies for energy harvesting, meaning mobile devices in 6G systems will not require separate charging. New network characteristics in 6G may include the following.

[0181] - Satellite Integrated Network: 6G is expected to be integrated with satellites to provide a global mobile population. Integrating terrestrial, satellite, and airborne networks into a single wireless communication system is crucial for 6G.

[0182] - Connected Intelligence: Unlike previous generations of wireless communication systems, 6G is innovative and will update wireless evolution from "connected things" to "connected intelligence." AI can be applied at each stage of the communication process (or at each step of the signal processing described below).

[0183] - Seamless integration of wireless information and energy transfer: 6G wireless networks will transfer power to charge the batteries of devices such as smartphones and sensors. Therefore, wireless information and energy transfer (WIET) will be integrated.

[0184] - Ubiquitous Super 3D Connectivity: Connectivity to the network and core network functions of drones and very low Earth orbit satellites will create Super 3D connectivity in 6G ubiquitous.

[0185] Some general requirements regarding the new network characteristics of 6G mentioned above may be as follows.

[0186] - Small cell networks: The idea of ​​small cell networks was introduced to improve the quality of received signals in cellular systems as a result of increased throughput, energy efficiency, and spectrum efficiency. Consequently, small cell networks are an essential feature of communication systems for 5G and beyond 5G (5GB). Therefore, 6G communication systems also adopt the characteristics of small cell networks.

[0187] - Ultra-dense heterogeneous network: Ultra-dense heterogeneous networks will be another important characteristic of 6G communication systems. Multi-tier networks composed of heterogeneous networks improve overall QoS and reduce costs.

[0188] - High-capacity backhaul: Backhaul connections are characterized as high-capacity backhaul networks to support high-volume traffic. High-speed fiber optics and free-space optics (FSO) systems can be possible solutions to this problem.

[0189] - Radar technology integrated with mobile technology: High-precision localization (or location-based services) through communication is one of the functions of 6G wireless communication systems. Therefore, radar systems will be integrated with 6G networks.

[0190] - Softwarization and virtualization: Softwarization and virtualization are two important features that form the basis of the design process in 5GB networks to ensure flexibility, reconfigurability, and programmability. Additionally, billions of devices can be shared across a shared physical infrastructure.

[0191]

[0192] Core implementation technology of 6G systems

[0193]

[0194] Artificial Intelligence

[0195] The most critical and newly introduced technology for 6G systems is AI. AI was not involved in 4G systems. 5G systems will support AI partially or very limitedly. However, 6G systems will be supported by AI for complete automation. Advancements in machine learning will create more intelligent networks for real-time communication in 6G. Introducing AI into communications can streamline and enhance real-time data transmission. AI can determine how complex target tasks are performed using numerous analyses. In other words, AI can increase efficiency and reduce processing latency.

[0196] 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 M2M, machine-to-human, and human-to-machine communication. Furthermore, AI can enable rapid communication in Brain-Computer Interfaces (BCI). AI-based communication systems can be supported by metamaterials, intelligent structures, intelligent networks, intelligent devices, intelligent cognitive radios, self-sustaining wireless networks, and machine learning.

[0197] Recently, attempts to integrate AI with wireless communication systems have emerged, but these have primarily focused on the application and network layers, particularly deep learning in the field of wireless resource management and allocation. However, such research is increasingly advancing toward the MAC and physical layers, with attempts to combine deep learning with wireless transmission, particularly 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 in terms of 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.

[0198] Machine learning can be used for channel estimation and channel tracking, and for power allocation and interference cancellation in the physical layer of the downlink (DL). In addition, machine learning can be used for antenna selection, power control, and symbol detection in MIMO systems.

[0199] However, the application of DNNs for transmission at the physical layer may have the following problems.

[0200] 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 in specific channel environments can lead to contradictions between the dynamic characteristics and diversity of wireless channels.

[0201] 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.

[0202] Below, we will take a closer look at machine learning.

[0203] 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. Data and learning models are required for machine learning. Data learning methods in machine learning can be broadly classified into three types: supervised learning, unsupervised learning, and reinforcement learning.

[0204] 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 error from the output layer to the input layer in a direction that reduces the error.

[0205] Supervised learning uses training data with correct answers labeled, whereas unsupervised learning may not have correct answers labeled. That is, 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 (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 the connection weights of each node in each layer of the neural network can be updated according to this backpropagation. The amount of change in the connection weights of each node being updated can be determined by the learning rate. The neural network's calculations on the input data and the backpropagation of the error can constitute a learning cycle (epoch). The learning rate can be applied differently depending on the number of iterations of the neural network's learning cycle. For example, efficiency can be increased by using a high learning rate in the early stages of neural network training to enable the network to quickly achieve a certain level of performance, and accuracy can be increased by using a low learning rate in the later stages of training.

[0206] 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 preferable to perform learning using supervised learning rather than unsupervised learning or reinforcement learning.

[0207] A learning model corresponds to the human brain, and while the most basic linear model can be considered, a machine learning paradigm that uses highly complex neural network structures, such as artificial neural networks, as learning models is called deep learning.

[0208] The neural network cores used for learning methods are broadly classified into deep neural networks (DNN), convolutional deep neural networks (CNN), and recurrent Boltzmann machines (RNN).

[0209] An artificial neural network is an example of connecting multiple perceptrons.

[0210]

[0211] Figure 6 is a schematic diagram illustrating an example of a perceptron structure.

[0212] Referring to Fig. 6, the entire process of inputting an input vector x=(x1,x2,...,xd), multiplying each component by a weight (W1,W2,...,Wd), summing all the results, and then applying an activation function σ(·) is called a perceptron. A large artificial neural network structure can also apply input vectors to different multi-dimensional perceptrons by extending the simplified perceptron structure illustrated in Fig. 6. For convenience of explanation, input or output values ​​are referred to as nodes.

[0213] Meanwhile, the perceptron structure illustrated in Fig. 6 can be described as consisting of a total of three layers based on input and output values. An artificial neural network can be represented as shown in Fig. 7, in which there are H (d+1) dimensional perceptrons between the 1st layer and the 2nd layer, and K (H+1) dimensional perceptrons between the 2nd layer and the 3rd layer.

[0214]

[0215] Figure 7 is a schematic diagram illustrating an example of a multilayer perceptron structure.

[0216] The layer where the input vector is located is called the input layer, the layer where the final output value is located is called the output layer, and all layers located between the input and output layers are called hidden layers. Although the example in Fig. 7 shows three layers, the input layer is excluded when counting the actual number of layers in an artificial neural network, so it can be viewed as having a total of two layers. An artificial neural network is constructed by connecting perceptrons of basic blocks in a two-dimensional manner.

[0217] 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 and RNNs, 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).

[0218]

[0219] Figure 8 is a schematic diagram illustrating an example of a deep neural network.

[0220] The deep neural network illustrated in Fig. 8 is a multilayer perceptron composed of eight hidden layers plus eight output layers. The structure of the multilayer perceptron is referred to as a fully-connected neural network. In a fully-connected neural network, there are no connections between nodes located in the same layer, and connections exist only between nodes located in adjacent layers. A DNN has a fully-connected neural network structure and is composed of a combination of multiple hidden layers and activation functions, which 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.

[0221] Meanwhile, depending on how multiple perceptrons are connected to each other, various artificial neural network structures different from the aforementioned DNN can be formed.

[0222]

[0223] Figure 9 is a schematic diagram illustrating an example of a convolutional neural network.

[0224] In a DNN, nodes located within a single layer are arranged in a one-dimensional vertical direction. However, Figure 9 assumes a case where nodes are arranged two-dimensionally, with w nodes horizontally and h nodes vertically (the convolutional neural network structure of Figure 9). In this case, since a weight is applied for each connection during the connection process from a single input node to a hidden layer, a total of hYw weights must be considered. Since there are hYw nodes in the input layer, a total of h2w2 weights are required between two adjacent layers.

[0225] The convolutional neural network of Fig. 9 has a problem in which the number of weights increases exponentially with the number of connections. Therefore, instead of considering all mode connections between adjacent layers, it is assumed that there are small filters, and weighted sum and activation function operations are performed on the parts where filters overlap, as shown in Fig. 10.

[0226]

[0227] Figure 10 is a schematic diagram illustrating an example of a filter operation in a convolutional neural network.

[0228] A single filter has weights corresponding to its size, and the weights can be trained to extract and output specific features on an image as factors. In Fig. 10, a filter of size 3Y3 is applied to the top-left 3Y3 region of the input layer, and the output value resulting from the weighted sum and activation function operation for the corresponding node is stored in z22.

[0229] The above filter performs weighted sum and activation function operations while scanning the input layer and moving by a fixed interval horizontally and vertically, and places the output value at the current filter position. This method of operation is similar to the convolution operation on images in the field of computer vision, so a deep neural network with this structure is called a convolutional neural network (CNN), and the hidden layer generated as a result of the convolution operation is called a convolutional layer. In addition, a neural network having multiple convolutional layers is called a deep convolutional neural network (DCNN).

[0230] In the convolution layer, the number of weights can be reduced by calculating a weighted sum that includes only the nodes located within the area covered by the filter, starting from the node where the current filter is located. 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 2D area serves as an important judgment criterion. Meanwhile, multiple filters can be applied immediately before the convolution layer in a CNN, and multiple output results can be generated through the convolution operation of each filter.

[0231] Meanwhile, depending on the data attributes, there may be data where sequence characteristics are important. A structure that applies a method to an artificial neural network in which elements of the data sequence are input one by one at each timestep, taking into account the length variability and sequence relationships of such sequence data, and the output vector (hidden vector) of the hidden layer output at a specific timestep is input along with the next element in the sequence is called a recurrent neural network structure.

[0232]

[0233] Figure 11 is a schematic diagram illustrating an example of a neural network structure in which a recurrent loop exists.

[0234] Referring to Fig. 11, the recurrent neural network (RNN) is structured such that, in the process of inputting elements (x1(t), x2(t), ..., xd(t)) of a time point t in a data sequence into a fully connected neural network, the previous time point t-1 is input along with the hidden vector (z1(t-1), z2(t-1), ..., zH(t-1)), and a weighted sum and activation function are applied. The reason for passing the hidden vector to the next time point in this manner is that the information in the input vectors from previous time points is considered to be accumulated in the hidden vector of the current time point.

[0235]

[0236] Figure 12 is a schematic diagram illustrating an example of the operational structure of a recurrent neural network.

[0237] Referring to Fig. 12, the recurrent neural network operates on the input data sequence in a predetermined time sequence.

[0238] When the input vector (x1(t), x2(t), ..., xd(t)) at time point 1 is input into the recurrent neural network, the hidden vector (z1(1), z2(1), ..., zH(1)) is input together with the input vector (x1(2), x2(2), ..., xd(2)) at time point 2, and the vector (z1(2), z2(2), ..., zH(2)) of the hidden layer is determined through a weighted sum and activation function. This process is performed repeatedly up to time point 2, time point 3, ..., time point T.

[0239] Meanwhile, when multiple hidden layers are placed within a recurrent neural network, it is called a deep recurrent neural network (DRNN). Recurrent neural networks are designed to be usefully applied to sequence data (e.g., natural language processing).

[0240] In addition to DNN, CNN, and RNN, it includes various deep learning techniques such as Restricted Boltzmann Machine (RBM), Deep Belief Networks (DBN), and Deep Q-Network as neural network cores used for learning, and can be applied in fields such as computer vision, speech recognition, natural language processing, and speech / signal processing.

[0241] Recently, attempts to integrate AI with wireless communication systems have emerged, but these have primarily focused on the application and network layers, particularly deep learning in the field of wireless resource management and allocation. However, such research is increasingly advancing toward the MAC and physical layers, with attempts to combine deep learning with wireless transmission, particularly 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 in terms of 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.

[0242] THz (Terahertz) communication

[0243] Data transmission rates can be increased by expanding bandwidth. This can be achieved by using sub-THz communication with wide bandwidth and applying advanced large-scale MIMO technology. THz waves, also known as sub-millimeter radiation, generally refer to a frequency band between 0.1 THz and 10 THz with corresponding wavelengths ranging from 0.03 mm to 3 mm. The 100 GHz–300 GHz band range (Sub-THz band) is considered the primary portion of the THz band for cellular communication. Adding the Sub-THz band to the mmWave band increases 6G cellular communication capacity. Among the defined THz bands, the 300 GHz–3 THz band is located in the far-infrared (IR) frequency band. Although the 300 GHz–3 THz band is part of the broadband, it lies at the boundary of the broadband and immediately following the RF band. Therefore, this 300 GHz–3 THz band exhibits similarities to RF.

[0244]

[0245] Figure 13 is a diagram illustrating an example of an electromagnetic spectrum.

[0246] Key characteristics of THz communication include (i) widely available bandwidth to support very high data transmission rates, and (ii) high path loss occurring at high frequencies (highly directional antennas are indispensable). The narrow beam width generated by highly directional antennas reduces interference. The small wavelength of THz signals allows a much larger number of antenna elements to be integrated into devices and BSs operating in this band. This enables the use of advanced adaptive array technologies that can overcome range limitations.

[0247] Optical wireless technology

[0248] OWC technology has been planned for 6G communication in addition to RF-based communication for all possible device-to-access networks. These networks connect to network-to-backhaul / fronthaul network connections. Although OWC technology has already been in use since 4G communication systems, it will be used more widely to meet the demands of 6G communication systems. OWC technologies such as light fidelity, visible light communication, optical camera communication, and broadband-based FSO communication are already well-known technologies. Communication based on optical radio technology can provide very high data rates, low latency, and secure communication. LiDAR can also be utilized for ultra-high resolution 4D mapping in 6G communication based on broadband.

[0249] FSO Backhaul Network

[0250] The transmitter and receiver characteristics of an FSO system are similar to those of a fiber optic network. Therefore, data transmission in an FSO system is similar to that of a fiber optic system. Consequently, FSO can be a good technology for providing backhaul connectivity in 6G systems in conjunction with fiber optic networks. Using FSO enables very long-distance communication over distances of more than 10,000 km. FSO supports high-capacity backhaul connectivity for remote and non-remote areas such as the ocean, space, underwater, and isolated islands. FSO also supports cellular backhaul connectivity.

[0251] Massive MIMO technology

[0252] One of the key technologies for improving spectrum efficiency is the application of MIMO technology. As MIMO technology improves, spectrum efficiency also improves. Therefore, large-scale MIMO technology will be important in 6G systems. Since MIMO technology utilizes multiple paths, multiplexing technology and beam generation and operation technology suitable for the THz band must also be given important consideration to enable data signals to be transmitted through one or more paths.

[0253] blockchain

[0254] Blockchain will become a critical technology for managing massive amounts of data in future communication systems. As a form of distributed ledger technology, a distributed ledger is a database distributed across numerous nodes or computing devices. Each node replicates and stores an identical copy of the ledger. Blockchain is managed via a peer-to-peer (P2P) network and can exist without being managed by a centralized authority or server. Data in a blockchain is collected together and organized into blocks. These blocks are linked together and protected using encryption. Blockchain inherently complements large-scale IoT perfectly through enhanced interoperability, security, privacy, stability, and scalability. Therefore, blockchain technology provides various capabilities such as inter-device interoperability, large-scale data traceability, autonomous interaction with other IoT systems, and the large-scale connectivity stability of 6G communication systems.

[0255] 3D Networking

[0256] 6G systems integrate terrestrial and air networks to support vertically scalable user communications. 3D BS will be provided via low-orbit satellites and UAVs. By adding new dimensions in terms of altitude and associated degrees of freedom, 3D connectivity differs significantly from existing 2D networks.

[0257] Quantum communication

[0258] Unsupervised reinforcement learning of networks is promising in the context of 6G networks. Supervised learning methods cannot label the vast amount of data generated in 6G. Unsupervised learning does not require labeling. Therefore, this technology can be used to autonomously construct representations of complex networks. Combining reinforcement learning and unsupervised learning enables the operation of networks in a truly autonomous manner.

[0259] unmanned aerial vehicles

[0260] Unmanned Aerial Vehicles (UAVs) or drones will become a critical element in 6G wireless communication. In most cases, high-speed data wireless connectivity is provided using UAV technology. BS entities are installed on UAVs to provide cellular connectivity. UAVs possess specific capabilities not found in fixed BS infrastructure, such as easy deployment, robust line-of-sight links, and controlled degrees of freedom for mobility. During emergencies, such as natural disasters, the deployment of ground communication infrastructure is not economically feasible, and sometimes services cannot be provided in volatile environments. UAVs can easily handle these situations. UAVs will become a new paradigm in the field of wireless communication. This technology facilitates the three fundamental requirements of wireless networks: eMBB, URLLC, and mMTC. UAVs can also support various purposes, such as enhancing network connectivity, fire detection, disaster emergency services, security and surveillance, pollution monitoring, parking monitoring, and accident monitoring. Therefore, UAV technology is recognized as one of the most critical technologies for 6G communication.

[0261] Cell-free Communication

[0262] The tight integration of multiple frequencies and heterogeneous communication technologies is critical to 6G systems. Consequently, users can seamlessly move from one network to another without the need for any manual configuration on their devices. The best network among available communication technologies is automatically selected. This will break the limitations of the cellular concept in wireless communication. Currently, user movement from one cell to another in high-density networks causes excessive handovers, leading to handover failures, delays, data loss, and the "ping-pong" effect. 6G cell-free communication will overcome all of these issues and provide better QoS. Cell-free communication will be achieved through multi-connectivity and multi-tier hybrid technologies, as well as different heterogeneous radios on devices.

[0263] Wireless Information and Energy Transmission Integration

[0264] WIET uses the same fields and waves as wireless communication systems. In particular, sensors and smartphones will be charged using wireless power transmission during communication. WIET is a promising technology for extending the lifespan of wireless battery charging systems. Therefore, devices without batteries will be supported in 6G communication.

[0265] Integration of Sensing and Communication

[0266] Autonomous wireless networks are capable of continuously detecting dynamically changing environmental conditions and exchanging information between different nodes. In 6G, sensing will be tightly integrated with communication to support autonomous systems.

[0267] Integration of access backhaul networks

[0268] In 6G, the density of access networks will be enormous. Each access network will be connected via backhaul connections such as fiber optics and FSO networks. To cope with a very large number of access networks, there will be tight integration between access and backhaul networks.

[0269] Holographic beam forming

[0270] Beamforming is a signal processing procedure that adjusts an antenna array to transmit wireless signals in a specific direction. It is a subset of smart antennas or advanced antenna systems. Beamforming technology offers several advantages, such as a high call-to-noise ratio, interference prevention and rejection, and high network efficiency. Holographic Beamforming (HBF) is a new beamforming method that differs significantly from MIMO systems because it utilizes software-defined antennas. HBF will be a highly effective approach for the efficient and flexible transmission and reception of signals in multi-antenna communication devices in 6G.

[0271] Big data analysis

[0272] Big data analysis is a complex process for analyzing various large-scale data sets or big data. This process ensures perfect data management by uncovering information such as hidden data, unknown correlations, and customer preferences. Big data is collected from various sources, such as video, social networks, images, and sensors. This technology is widely used to process vast amounts of data in 6G systems.

[0273] Large Intelligent Surface (LIS)

[0274] THz band signals exhibit strong directivity, which can lead to numerous dead zones caused by obstacles. Consequently, LIS technology becomes important as it allows for the expansion of communication coverage, enhanced communication stability, and the provision of additional value-added services by installing LIS near these dead zones. An LIS is an artificial surface made of electromagnetic materials capable of altering the propagation of incoming and outgoing radio waves. While LIS can be viewed as an extension of massive MIMO, it differs from massive MIMO in its array structure and operational mechanism. Furthermore, LIS offers the advantage of low power consumption because it operates as a reconfigurable reflector with passive elements—that is, by passively reflecting signals without using an active RF chain. Additionally, since each passive reflector in an LIS must independently adjust the phase shift of the incident signal, this can be advantageous for wireless communication channels. By appropriately adjusting the phase shift through the LIS controller, the reflected signal can be collected at the target receiver to boost the received signal power.

[0275]

[0276] Terahertz (THz) wireless communication general

[0277]

[0278] THz wireless communication utilizes THz waves with a frequency of approximately 0.1 to 10 THz (1 THz = 10¹² Hz) for wireless communication, and can refer to terahertz (THz) band wireless communication using very high carrier frequencies of 100 GHz or higher. THz waves are located between the RF (Radio Frequency) / millimeter (mm) and infrared bands, and (i) they penetrate non-metallic / non-polar materials well compared to visible light / infrared light, and because their wavelengths are shorter than RF / millimeter waves, they have high directivity and can be beam focused. In addition, since the photon energy of THz waves is only a few meV, they have the characteristic of being harmless to the human body. The frequency bands expected to be used for THz wireless communication may be the D-band (110 GHz–170 GHz) or H-band (220 GHz–325 GHz) bands, which have low propagation loss due to molecular absorption in the air. Standardization discussions regarding THz wireless communication are being conducted primarily by the IEEE 802.15 THz working group in addition to 3GPP, and standard documents published by the IEEE 802.15 Task Group (TG3d, TG3e) may elaborate on or supplement the contents described in the various embodiments of this disclosure. THz wireless communication can be applied to wireless cognition, sensing, imaging, wireless communication, THz navigation, etc.

[0279]

[0280] Figure 14 is a diagram illustrating an example of a THz communication application.

[0281] As illustrated in FIG. 14, THz wireless communication scenarios can be classified into macro networks, micro networks, and nanoscale networks. In macro networks, THz wireless communication can be applied to vehicle-to-vehicle connections and backhaul / fronthaul connections. In micro networks, THz wireless communication can be applied to fixed point-to-point or multi-point connections, such as indoor small cells and wireless connections in data centers, and near-field communication, such as kiosk downloading.

[0282] Table 2 below shows an example of a technology that can be used in THz waves.

[0283] Transceivers DeviceAvailable immature: UTC-PD, RTD and SBDModulation and CodingLow order modulation techniques (OOK, QPSK), LDPC, Reed Soloman, Hamming, Polar, TurboAntennaOmni and Directional, phased array with low number of antenna elementsBandwidth69GHz (or 23 GHz) at 300GHzChannel modelsPartiallyData rate100GbpsOutdoor deploymentNoFree space lossHighCoverageLowRadio Measurements300GHz indoorDevice sizeFew micrometers

[0284] THz wireless communication can be classified based on the methods for generating and receiving THz. THz generation methods can be classified into optical or electronic device-based technologies.

[0285]

[0286] FIG. 15 is a diagram illustrating an example of an electronic device-based THz wireless communication transceiver.

[0287] Methods for generating THz using electronic components include using semiconductor devices such as Resonant Tunneling Diodes (RTDs), using local oscillators and multipliers, using Monolithic Microwave Integrated Circuits (MMICs) based on compound semiconductor High Electron Mobility Transistors (HEMTs), and using Si-CMOS based integrated circuits. In the case of Fig. 15, a doubler, tripler, or multiplier is applied to increase the frequency, and the signal passes through a subharmonic mixer and is radiated by the antenna. Since the THz band forms high frequencies, a multiplier is essential. Here, the multiplier is a circuit that produces an output frequency N times that of the input, matches it to the desired harmonic frequency, and filters out all other frequencies. Additionally, beamforming may be implemented by applying an array antenna or similar device to the antenna in Fig. 15. In Fig. 15, IF represents the intermediate frequency, tripler and multipler represent multipliers, PA represents the power amplifier, LNA represents the low noise amplifier, and PLL represents the phase-locked loop.

[0288]

[0289] FIG. 16 is a diagram illustrating an example of a method for generating a THz signal based on an optical element.

[0290] FIG. 17 is a diagram illustrating an example of a THz wireless communication transceiver based on an optical element.

[0291] Optical device-based THz wireless communication technology refers to a method of generating and modulating THz signals using optical devices. Optical device-based THz signal generation technology is a technique that generates ultra-high-speed optical signals using lasers and optical modulators, and converts them into THz signals using ultra-high-speed photodetectors. Compared to technology that uses only electronic devices, this technology makes it easier to increase the frequency, enables the generation of high-power signals, and allows for flat response characteristics over a wide frequency band. To generate optical device-based THz signals, a laser diode, a broadband optical modulator, and an ultra-high-speed photodetector are required, as shown in Fig. 16. In the case of Fig. 16, light signals from two lasers with different wavelengths are combined to generate a THz signal corresponding to the wavelength difference between the lasers. In FIG. 16, an optical coupler refers to a semiconductor device that uses light waves to transmit electrical signals in order to provide coupling with electrical isolation between circuits or systems, and a Uni-Travelling Carrier Photo-Detector (UTC-PD) is a type of photodetector that uses electrons as active carriers and reduces the electron travel time through bandgap grading. The UTC-PD is capable of photodetect at 150 GHz or higher. In FIG. 17, an Erbium-Doped Fiber Amplifier (EDFA) represents an erbium-doped fiber amplifier, a Photo Detector (PD) represents a semiconductor device capable of converting optical signals into electrical signals, an Optical Sub Assembly (OSA) represents an optical module that modularizes various optical communication functions (photoelectric conversion, electro-optical conversion, etc.) into a single component, and a Digital Storage Oscilloscope (DSO) represents a digital storage oscilloscope.

[0292]

[0293] The structure of a photoelectric converter (or photoelectric converter) is described with reference to FIGS. 18 and 19.

[0294] FIG. 18 is a diagram illustrating the structure of a photonic source-based transmitter.

[0295] Figure 19 is a diagram illustrating the structure of an optical modulator.

[0296] Generally, the phase of a signal can be changed by passing an optical source of a laser through an optical wave guide. At this time, data is carried by changing electrical characteristics through a microwave contact, etc. Therefore, the optical modulator output is formed as a modulated waveform. An O / E converter can generate THz pulses based on optical rectification by a nonlinear crystal, O / E conversion by a photoconductive antenna, and emission from a bundle of relativistic electrons. Terahertz pulses generated in the above manner can have lengths ranging from femtoseconds to picoseconds. The photoelectric converter (O / E converter) performs down-conversion by utilizing the non-linearity of the device.

[0297] When considering the usage of the terahertz spectrum, it is highly likely that multiple contiguous GHz bands will be used for fixed or mobile service applications for terahertz systems. According to outdoor scenario criteria, available bandwidth can be classified based on an oxygen attenuation of 10^2 dB / km in the spectrum up to 1 THz. Accordingly, a framework in which the available bandwidth is composed of multiple band chunks can be considered. As an example of the above framework, if the length of a terahertz pulse (THz pulse) for a single carrier is set to 50 ps, ​​the bandwidth (BW) becomes approximately 20 GHz.

[0298] Effective down-conversion from the infrared (IR) band to the terahertz (THz) band depends on how the nonlinearity of the photoelectric converter (O / E converter) is utilized. In other words, to achieve down-conversion to the desired terahertz band, it is required to design an O / E converter with the most ideal nonlinearity for transferring to that specific band. If an O / E converter that does not match the target frequency band is used, there is a high probability of errors occurring regarding the amplitude and phase of the corresponding pulse.

[0299] In a single-carrier system, a terahertz transceiver system can be implemented using a single photoelectric converter. Depending on the channel environment, in a multi-carrier system, as many photoelectric converters as there are carriers may be required. This phenomenon will be particularly pronounced in multi-carrier systems utilizing multiple broadbands according to the plans related to the aforementioned spectrum applications. In this regard, a frame structure for the multi-carrier system may be considered. A signal down-frequency converted based on a photoelectric converter can be transmitted in a specific resource region (e.g., a specific frame). The frequency domain of the specific resource region may include multiple chunks. Each chunk may consist of at least one component carrier (CC).

[0300]

[0301] Detailed description of various embodiments of the present disclosure

[0302] Various embodiments of the present disclosure will be described in more detail below.

[0303] The present disclosure provides an apparatus and method for performing beam management using multiple predictions in a wireless communication system.

[0304] In the present disclosure, ' / ' may be interpreted as 'and', 'or', or 'and / or' depending on the context.

[0305] In the present disclosure, 'AI / ML model' may be interpreted as 'AI and ML based model', 'AI or ML based model', or 'AI and / or ML based model' depending on the context.

[0306]

[0307] Background art for various embodiments of the present disclosure

[0308] The symbols, abbreviations, and terms used in this disclosure are as follows.

[0309] BMFO: beam management feedback optimization

[0310] BM: beam management

[0311] TRP: transmit receive point

[0312]

[0313] FIG. 20 is a diagram illustrating an example of an AI / ML model for beam prediction in a system applicable to the present disclosure.

[0314] This study aims to address the problem of high beam failure that occurs when AI / ML models in physical layer communication systems perform beam tracking via beam prediction without considering the motion probability distributions arising from the complex mobility characteristics of the terminal and the channel environment. It also seeks to resolve the issue of high signaling resource consumption between the base station and the terminal resulting from this ripple effect.

[0315] In an AI / ML model for beam prediction, the input is information that explicitly or implicitly reflects the channel conditions between the transmitter and the receiver, and the output is an output regarding the beam. Explicit information is channel estimation through a beam reference signal. Implicit information may be side information, such as location information of the transmitter and receiver terminals, that can reflect other channel information. The output of the AI / ML model is a predicted beam candidate. The beam information input may be a set of multiple beams spatially transmitted in a wireless channel, or multiple consecutive time information beams observed or estimated temporally.

[0316] For example, in the 3GPP standard specification TS38.843, an AI / ML model for beam prediction is modeled as shown in Fig. 20. Referring to Fig. 20, the model input is a Set B beam based on beam measurements. The Set B beam is divided into cases containing spatial information of the beam and cases containing temporal information. These are divided into case 1 and case 2, respectively. The model output is the beam probability in the Set A beam or the predicted beam.

[0317]

[0318] FIG. 21 is a diagram illustrating an example of an AI / ML model for beam prediction in a system applicable to the present disclosure.

[0319] In 3GPP technical document R1-2400376, Case 2 can be modeled as shown in Fig. 21.

[0320]

[0321] FIG. 22 is a diagram illustrating an example of a process for estimating the probability distribution of a channel's beam sequence according to the movement path of a terminal in a system applicable to the present disclosure.

[0322] The AI / ML model measures the beam within the observation window and, based on this, finds the highest quality beam within the prediction window. The elements for the observation window and the prediction window, respectively, can be configured as follows.

[0323] Model Input - measurement of K (≥1) latest measurement instances

[0324] L1-RSRP based on Set B

[0325] L1-RSRP based on Set B + assistance information

[0326] L1-RSRP based on Set B + DL Tx / Rx beam ID

[0327] CIR based on Set B

[0328] Model output

[0329] Best (K, where K≥1) beam predictions for F future time instances where at least F=1

[0330] This proposal suggests that beam prediction can be viewed as a problem of predicting beams by taking into account terminal mobility, base station cell planning, and changes in the terrain around the base station. The figure below shows the specific movement paths of UE1 and UE2 based on the cell planning of specific base stations BS1 through BS4 and beam radiation. A specific optimal beam sequence can be found along a specific movement path. The beam prediction problem can be viewed as a problem of estimating the probability distribution of channel beam sequences based on these numerous terminal movement paths.

[0331]

[0332] FIG. 23 is a diagram illustrating an example of an AI / ML model for beam prediction in a system applicable to the present disclosure.

[0333] When operating this distribution as a single window relying on the observation window, it may fail to reflect the uncertainty regarding the beam sequence probability distribution for various paths. If the probability of beam failure increases, the failure of the prediction window may increase. To leverage the advantages of the prediction window, if the base station and the terminal perform pre-agreed signaling, signaling re-establishment may be required in the event of failure. This is particularly true when the observation window is located at the terminal and the AI / ML model is at the base station, as signaling resources are required to transmit the observation window values ​​to the base station.

[0334] There is a correlation between the prediction window and the amount of signaling resources required to operate it. The larger the window and the higher the prediction accuracy, the more signaling resources can be saved between the base station and the terminal. This is because once a window is opened between the base station and the terminal, the information elements within the window are agreed upon in advance; if the prediction information matches within a long window, only event information needs to be exchanged using pre-agreed indicators. However, if a window reconstruction is performed due to a beam failure, this process must be repeated, resulting in significant signaling resource consumption. This issue becomes even more pronounced, particularly when additional information such as location data or sensor data is included within the prediction window.

[0335] Ultimately, if the prediction window fails to properly reflect the various mobility of the actual terminal, beam failure occurs, and the base station and the terminal must undergo an initial beam alignment process to form the window again, resulting in simultaneous problems of performance degradation and wasted signaling resources.

[0336] To explain this, Fig. 23 shows the first element inside the beam prediction window This illustrates two cases of compression based on the difference sign of the remaining elements. The first is the case where prediction is successful based on a large prediction window size C2. The second is the case where predictions fail three times within the prediction window, resulting in the window being reset to a small size three times. When prediction window resets occur frequently, signaling resources are wasted due to these resets.

[0337] FIG. 24 is a diagram illustrating an example of a process for obtaining channel estimation values ​​based on a pre-trained generative model in a system applicable to the present disclosure.

[0338] Beam tracing and additional information

[0339] Recent research has reported that multimodal information obtainable by a terminal can significantly contribute to beam tracking performance. Multimodal information refers to information that a terminal can acquire from sensors, GPS, cameras, radar, lidar, and the like.

[0340] The following shows various multimodal configurations based on each performance score.

[0341] Multimodal

[0342] Images 34 + Radar (Angle) 18 = 0.6992

[0343] Images 34 + Radar (Angle) 34 = 0.7206

[0344] Images 34 + Radar (Angle) 18 + Point-Cloud 18 = 0.6356

[0345] Images 34 + Radar (Angle) 34 + Point-Cloud 34 = 0.7358

[0346] Images34 + GPS = 0.7767

[0347] Images 34 + GPS (Image Augmentation) = 0.7127

[0348] Images 34 + GPS (Flipping Augmentation) = 0.7844

[0349] [arXiv:2309.11811 Multimodal Transformers for Wireless Communications: A Case Study in Beam Prediction]

[0350] According to the results of this study on beam tracking, performance can be observed converted into a score based on a combination of various multimodal information. It can be seen that the combination of GPS information and images holds the greatest utility.

[0351] Additionally, multimodal information may include data obtained from communication channels of different bands, uplink signal data in the case of a downlink, and historical signal processing data.

[0352] generative model

[0353] AI / ML models are classified into 'Generative AI,' which models the probability distribution of data and subsequently generates new data, and 'Discriminative AI,' which learns the probability distribution of data and estimates or distinguishes new data based on it. This classification is based on probabilistic models; Generative AI estimates the probability distribution of the original data to indicate the probability of a specific data point existing.

[0354] For example, generative pre-trained models are models that are used for specific tasks after being pre-trained on probability distributions for long sequences. A key example is the large language model GPT (Generative Pre-trained Transformer). After being pre-trained on probability distributions for vast amounts of sentence data, it enables the model to perform new tasks through fine-tuning, meta-learning, and in-context learning.

[0355] Recent research results on generative models for physical layer communication demonstrate that they can outperform discriminative models. Discriminative models, which are AI / ML models trained on data distributions, rely on the maximum likelihood when receiving new data to perform estimation or discrimination. In contrast, generative models present a probabilistic model of the data distribution; based on this model, they can generate optimal samples or provide probability distribution information to perform more sophisticated optimization.

[0356] For example, to solve high-dimensional channel estimation problems using deep neural networks, the following study obtains channel estimates by sampling the channel probability distribution from a pre-trained generative model, multiplying it by a measurement matrix, and optimizing it with the received signal. Recent research results demonstrate the superior channel estimation performance driven by the generative network's channel representation power.

[0357] [Ref: E. Balevi "High Dimensional Channel Estimation Using Deep Generative Networks", 2021]

[0358]

[0359] FIG. 25 is a drawing illustrating an example of downlink beam measurement in a system applicable to the present disclosure.

[0360] Beam management (BM)

[0361] BM procedures are L1 (layer 1) / L2 (layer 2) procedures for acquiring and maintaining a set of base station (e.g., gNB, TRP, etc.) and / or terminal (e.g., UE) beams that can be used for downlink (DL) and uplink (UL) transmission / reception, and may include the following procedures and terms.

[0362] (1) Beam measurement: An operation in which a base station or UE measures the characteristics of a received beamforming signal.

[0363] (2) Beam determination: An operation in which a base station or UE selects its own transmit beam (Tx beam) / receive beam (Rx beam).

[0364] (3) Beam sweeping: An operation that covers a spatial area using a transmitting and / or receiving beam for a set time interval in a predetermined manner.

[0365] (4) Beam report: An operation in which the UE reports information about the beam-formed signal based on beam measurements.

[0366] The BM procedure can be divided into (1) a DL BM procedure using an SS (synchronization signal) / PBCH (physical broadcast channel) Block or CSI-RS, and (2) a UL BM procedure using an SRS (sounding reference signal). Additionally, each BM procedure may include Tx beam sweeping to determine the Tx beam and Rx beam sweeping to determine the Rx beam.

[0367]

[0368] DL BM

[0369] The DL BM procedure may include (1) transmission to beamformed DL RS (reference signals) of the base station (e.g., CSI-RS or SS Block (SSB)) and (2) beam reporting of the terminal.

[0370] Here, beam reporting may include preferred DL RS ID(identifier)(s) and the corresponding L1-RSRP(Reference Signal Received Power).

[0371] The above DL RS ID may be SSBRI (SSB Resource Indicator) or CRI (CSI-RS Resource Indicator).

[0372] As shown in FIG. 25, the SSB beam and the CSI-RS beam can be used for beam measurement. The measurement metric is L1-RSRP per resource / block. The SSB is used for coarse beam measurement, while the CSI-RS can be used for fine beam measurement. The SSB can be used for both Tx beam sweeping and Rx beam sweeping.

[0373] Rx beam sweeping using SSBs can be performed as the UE changes the Rx beam across multiple SSB bursts for the same SSBRI. Here, one SS burst includes one or more SSBs, and one set of SS bursts includes one or more SSB bursts.

[0374]

[0375] FIG. 26 is a diagram illustrating an example of a process of performing downlink beam management using an SSB in a system applicable to the present disclosure.

[0376] Configuration for beam reporting using SSB is performed during CSI / beam configuration in the RRC connected state (or RRC connected mode).

[0377] The terminal receives a CSI-ResourceConfig IE from the base station containing a CSI-SSB-ResourceSetList containing SSB resources used for BM (S410).

[0378] The following shows an example of CSI-ResourceConfig IE, where BM configuration using SSB is not defined separately and SSB is configured as a CSI-RS resource.

[0379] [CSI-ResourceConfig IE]

[0380] -- ASN1START

[0381] -- TAG-CSI-RESOURCECONFIG-START

[0382]

[0383] CSI-ResourceConfig ::= SEQUENCE {

[0384] csi-ResourceConfigId CSI-ResourceConfigId,

[0385] csi-RS-ResourceSetList CHOICE {(csi-RS-ResourceSetList CHOICE {)

[0386] nzp-CSI-RS-SSB SEQUENCE {

[0387] nzp-CSI-RS-ResourceSetList SEQUENCE (にん (1..maxNrofNZP-CSI-RS-ResourceSetsPerConfig)) OF NZP-CSI-RS-ResourceSetId OPTIONAL,(nzp-CSI-RS-ResourceSetList SEQUENCE (SIZE (1..maxNrofNZP-CSI-RS-ResourceSetsPerConfig)) OF NZP-CSI-RS-ResourceSetId OPTIONAL

[0388] csi-SSB-ResourceSetList SEQUENCE (SIZE (1..maxNrofCSI-SSB-ResourceSetsPerConfig)) OF CSI-SSB-ResourceSetId OPTIONAL

[0389] },

[0390] csi-IM-ResourceSetList SEQUENCE (SIZE (1..maxNrofCSI-IM-ResourceSetsPerConfig)) OF CSI-IM-ResourceSetId (csi-IM-ResourceSetList SEQUENCE (SIZE (1..maxNrofCSI-IM-ResourceSetsPerConfig)) OF CSI-IM-ResourceSetId)

[0391] },

[0392]

[0393] bwp-Id BWP-Id,

[0394] resourceType 엄거형 { 비주전적, 전전구적, 주전적}, (resourceType ENUMERATED { aperiodic, semiPersistent, periodic},)

[0395] ...

[0396] }

[0397]

[0398] -- TAG-CSI-RESOURCECONFIGTOADDMOD-STOP

[0399] -- ASN1STOP

[0400]

[0401] In [CSI-ResourceConfig IE], the csi-SSB-ResourceSetList parameter represents a list of SSB resources used for beam management and reporting in a single CSI-RS resource set. Here, the SSB resource set can be set to {SSBx1, SSBx2, SSBx3, SSBx4, ...}. For example, the SSB index can be defined from 0 to 63.

[0402] The terminal receives an SSB resource from the base station based on the above CSI-SSB-ResourceSetList (S420).

[0403] If CSI-ReportConfig related to reporting for SSBRI and L1-RSRP is configured, the terminal reports the best SSBRI and the corresponding L1-RSRP to the base station (beam) (S430).

[0404] That is, if the reportQuantity of the above CSI-ReportConfig IE is set to 'ssb-Index-RSRP', the terminal reports the best SSBRI and the corresponding L1-RSRP to the base station.

[0405] And, if the terminal has a CSI-RS resource configured in the same OFDM symbol(s) as the SSB (SS / PBCH Block) and 'QCL-TypeD' is applicable, the terminal can assume that the CSI-RS and SSB are quasi-co-located in terms of 'QCL-TypeD'.

[0406] Here, the above QCL Type D may mean that the antenna ports are QCL-connected in terms of spatial Rx parameters. When a terminal receives multiple DL antenna ports that are in a QCL Type D relationship, it is acceptable to apply the same receiving beam. Additionally, the terminal does not expect CSI-RS to be established in an RE that overlaps with the RE of the SSB.

[0407]

[0408] FIG. 27 is a diagram illustrating an example of a process for performing downlink beam management using CSI-RS in a system applicable to the present disclosure.

[0409] FIG. 28 is a diagram illustrating an example of a process for performing downlink beam management using CSI-RS in a system applicable to the present disclosure.

[0410] DL BM using CSI-RS

[0411] Regarding the uses of CSI-RS, i) if the repetition parameter is set for a specific CSI-RS resource set and trs-Info is not set, CSI-RS is used for beam management. ii) if the repetition parameter is not set and trs-Info is set, CSI-RS is used for the tracking reference signal (TRS). iii) if the repetition parameter is not set and trs-Info is not set, CSI-RS is used for CSI acquisition.

[0412] This repetition parameter can only be set for CSI-RS resource sets associated with a CSI-ReportConfig set to L1 RSRP or a 'No Report (or None)' report.

[0413] If a terminal receives a CSI-ReportConfig with reportQuantity set to 'cri-RSRP', 'cri-SINR', or 'none', and if a CSI-ResourceConfig (higher layer parameter resourcesForChannelMeasurement) for channel measurement includes an NZP-CSI-RS-ResourceSet with a higher layer parameter 'repetition' set but without a higher layer parameter 'trs-Info', then the terminal can be configured with the same number of ports (1-port or 2-port) for all CSI-RS resources within the NZP-CSI-RS-ResourceSet using the higher layer parameter 'nrofPorts'.

[0414] (higher layer parameter) When repetition is set to 'ON', it relates to the terminal's Rx beam sweeping procedure. In this case, if the terminal receives an NZP-CSI-RS-ResourceSet with repetition set to 'ON', the terminal can assume that at least one CSI-RS resource within the NZP-CSI-RS-ResourceSet is transmitted through the same downlink spatial domain transmission filter. That is, at least one CSI-RS resource within the NZP-CSI-RS-ResourceSet is transmitted through the same Tx beam. Here, at least one CSI-RS resource within the NZP-CSI-RS-ResourceSet may be transmitted with different OFDM symbols. Additionally, the terminal does not expect to receive different periodicities in periodicityAndOffset from all CSI-RS resources within the NZP-CSI-RS-ResourceSet.

[0415] On the other hand, when Repetition is set to 'OFF', it relates to the base station's Tx beam sweeping procedure. In this case, when repetition is set to 'OFF', the terminal does not assume that at least one CSI-RS resource within NZP-CSI-RS-ResourceSet is transmitted to the same downlink spatial domain transmission filter. That is, at least one CSI-RS resource within NZP-CSI-RS-ResourceSet is transmitted through different Tx beams.

[0416] FIGS. 27 and 28 illustrate an example of a DL BM procedure using CSI-RS. FIG. 27 illustrates the Rx beam determination (or refinement) procedure of a terminal, and FIG. 28 illustrates the Tx beam sweeping procedure of a base station. Additionally, FIG. 27 shows the case where the repetition parameter is set to 'ON', and FIG. 28 shows the case where the repetition parameter is set to 'OFF'.

[0417]

[0418] FIG. 29 is a diagram illustrating an example of a receiving beam determination process of a terminal in a system applicable to the present disclosure.

[0419] The terminal receives an NZP CSI-RS resource set IE containing a higher layer parameter repetition from the base station via RRC signaling (S610). Here, the repetition parameter is set to 'ON'.

[0420] The terminal repeatedly receives CSI-RS resource(s) within an NZP CSI-RS resource set set to repetition 'ON' in different OFDM symbols through the same Tx beam (or DL ​​spatial domain transmission filter) of the base station (S620).

[0421] The terminal determines its own Rx beam (S630).

[0422] The terminal omits the CSI report (S640). In this case, the reportQuantity of the CSI report config can be set to 'No report (or None)'.

[0423] In other words, if the terminal is set to repetition 'ON', the CSI report can be omitted.

[0424]

[0425] FIG. 30 is a diagram illustrating an example of a transmission beam determination process of a base station in a system applicable to the present disclosure.

[0426] The terminal receives an NZP CSI-RS resource set IE containing a higher layer parameter repetition from the base station via RRC signaling (S710). Here, the repetition parameter is set to 'OFF' and is related to the base station's Tx beam sweeping procedure.

[0427] The terminal receives CSI-RS resources within the NZP CSI-RS resource set set to repetition 'OFF' through different Tx beams (DL spatial domain transmission filter) of the base station (S720).

[0428] The terminal selects (or determines) the best beam (S740)

[0429] The terminal reports the ID and associated quality information (e.g., L1-RSRP) for the selected beam to the base station (S740). In this case, the reportQuantity of the CSI report config can be set to 'CRI + L1-RSRP'.

[0430] That is, the terminal reports the CRI and the L1-RSRP for it to the base station when the CSI-RS is transmitted for BM.

[0431]

[0432] FIG. 31 is a diagram illustrating an example of resource allocation in the time and frequency domains related to the receiving beam determination process of a terminal in a system applicable to the present disclosure.

[0433] When repetition 'ON' is set in the CSI-RS resource set, multiple CSI-RS resources are repeatedly used by applying the same transmission beam, and when repetition 'OFF' is set in the CSI-RS resource set, different CSI-RS resources are transmitted using different transmission beams.

[0434]

[0435] DL BM related beam indication

[0436] The terminal may receive a list of up to M candidate Transmission Configuration Indication (TCI) states for the purpose of at least Quasi Co-location (QCL) indication via RRC configuration. Here, M may vary depending on the capability of the UE and, for example, may be 64.

[0437] Each TCI state can be configured as one RS set. Each ID of a DL RS for spatial QCL purposes (QCL Type D) within at least one RS set can refer to one of the DL RS types, such as SSB, P-CSI RS, SP-CSI RS, A-CSI RS, etc.

[0438] At a minimum, the initialization / update of the IDs of DL RS(s) within the RS set used for spatial QCL purposes can be performed through explicit signaling. The following shows an example of a TCI-State IE.

[0439] TCI-State IE는 하나 또는 두 개의 DL reference signal(RS) 대응하는 quasi co-location (QCL) type과 연관시킨다.

[0440] [TCI-State IE]

[0441] -- ASN1START

[0442] -- TAG-TCI-STATE-START

[0443]

[0444] TCI-State ::= SEQUENCE {

[0445] tci-StateId TCI-StateId,

[0446] qcl-Type1 QCL-Info,

[0447] qcl-Type2 QCL-Info OPTIONAL, -- Need R

[0448] ...

[0449] }

[0450]

[0451] QCL-Info ::= SEQUENCE {

[0452] cell ServCellIndex OPTIONAL, -- Need R

[0453] bwp-Id BWP-Id OPTIONAL, -- Cond CSI-RS-Indicated

[0454] referenceSignal CHOICE {

[0455] csi-rs NZP-CSI-RS-ResourceId,

[0456] ssb SSB-Index

[0457] },

[0458] qcl-Type ENUMERATED {typeA, typeB, typeC, typeD},

[0459] ...

[0460] }

[0461]

[0462] -- TAG-TCI-STATE-STOP

[0463] -- ASN1STOP

[0464]

[0465] In [TCI-State IE], the bwp-Id parameter indicates the DL BWP where the RS is located, and the cell parameter indicates the serving cell of the UE where the referenceSignal parameter is set. The RS may be located in a serving cell other than the serving cell where the TCI-state is set only when the qcl-Type parameter is set to typeC or typeD. The referenceSignal parameter indicates a reference signal to which QCL information is provided, and specifically, it may indicate a reference antenna port(s) that serve as a source of quasi-co-location for the corresponding target antenna port(s) or a reference signal containing such a port(s). The target antenna port(s) may be a CSI-RS, PDCCH DMRS, or PDSCH DMRS. For example, to indicate QCL reference RS information for an NZP CSI-RS, the corresponding TCI state ID may be indicated in the NZP CSI-RS resource configuration information. As another example, a TCI state ID can be specified in each CORESET setting to indicate QCL reference information for PDCCH DMRS antenna port(s). As another example, a TCI state ID can be specified via DCI to indicate QCL reference information for PDSCH DMRS antenna port(s).

[0466]

[0467] QCL(Quasi-Co Location)

[0468] An antenna port is defined such that the channel carrying a symbol on the antenna port can be inferred from the channel carrying another symbol on the same antenna port. If the property of the channel carrying a symbol on one antenna port can be inferred from the channel carrying a symbol on another antenna port, the two antenna ports can be said to be in a QC / QCL (quasi co-located or quasi co-location) relationship.

[0469] Here, the channel characteristics include one or more of delay spread, average delay, Doppler spread, frequency / Doppler shift, average received power, received timing / average delay, and spatial RX parameters. Here, the spatial Rx parameter refers to a spatial (received) channel characteristic parameter such as the angle of arrival.

[0470] The terminal may be configured with a list of up to M TCI-State configurations within the higher layer parameter PDSCH-Config to decode the PDSCH according to the detected PDCCH having the DCI intended for the terminal and the given serving cell. The said M depends on the UE capability.

[0471] Each TCI-State includes parameters for establishing a quasi-co-location relationship between one (or two) DL reference signals and the DM-RS port(s) of the PDSCH (or the DM-RS port(s) of the PDCCH or the CSI-RS port(s) of the CSI-RS resource).

[0472] Quasi co-location relationships are established by the higher layer parameter qcl-Type1 for the first DL RS and qcl-Type2 for the second DL RS (if set). For two DL RSs, the QCL types are not the same regardless of whether the references are the same DL RS or different DL RSs.

[0473] The quasi co-location type corresponding to each DL RS is given by the higher layer parameter qcl-Type of QCL-Info and can take one of the following values:

[0474] (1) 'QCL-TypeA': {Doppler shift, Doppler spread, average delay, delay spread}

[0475] (2) 'QCL-TypeB': {Doppler shift, Doppler spread}

[0476] (3) 'QCL-TypeC': {Doppler shift, average delay}

[0477] (4) 'QCL-TypeD': {Spatial Rx parameter}

[0478] For example, if the target antenna port is a specific NZP CSI-RS, the corresponding NZP CSI-RS antenna ports may be indicated / configured to be QCLed with a specific TRS from the perspective of QCL-Type A and with a specific SSB from the perspective of QCL-Type D. A terminal that receives such indication / configuration can receive the corresponding NZP CSI-RS using the Doppler and delay values ​​measured at the QCL-Type A TRS, and can apply the receiving beam used for receiving the QCL-Type D SSB to receiving the corresponding NZP CSI-RS.

[0479] The UE can receive an activation command via MAC CE signaling used to map up to eight TCI states to the codepoint of the DCI field 'Transmission Configuration Indication' of one CC / DL BWP or each set of CCs / DL BWPs.

[0480]

[0481] FIG. 32 is a diagram illustrating an example of a process in which a base station determines a receiving beam using an SRS in a system applicable to the present disclosure.

[0482] FIG. 33 is a diagram illustrating an example of a process in which a terminal determines a transmission beam using an SRS in a system applicable to the present disclosure.

[0483] UL BM

[0484] Depending on the terminal implementation, beam reciprocity (or beam correspondence) between the Tx beam and Rx beam may or may not be established in the UL BM. If reciprocity between the Tx beam and Rx beam is established at both the base station and the terminal, the UL beam pair can be matched through the DL beam pair. However, if reciprocity between the Tx beam and Rx beam is not established at either the base station or the terminal, a process for determining the UL beam pair is required separately from the determination of the DL beam pair.

[0485] In addition, even when both the base station and the terminal maintain beam correspondence, the base station can use the UL BM procedure to determine the DL Tx beam without the terminal requesting a report of the preferred beam.

[0486] UL BM can be performed via beamformed UL SRS transmission, and whether UL BM is applied to an SRS resource set is determined by the usage (higher layer parameter). When usage is set to 'BeamManagement(BM)', only one SRS resource can be transmitted to each of multiple SRS resource sets in a given time instant. However, SRS resources within different SRS resource sets that have the same time domain operation within the same BWP can be transmitted simultaneously.

[0487] The terminal may receive one or more Sounding Reference Symbol (SRS) resource sets configured by the (higher layer parameter) SRS-ResourceSet or SRS-PosResourceSet-r16 (via higher layer signaling, RRC signaling, etc.). For each SRS resource set configured by the SRS-ResourceSet, the UE may be configured with K≥1 SRS resources (higher later parameter SRS-resource). Here, K is a natural number, and the maximum value of K is indicated by SRS_capability. When the SRS is configured by the SRS-PosResourceSet-r16, the UE may be configured with K SRS resources, and the maximum value of K may be 16.

[0488] Similar to DL BM, the UL BM procedure can also be divided into the terminal's Tx beam sweeping and the base station's Rx beam sweeping.

[0489] FIGS. 32 and FIGS. 33 illustrate an example of a UL BM procedure using SRS. FIG. 32 illustrates the Rx beam determination procedure of a base station, and FIG. 33 illustrates the Tx beam sweeping procedure of a terminal.

[0490]

[0491] FIG. 34 is a diagram illustrating an example of an uplink beam management process using SRS in a system applicable to the present disclosure.

[0492] The terminal receives RRC signaling (e.g., SRS-Config IE) from the base station that includes a usage parameter (higher layer parameter) set to 'beam management' (S1010).

[0493] The following is an example of an SRS-Config IE (Information Element), which is used to configure SRS transmission settings or SRS measurements for cross-link interference (CLI). An SRS-Config IE includes a list of SRS-Resources and a list of SRS-ResourceSets. Each SRS resource set represents a set of SRS-resources.

[0494] The network can trigger the transmission of an SRS resource set using the configured aperiodicSRS-ResourceTrigger (L1 DCI).

[0495] [SRS-Config IE]

[0496] -- ASN1START

[0497] -- TAG-MAC-CELL-GROUP-CONFIG-START

[0498]

[0499] SRS-Config ::= SEQUENCE {

[0500] srs-ResourceSetToReleaseList SEQUENCE (SIZE(1..maxNrofSRS-ResourceSets)) OF SRS-ResourceSetId OPTIONAL, -- Need N

[0501] srs-ResourceSetToAddModList SEQUENCE (SIZE(1..maxNrofSRS-ResourceSets)) OF SRS-ResourceSet OPTIONAL, -- Need N

[0502]

[0503] srs-ResourceToReleaseList SEQUENCE (SIZE(1..maxNrofSRS-Resources)) OF SRS-ResourceId OPTIONAL, -- Need N

[0504] srs-ResourceToAddModList SEQUENCE (SIZE(1..maxNrofSRS-Resources)) OF SRS-Resource OPTIONAL, -- Need N

[0505]

[0506] tpc-Accumulation ENUMERATED {disabled} OPTIONAL, -- Need S

[0507] ...

[0508] }

[0509]

[0510] SRS-ResourceSet ::= SEQUENCE {

[0511] srs-ResourceSetId SRS-ResourceSetId,

[0512] srs-ResourceIdList SEQUENCE (SIZE(1..maxNrofSRS-ResourcesPerSet)) OF SRS-ResourceId OPTIONAL, -- Cond Setup

[0513] resourceType CHOICE {

[0514] aperiodic SEQUENCE {

[0515] aperiodicSRS-ResourceTrigger INTEGER (1..maxNrofSRS-TriggerStates-1),

[0516] csi-RS NZP-CSI-RS-ResourceId OPTIONAL, -- Cond NonCodebook

[0517] slotOffset INTEGER (1..32) OPTIONAL, -- Need S

[0518] ...,

[0519] [[

[0520] aperiodicSRS-ResourceTriggerList SEQUENCE (SIZE(1..maxNrofSRS-TriggerStates-2))

[0521] OF INTEGER (1..maxNrofSRS-TriggerStates-1) OPTIONAL -- Need M

[0522] ]]

[0523] },

[0524] semi-persistent SEQUENCE {

[0525] associatedCSI-RS NZP-CSI-RS-ResourceId OPTIONAL, -- Cond NonCodebook

[0526] ...

[0527] },

[0528] periodic SEQUENCE {

[0529] associatedCSI-RS NZP-CSI-RS-ResourceId OPTIONAL, -- Cond NonCodebook

[0530] ...

[0531] }

[0532] },

[0533] usage ENUMERATED {beamManagement, codebook, nonCodebook, antennaSwitching},

[0534] alpha Alpha OPTIONAL, -- Need S

[0535] p0 INTEGER (-202..24) OPTIONAL, -- Cond Setup

[0536] pathlossReferenceRS PathlossReferenceRS-Config OPTIONAL, -- Need M

[0537] srs-PowerControlAdjustmentStates ENUMERATED { sameAsFci2, separateClosedLoop} OPTIONAL, -- Need S

[0538] ...,

[0539] [[

[0540] pathlossReferenceRSList-r16 SetupRelease { PathlossReferenceRSList-r16} OPTIONAL -- Need M

[0541] ]]

[0542] }

[0543]

[0544] PathlossReferenceRS-Config ::= CHOICE {

[0545] ssb-Index SSB-Index,

[0546] csi-RS-Index NZP-CSI-RS-ResourceId

[0547] }

[0548] SRS-PosResourceSet-r16 ::= SEQUENCE {

[0549] srs-PosResourceSetId-r16 SRS-PosResourceSetId-r16,

[0550] srs-PosResourceIdList-r16 SEQUENCE (SIZE(1..maxNrofSRS-ResourcesPerSet)) OF SRS-PosResourceId-r16

[0551] OPTIONAL, -- Cond Setup

[0552] resourceType-r16 CHOICE {

[0553] aperiodic-r16 SEQUENCE {

[0554] aperiodicSRS-ResourceTriggerList-r16 SEQUENCE (SIZE(1..maxNrofSRS-TriggerStates-1))

[0555] OF INTEGER (1..maxNrofSRS-TriggerStates-1) OPTIONAL, -- Need M

[0556] ...

[0557] },

[0558] semi-persistent-r16 SEQUENCE {

[0559] ...

[0560] },

[0561] periodic-r16 SEQUENCE {

[0562] ...

[0563] }

[0564] },

[0565] alpha-r16 Alpha OPTIONAL, -- Need S

[0566] p0-r16 INTEGER (-202..24) OPTIONAL, -- Cond Setup

[0567] pathlossReferenceRS-Pos-r16 CHOICE {

[0568] ssb-IndexServing-r16 SSB-Index,

[0569] ssb-Ncell-r16 SSB-InfoNcell-r16,

[0570] dl-PRS-r16 DL-PRS-Info-r16

[0571] } OPTIONAL, -- Need M

[0572] ...

[0573] }

[0574]

[0575] SRS-SpatialRelationInfo ::= SEQUENCE {

[0576] servingCellId ServCellIndex OPTIONAL, -- Need S

[0577] referenceSignal CHOICE {

[0578] ssb-Index SSB-Index,

[0579] csi-RS-Index NZP-CSI-RS-ResourceId,

[0580] srs SEQUENCE {

[0581] resourceId SRS-ResourceId,

[0582] uplinkBWP BWP-Id

[0583] }

[0584] }

[0585] }

[0586]

[0587] SRS-SpatialRelationInfoPos-r16 ::= CHOICE {

[0588] servingRS-r16 SEQUENCE {

[0589] servingCellId ServCellIndex OPTIONAL, -- Need S

[0590] referenceSignal-r16 CHOICE {

[0591] ssb-IndexServing-r16 SSB-Index,

[0592] csi-RS-IndexServing-r16 NZP-CSI-RS-ResourceId,

[0593] srs-SpatialRelation-r16 SEQUENCE {

[0594] resourceSelection-r16 CHOICE {

[0595] srs-ResourceId-r16 SRS-ResourceId,

[0596] srs-PosResourceId-r16 SRS-PosResourceId-r16

[0597] },

[0598] uplinkBWP-r16 BWP-Id

[0599] }

[0600] }

[0601] },

[0602] ssb-Ncell-r16 SSB-InfoNcell-r16,

[0603] dl-PRS-r16 DL-PRS-Info-r16

[0604] }

[0605]

[0606] SRS-ResourceId ::= INTEGER (0..maxNrofSRS-Resources-1)

[0607]

[0608] In [SRS-Config IE], 'usage' represents a higher-layer parameter indicating whether the SRS resource set is used for beam management, or for codebook-based or non-codebook-based transmission. The usage parameter corresponds to the L1 parameter 'SRS-SetUse'. 'spatialRelationInfo' or 'spatialRelationInfoPos-r16' is a parameter indicating the configuration of the spatial relation between the reference RS and the target SRS. Here, the reference RS can be the SSB, CSI-RS, or SRS corresponding to the L1 parameter 'SRS-SpatialRelationInfo'. If the SRS is configured by SRS-PosResourceSet-r16, the reference RS can also be the DL PRS (Positioning reference signal). The above usage is configured per SRS resource set.

[0609] The terminal determines the Tx beam for the SRS resource to be transmitted based on the spatialRelationInfo included in the SRS-Config IE (S1020). Here, spatialRelationInfo is configured for each SRS resource and indicates whether to apply the same beam used in the SSB, CSI-RS, or SRS for each SRS resource. Additionally, spatialRelationInfo may or may not be configured for each SRS resource.

[0610] If spatialRelationInfo is set in the SRS resource, transmission is performed by applying the same beam as the beam used in the SSB, CSI-RS, or SRS. However, if spatialRelationInfo is not set in the SRS resource, the terminal arbitrarily determines a Tx beam and transmits the SRS through the determined Tx beam (S1030).

[0611] More specifically, for P-SRS where resourceType in SRS-Resource or SRS-PosResource-r16 is set to 'periodic':

[0612] i) If spatialRelationInfo or spatialRelationInfoPos-r16 is set to 'SSB / PBCH', the UE transmits the SRS resource by applying a spatial domain transmission filter identical to (or generated from) the spatial domain Rx filter used for receiving SSB / PBCH; or

[0613] ii) If spatialRelationInfo or spatialRelationInfoPos-r16 is set to 'CSI-RS', the UE transmits the SRS resource by applying the same spatial domain transmission filter used for receiving periodic CSI-RS or SP CSI-RS; or

[0614] iii) If spatialRelationInfo or spatialRelationInfoPos-r16 is set to 'SRS', the UE transmits the SRS resource by applying the same spatial domain transmission filter used for the transmission of periodic SRS.

[0615] iv) When spatialRelationInfoPos-r16 is set to 'PRS', the UE transmits the SRS resource by applying the same spatial domain transmission filter used for receiving the DL PRS.

[0616] Even if the resourceType within SRS-Resource or SRS-PosResource-r16 is set to 'SP-SRS' or 'AP-SRS', beam determination and transmission operations can be applied similarly to the above.

[0617] Additionally, the terminal may receive or not receive feedback on the SRS from the base station in the following three cases (S1040).

[0618] i) If spatialRelationInfo is set for all SRS resources within the SRS resource set, the terminal transmits the SRS to the beam designated by the base station. For example, if spatialRelationInfo all designates the same SSB, CRI, or SRI, the terminal repeatedly transmits the SRS to the same beam. This case corresponds to Fig. G(a) for the purpose of the base station selecting the Rx beam.

[0619] ii) SpatialRelationInfo may not be set for all SRS resources within the SRS resource set. In this case, the terminal can freely switch SRS beams and transmit. That is, this case is used for the terminal to sweep the Tx beam, corresponding to Fig. G(b).

[0620] iii) SpatialRelationInfo may be set for only some SRS resources within an SRS resource set. In this case, SRS is transmitted via the designated beam for the configured SRS resources, and for SRS resources for which spatialRelationInfo is not set, the terminal may arbitrarily apply a Tx beam to transmit.

[0621]

[0622] Proposal procedure for various embodiments of the present disclosure

[0623] FIG. 35 is a drawing illustrating an example of a multi-window structure for beam tracking in a system applicable to the present disclosure.

[0624] The present invention proposes a multi-window beam prediction structure to effectively support AI / ML-based spatial-temporal beam prediction. For beam prediction characterized by this structure, the invention proposes the operation and related signaling for AI / ML model configuration, model reporting, and model training.

[0625] Beam prediction can occur along the temporal or spatial axis. This is a result of the terminal's directionality, self-movement, and movement path being influenced by the terrain between the base station and the terminal, as well as the mobility of surrounding moving objects and the terminal itself. Millimeter-band radio waves are significantly affected by terrain due to their directional propagation. Although the terminal moves intentionally by the user, the probability distribution of movement may be influenced by the surrounding terrain rather than being a perfectly random distribution. For example, the beam prediction patterns of cars passing between narrow urban buildings will always be similar due to the restrictive nature of the roads. Beam prediction will show slightly different trends along the temporal axis when moving slowly during congestion versus moving quickly during smooth traffic flow. Ultimately, beam prediction models the distribution of spatiotemporal continuity.

[0626] In this type of beam prediction, signaling resource consumption decreases as the prediction window lengthens and prediction accuracy increases. As explained in the problem definition, the base station and the terminal exchange pre-agreed context values ​​and beam information during the initial window setup, and then exchange satisfaction status regarding events occurring within the window. This is because the longer the window and the better the prediction, the more verification can be performed only on pre-agreed points. However, even if the window is long, signaling resource consumption occurs because a new window must be opened if a beam failure occurs within it.

[0627] The present invention aims to solve this problem by presenting multiple configurable beam prediction sequence candidates based on various possible movement distributions of a terminal. For example, when a terminal is moving along a single path, its direction of travel may change arbitrarily at a specific point depending on the mobility distribution. The probability distribution regarding the continuity of space-time may include multiple paths. To solve this problem, a multi-beam prediction window is proposed. Furthermore, the data structure within this observation and prediction window is based on multimodal information. Multimodal information is defined as context in this patent.

[0628] We propose one observation window and multiple beam prediction windows. The AI / ML model can perform in-context learning, meta-learning, and fine-tuning. The AI / ML model outputs prediction data based on multiple prediction windows based on the observation window. Q number of multiple beam prediction windows were generated.

[0629] x k is the k-th set of context-beam information. Context-beam information is a bundle of contexts and beams.

[0630] Context information is multimodal information and may include some or all of the following information.

[0631] (1) Images from camera

[0632] (2) Target object data from radar

[0633] (3) Object sensing data from lidar

[0634] (4) Posture data from gyro sensor

[0635] (5) Position data from GPS

[0636] (6) Mobile type (smartphone, car, drone, satellite)

[0637] (7) RSSI, RSRP, CIR measurement data from other RATs

[0638] (8) RSSI, RSRP, and CIR measurement data from other frequencies

[0639] (9) Interference power from multiple TRPs

[0640] (10) Doppler spread measurement

[0641] (11) CSI feedback information

[0642]

[0643] Beam information may include some or all of the following information.

[0644] (1) DL Transceiver / Receiver Beam ID (DL Tx / Rx beam ID)

[0645] (2) L1-RSRP

[0646] (3) CIR-based quality

[0647]

[0648] The context-beam info within the window is a set x of the combinations (c,b) of context (C) and beam (B) information. kIt consists of. For example, x k It can be composed of (time, GPS data, Tx / Rx beam ID, CSI-RS quality) by a combination of location information and beam. Both the input and output can have this format. Here, time and GPS data are the context, and Tx / Tx beam ID and CSI-RS quality can be beam information.

[0649] AI / ML models can be pre-trained meta-models. Instead of inputting all data within the observation window, they can receive appropriate prompting based on this data. This allows for few-shot or zero-shot learning. If the above learning is not performed, the query model can transparently pass the observation window data through as is and input it into the AI / ML model. A representation format can be agreed upon in advance to provide input suitable for the AI / ML model using the data within the observation window.

[0650] The sizes of multiple prediction windows can vary. The number of windows, q, is an optimization setting value. It can have a larger value as the uncertainty of the terminal's movement distribution increases. The value of q can provide benefits from increasing the number of prediction windows. Conversely, if multiple windows are unnecessary, the value can revert to 1 to reduce complexity.

[0651] Multiple prediction windows can be divided as follows.

[0652] (1) Serving window: Size C s Context windows with window size, K per context s There are 12 beam predictions, and they are beams associated with the TCI of the data and control physical channels. There is always only 1 window.

[0653] (2) Candidate windows: Size C c K per context with candidate beam prediction windows having a window size c It consists of q-1 beams. It consists of multiple windows. It may be a beam associated with the TCI of the data and control physical channels.

[0654] Each window can be indicated by multiple TCIs (transmission configuration indices) associated with QCL information.

[0655] The context within the prediction window is As such, differential encoding is applied to increase efficiency during signaling between the base station and the terminal. That is, The reference value is placed first, and subsequent values ​​can be transmitted by encoding only the difference from this value or by encoding the difference from adjacent values.

[0656] One context inside the window is In this case, beamforming assumes group-based beam reporting capable of measuring multiple CSI-RS simultaneously and can be a multiple beam forming group.

[0657] FIG. 36 is a drawing illustrating an example of a multi-window structure for beam tracking in a system applicable to the present disclosure.

[0658] We propose an action on the time or context axis for multiple windows. The objective function for the window action is as follows: maximize the average beam gain for context x containing time information. is a transmission and reception codebook This is the beamforming vector for . The operation of the multiple window is to perform optimization for the following [Equation 1] on the probability distribution p(x) for the entire context x of the serving window.

[0659]

[0660] Multiple prediction windows can maximize the total cumulative quality for the prediction beam from the moment the window opens until it closes. The total cumulative utility function is the objective function.

[0661] Observation window Based on the information, the window context for the serving window and candidate window is queried to the AI / ML model. It receives the identifier for each window, and simultaneously, the prediction context within the window is represented by differential encoding as described above.

[0662] Each window can have a current activation pointer p within the current window. Prediction context for the current position Receive group-based or non-group-based beams for. In these 3GPP TS 38.214 group-based or non-group-based reports, a window identifier may additionally be transmitted.

[0663] Moving to the next location within the active window is Event directive function for It can be used. This function can be transmitted by the base station to the terminal via a configuration message. The event indication function is a prediction context It is an indicator value regarding the extent to which radio link quality is satisfied. The definition function of radio quality can evaluate the quality level based on measured L1-RSRP, L1-RSRQ, and CIR. Using this, if a radio quality event is satisfied, the terminal can transmit it to the base station via a reporting message. This report is transmitted along with a window identifier. Through this, the terminal can move from its current location p to p+1.

[0664] For each multiple window, the terminal can calculate the total accumulated value for L1-RSRP and CIR-based quality up to the current context within the window and report it to the base station for each window. Using these accumulated values, the base station can perform window changes and resets.

[0665] For each of the multiple windows, the terminal can measure prediction accuracy by measuring L1-RSRP and CIR-based quality within the window and report it to the base station for each window.

[0666] For each multiple window, the terminal can measure context information within the window and report it to the base station.

[0667]

[0668] FIG. 37 is a drawing illustrating an example of a multi-window structure for beam tracking in a system applicable to the present disclosure.

[0669] Continuous operation on the time axis for multiple windows can be performed as shown in FIG. 37. C A At this point, a multiple prediction window opens. C B The window does not expand until the point in time, and moves the active position to the edge of the predicted window. C B The prediction window can be expanded at a given point. In other words, it represents the behavior of reopening the prediction window after the multiple prediction windows have been completely exhausted.

[0670] FIG. 38 is a diagram illustrating an example of a signaling process between a terminal and a base station in a system applicable to the present disclosure.

[0671] The present invention relates to an enhanced technique for configuration and reporting methods to effectively support AI / ML-based beam prediction. Here, a one-sided AI / ML model refers to performing training and inference on a single node, although training and inference do not necessarily have to be performed on the same node. The node performing training and inference may include, for example, a gNB, an AI / ML server (e.g., non-3GPP), or a UE. The proposals of the present invention are generally applicable to one-sided AI / ML models, with a particular focus on NW-sided AI / ML models.

[0672] FIG. 38 illustrates an example of signaling between a UE (user equipment) and a NW (network) based on the proposed method according to an embodiment of the present disclosure. Here, the UE / NW is an example and can be replaced with various devices as described in the core implementation of a 6G communication system. This is done merely for convenience of explanation and does not limit the scope of the invention. Additionally, some step(s) may be omitted depending on the situation and / or settings. The BS / TRP may correspond to any entity belonging to the network, such as a base station (BS), node B, or TRP.

[0673] For the proposed method, the UE performs a reporting procedure related to terminal capability values ​​in UE capability requests and responses. In this procedure, range information of terminal capability values ​​for beam prediction multi-window functions may be included and reported to the network.

[0674]

[0675] [Configuration]

[0676] Based on the proposed method, the base station can request a configuration for the settings of functions / parameters related to the terminal capability values ​​reported in the UE capability procedure.

[0677] The Configuration message may include the serving window, candidate window, event directive function, and some or all of the context configuration information proposed in the previous chapter for each window.

[0678]

[0679] [Report]

[0680] Based on UE capability, the UE reports the terminal capability value for the reporting time or a specific past / future time point via a report message based on the proposed method. This report message can be transmitted via the fields of L1's PUSCH / PUCCH / PRACH, or via L2's MAC-CE or L3's RRC message. This report message is the current active position of all windows. It may include the following information regarding.

[0681] (1) Windows identifier

[0682] (2) Activation location within the window

[0683] (3) Quality measurement based on L1-RSRP, L1-RSRQ, and CIR for active locations within the window, prediction accuracy

[0684] (4) Measurement of beam quality for active location within the window and whether the beam event instruction function is satisfied

[0685] (5) Beam quality measurement and beam event indicator function satisfaction for the following active location within the window

[0686] (6) Expected beam information for the next active location in the window

[0687] (7) Part or all of the context information measured within the window

[0688] When reporting expected beam information for the next active location within the window to the base station, it may include both cases where the base station is transmitting the expected beam at the time of reporting and cases where it is not.

[0689]

[0690] [Reconfiguration / indication]

[0691] Based on the terminal's report information, the base station can perform reconfiguration / indication for multiple windows regarding functions / parameters related to the terminal capability value.

[0692] These reconfiguration / indication messages can be transmitted via the fields of L1's PDSCH / PDCCH, or via L2's MAC-CE or L3's RRC messages. Through the reconfiguration message, the current serving window can become a candidate window, and a window with good beam quality can become the serving window. When performing this window switching, the serving window identifier and the candidate window identifier can be transmitted.

[0693]

[0694] FIG. 39 is a drawing illustrating an example of a site-specific beam forming process in a system applicable to the present disclosure.

[0695] FIG. 39 shows an example of solving the problem of beam context caused by terminal mobility and complex channel environment distribution using the method presented in this patent.

[0696] Figure 39 illustrates a case where multiple base stations or TRPs (transmission and reception points) exist in a specific area where mobility is complex and beam communication is important. As the terminal enters point S, it obtains four windows for paths A, B, C, and D from a base station AI / ML model based on measurements within the observation window, designates the window with the best beam reception quality among them as the serving window, and starts a beam prediction operation. Upon reaching point W, it evaluates explicit paths for A, B, C, and D based on the beam quality evaluated within the proposed multiple windows, thereby covering the path by changing only the serving window without resetting the windows. By operating multiple prediction windows in this manner, the probability of beam failure occurring in a single window can be reduced due to high predictability.

[0697] When operating a single window using a discriminative AI / ML model, the uncertainty in the distribution of terminal paths A, B, C, and D from point W may not be properly reflected. This is because the model tends to suggest the path with the maximum likelihood. For example, let's assume that measuring the probability distribution of terminal movement reveals that the probability of branching in direction B is the highest compared to A, C, and D. In this case, even if the actual movement from point W is in direction A, the discriminative model will provide a beam context for direction B based on the maximum likelihood.

[0698]

[0699] FIG. 40 is a diagram illustrating an example of a multi-window generation process using a generative AI / ML model in a system applicable to the present disclosure.

[0700] As one embodiment of the method proposed in the present disclosure, the AI / ML model can be implemented as a generative model. The generative model models the probability distribution p(x) for the terminal context beam sequence. In the following figure, G(z) models the output x as p(x). z is a latent variable and is the variable to be optimized. z is a latent variable of the optimal path. * You can operate multiple windows representing four paths sampled from the vicinity.

[0701] Here, function F is the loss function during generative model training. It illustrates the process of correcting errors between the observations and the context beam sequences generated by the generative model. Since the generative model learns probability distributions well, the prediction accuracy of multiple windows can be improved.

[0702]

[0703] FIG. 41 is a diagram illustrating an example of a multi-window generation process using a multi-task AI / ML model in a system applicable to the present disclosure.

[0704] As one embodiment of the method proposed in the present disclosure, the AI / ML model can be implemented as a multi-task model. A small AI / ML model is trained for each path of the terminal, and the entire system is configured with a controller model that controls them collectively.

[0705] Figure 41 shows an example of configuring up to four multiple prediction windows to operate four predictions as four independent tasks. It involves receiving values ​​from the observation windows and selecting the task that provides the optimal value from four AI / ML models. In the first embodiment, specific site beamforming, four possible task branches can be easily captured. When the four branches merge into one, it can be switched to a candidate window.

[0706]

[0707] FIG. 42 is a diagram illustrating an example of a multi-window generation process using a multi-task AI / ML model in a system applicable to the present disclosure.

[0708] Multi-window measurement event handling

[0709] As one embodiment of the proposed method, the current context beam within the serving window From the next prediction context beam This is the case where it moves to. Current context beam This can be, for example, group beam information from CRI#4 to CRI#7 combined with sensor information. It can be a context beam proposed from a previous location suggested by an AI / ML model, or a beam group started when the initial serving window opened. An event indicator function that performs measurements on the beam group or evaluates beam group quality. It can instruct the base station on whether satisfaction is present. Based on this report, the base station is the next beam context It can be configured to operate.

[0710] FIG. 43 is a drawing illustrating an example of a switching structure between multiple beam prediction windows in a system applicable to the present disclosure.

[0711] Switching between multiple beam prediction windows

[0712] As one embodiment of multi-window operation, signaling resources can be minimized by supporting simple serving window and candidate window switching without resetting the window for the spatiotemporal beam channel distribution of the terminal.

[0713] today Evaluating cumulative wireless quality within the window until, if the event is not satisfied, within the candidate window If this event is satisfied, window switching can be performed.

[0714] Alternatively, the base station can set or switch windows through the terminal's context information report.

[0715] (1) Images from camera

[0716] (2) Target object data from radar

[0717] (3) Object sensing data from lidar

[0718] (4) Posture data from gyro sensor

[0719] (5) Position data from GPS

[0720] (6) Mobile type (smartphone, car, drone, satellite)

[0721] (7) RSSI, RSRP, CIR measurement data from other RATs

[0722] (8) RSSI, RSRP, and CIR measurement data from other frequencies

[0723] (9) Interference power from multiple TRPs

[0724] (10) Doppler spread measurement

[0725] (11) CSI feedback information

[0726]

[0727] FIG. 44 is a diagram illustrating an example of a process for improving the probability of beam failure by serving window switching in a system applicable to the present disclosure.

[0728] In Fig. 44, the effect of improving the beam failure probability through serving window switching can be seen by proposing a probabilistic path that branches into two when the mobility of the terminal changes from CRI#1b to CRI#7b in the path moving from CRI#1a to 10a.

[0729]

[0730] Effects of various embodiments of the present disclosure

[0731] The expected effects of the various embodiments of the present disclosure are as follows.

[0732] A multi-window beam prediction structure can significantly reduce the probability of beam failure by spatiotemporally corresponding to a high-dimensional probability distribution of the terminal's movement distribution. This probability distribution depends on the terminal's own mobility and motion vector, surrounding terrain, the terminal's form (e.g., drone, vehicle, satellite, smartphone), and the movement of surrounding objects. The beam experienced by the terminal is assumed to have a high-dimensional probability distribution in spacetime. The number of windows can be one or multiple by adaptively managing this probability distribution.

[0733] Multiple candidate models are presented for various possible movement distributions of the terminal. When the terminal is moving, its direction of travel may change arbitrarily at branching points. The probability distribution regarding spatiotemporal continuity may include multiple paths. Optimizing and predicting these paths can improve beam prediction performance. A generative model method capable of multi-prediction window-based operation is proposed to effectively utilize the representation of the probability distribution of the context beam sequence, which is a unique advantage of generative models.

[0734] It is also possible to prevent an increase in the number of unnecessary windows by proposing a signaling method that allows setting the number of variable multiple prediction windows based on channel conditions.

[0735] This multi-prediction window-based long beam prediction consumes less signaling resources as the prediction window lengthens and the prediction accuracy increases. Signaling resources can be saved by exchanging pre-agreed context values ​​and beam information during the initial window setup, and by exchanging only indicators with low signaling costs for reporting events occurring within the window. The signaling costs incurred when beam prediction failures frequently occur in a single window can be reduced by increasing prediction probabilities across multiple windows.

[0736]

[0737] The characteristic configurations of various embodiments of the present disclosure are as follows.

[0738] (1) As an AI / ML beam prediction model with a characteristic structure of multiple prediction windows, the windows are characterized by being composed of a sequence of sets of combinations of context and beams. UE capability to support this feature, (re-)configuration, beam tracing window-specific messages and signaling procedures in the reporting process

[0739] (2) A method in which a base station manages multiple prediction windows by establishing a structure of proposed serving windows and candidate windows, measures terminals based on this, measures beam reception quality and context by window, reports measurement events, and collects data. Signaling and messages for the procedure to set and release multiple windows.

[0740]

[0741] [Explanation regarding terminal claim]

[0742] The embodiments described above will be explained in detail below with reference to FIG. 45 regarding the operation of the terminal. The methods described below are distinguished only for the convenience of explanation, and it is obvious that, as long as they are not mutually excluded, a part of one method may be substituted with a part of another method or combined with one another and applied.

[0743] FIG. 45 is a diagram illustrating an example of the operation process of a terminal in a system applicable to the present disclosure.

[0744] According to various embodiments of the present disclosure, a method performed by a terminal in a communication system is provided.

[0745] The embodiment of FIG. 45 may further include, prior to step S4501, one or more of the steps of: the terminal receiving one or more synchronization signals from a base station; the terminal receiving system information from a base station; the terminal receiving configuration information from a base station; and the terminal receiving control information from a base station.

[0746] The embodiment of FIG. 45 may further include, prior to step S4501, one or more of the steps of: the terminal transmitting a random access preamble to the base station; the terminal receiving a random access response (RAR) from the base station; the terminal transmitting a random access message 3 to the base station; and the terminal receiving a contention resolution message from the base station. Message 3 is the first PUSCH transmission scheduled by the RAR with a RAR UL grant.

[0747] In step S4501, the terminal (user equipment, UE) transmits information about the terminal capability (UE capability) to the base station (base station, BS).

[0748] In step S4502, the terminal receives configuration information for a first serving window and one or more candidate windows from the base station based on the terminal capability. The first serving window and the one or more candidate windows include combinations of context information and beam information. The context information includes location information of the terminal.

[0749] In step S4503, the terminal transmits a report message of the measured beam quality for the first serving window and the one or more candidate windows to the base station based on the configuration information.

[0750] In step S4504, the terminal receives information from the base station instructing a switching from the first serving window to a second serving window, which is one of the candidate windows among the one or more candidate windows.

[0751] In step S4505, the terminal transmits or receives a signal to or from the base station based on beam information corresponding to the second serving window.

[0752]

[0753] According to various embodiments of the present disclosure, the one or more candidate windows may be associated with a predicted movement path based on the location and movement direction of the terminal.

[0754] According to various embodiments of the present disclosure, the second serving window may be associated with the explicit path of the terminal determined based on the measured beam quality for the first serving window and the one or more candidate windows of the terminal at the point where the mobility of the terminal changes.

[0755] According to various embodiments of the present disclosure, the switching from the first serving window to the second serving window can be performed without resetting the new serving window or the new candidate window.

[0756] According to various embodiments of the present disclosure, each of the first serving window and the one or more candidate windows may be associated with at least one artificial intelligence / machine learning model (AI / ML model) identifier. The at least one AI / ML model identifier may be determined by the terminal or indicated to the terminal by the base station.

[0757] According to various embodiments of the present disclosure, the first serving window and the one or more candidate windows may be related to beam information of a plurality of beams for determining an optimal beam.

[0758] According to various embodiments of the present disclosure, the first serving window and the one or more candidate windows may be associated with different context information and different beam information. The optimal beam may be the beam with the best quality among a plurality of beams corresponding to the optimal beam. Transmission or reception between the terminal and the base station may be based on the optimal beam.

[0759] According to various embodiments of the present disclosure, the context information further comprises a camera-based image from the terminal, radar-based target object data from the terminal, lidar-based object sensing data from the terminal, gyro sensor-based posture data from the terminal, GPS-based position data from the terminal, mobile type (smartphone, car, drone, satellite) of the terminal, measurement data for one or more of RSSI (received signal strength indicator), RSRP (reference signal received power), and CIR (channel impulse response) based on a plurality of RATs (radio access technologies) from the terminal, measurement data for one or more of RSSI (received signal strength indicator), RSRP (reference signal received power), and CIR (channel impulse response) based on a plurality of frequencies from the terminal, and one or more of interference power, Doppler spread measurement, and CSI (channel state information) feedback information based on a plurality of TRPs (transmission reception points) from the terminal. It is possible.

[0760] According to various embodiments of the present disclosure, the beam information may include one or more of an identifier (ID) of a transmission beam (Tx beam) or reception beam (Rx beam) of a downlink (DL), L1-RSRP (layer one-reference signal received power), and CIR (channel impulse response) based quality.

[0761] According to various embodiments of the present disclosure, the embodiment of FIG. 34 may further include the step of receiving second setting information for a plurality of new candidate windows and a third serving window when the measured beam quality for the first serving window and the one or more candidate windows is all smaller than the threshold beam quality.

[0762]

[0763] According to various embodiments of the present disclosure, a terminal is provided in a wireless communication system. The terminal includes a transceiver and at least one processor, and the at least one processor may be configured to perform a method of operation of the terminal according to FIG. 45.

[0764]

[0765] According to various embodiments of the present disclosure, a device for controlling a terminal in a wireless communication system is provided. The device comprises at least one processor and at least one memory operably connected to the at least one processor. The at least one memory may be configured to store instructions for performing a method of operation of the terminal according to FIG. 45 based on execution by the at least one processor.

[0766]

[0767] According to various embodiments of the present disclosure, one or more non-transitory computer-readable media (CRMs) storing one or more instructions are provided. The one or more instructions perform operations based on execution by one or more processors, and the operations may include a method of operation of a terminal according to FIG. 45.

[0768]

[0769] [Explanation regarding base station claims]

[0770] The embodiments described above will be explained in detail below with reference to FIG. 46 regarding the operation of a base station. The methods described below are distinguished only for the convenience of explanation, and it is understood that, as long as they are not mutually excluded, a part of one method may be substituted with a part of another method or combined with one another and applied.

[0771] FIG. 46 is a diagram illustrating an example of the operation process of a base station in a system applicable to the present disclosure.

[0772] According to various embodiments of the present disclosure, a method performed by a base station in a communication system is provided.

[0773] The embodiment of FIG. 46 may further include, prior to step S4601, one or more of the steps of: a base station transmitting one or more synchronization signals to a terminal; a base station transmitting system information to a terminal; a base station transmitting configuration information to a terminal; and a base station transmitting control information to a terminal.

[0774] The embodiment of FIG. 46 may further include, prior to step S4601, one or more of the steps of: the base station receiving a random access preamble from the terminal; the base station transmitting a random access response (RAR) to the terminal; the base station receiving a random access message 3 from the terminal; and the base station transmitting a contention resolution message to the terminal. Message 3 is the first PUSCH transmission scheduled by the RAR with a RAR UL grant.

[0775] In step S4601, the base station transmits information on the terminal capability (UE capability) to the terminal (user equipment, UE).

[0776] In step S4602, the base station transmits configuration information for a first serving window and one or more candidate windows to the terminal based on the terminal capability. The first serving window and the one or more candidate windows include combinations of context information and beam information. The context information includes location information of the terminal.

[0777] In step S4603, the base station receives a report message from the terminal regarding the beam quality measured for the first serving window and the one or more candidate windows based on the configuration information.

[0778] In step S4604, the base station transmits information to the terminal instructing it to switch from the first serving window to a second serving window, which is one of the candidate windows among the one or more candidate windows.

[0779] In step S4605, the base station transmits or receives a signal to or from the terminal based on beam information corresponding to the second serving window.

[0780]

[0781] According to various embodiments of the present disclosure, the one or more candidate windows may be associated with a predicted movement path based on the location and movement direction of the terminal.

[0782] According to various embodiments of the present disclosure, the second serving window may be associated with the explicit path of the terminal determined based on the measured beam quality for the first serving window and the one or more candidate windows of the terminal at the point where the mobility of the terminal changes.

[0783] According to various embodiments of the present disclosure, the switching from the first serving window to the second serving window can be performed without resetting the new serving window or the new candidate window.

[0784] According to various embodiments of the present disclosure, each of the first serving window and the one or more candidate windows may be associated with at least one artificial intelligence / machine learning model (AI / ML model) identifier. The at least one AI / ML model identifier may be determined by the terminal or indicated to the terminal by the base station.

[0785] According to various embodiments of the present disclosure, the first serving window and the one or more candidate windows may be associated with beam information of a plurality of beams for determining an optimal beam. The first serving window and the one or more candidate windows may be associated with different context information and different beam information. The optimal beam may be the beam with the best quality among a plurality of beams corresponding to the optimal beam. Transmission or reception between the terminal and the base station may be based on the optimal beam.

[0786] According to various embodiments of the present disclosure, the context information further comprises a camera-based image from the terminal, radar-based target object data from the terminal, lidar-based object sensing data from the terminal, gyro sensor-based posture data from the terminal, GPS-based position data from the terminal, mobile type (smartphone, car, drone, satellite) of the terminal, measurement data for one or more of RSSI (received signal strength indicator), RSRP (reference signal received power), and CIR (channel impulse response) based on a plurality of RATs (radio access technologies) from the terminal, measurement data for one or more of RSSI (received signal strength indicator), RSRP (reference signal received power), and CIR (channel impulse response) based on a plurality of frequencies from the terminal, and one or more of interference power, Doppler spread measurement, and CSI (channel state information) feedback information based on a plurality of TRPs (transmission reception points) from the terminal. It is possible.

[0787] According to various embodiments of the present disclosure, the beam information may include one or more of an identifier (ID) of a transmission beam (Tx beam) or reception beam (Rx beam) of a downlink (DL), L1-RSRP (layer one-reference signal received power), and CIR (channel impulse response) based quality.

[0788] According to various embodiments of the present disclosure, the embodiment of FIG. 46 may further include the step of receiving second setting information for a plurality of new candidate windows and a third serving window when the measured beam quality for the first serving window and the one or more candidate windows is all smaller than the threshold beam quality.

[0789]

[0790] According to various embodiments of the present disclosure, a base station is provided in a communication system. The base station includes a transceiver and at least one processor, and the at least one processor may be configured to perform the operation method of the base station according to FIG. 46.

[0791]

[0792] According to various embodiments of the present disclosure, an apparatus for controlling a base station in a communication system is provided. The apparatus comprises at least one processor and at least one memory operably connected to the at least one processor. The at least one memory may be configured to store instructions for performing a method of operating a base station according to FIG. 46 based on execution by the at least one processor.

[0793]

[0794] According to various embodiments of the present disclosure, one or more non-transitory computer-readable media (CRMs) storing one or more instructions are provided. The one or more instructions perform operations based on execution by one or more processors, and the operations may include a method of operation of a base station according to FIG. 46.

[0795]

[0796] Communication systems applicable to the present disclosure

[0797] FIG. 47 illustrates a communication system (1) applicable to various embodiments of the present disclosure.

[0798] Referring to FIG. 47, a communication system (1) applicable to various embodiments of the present disclosure includes a wireless device, a base station, and a network. Here, the wireless device refers to a device that performs communication using wireless access technology (e.g., 5G NR (New RAT), LTE (Long Term Evolution), 6G wireless communication) and may be referred to as a communication / wireless / 5G device / 6G 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, the vehicle may include an Unmanned Aerial Vehicle (UAV) (e.g., a drone). XR devices include AR (Augmented Reality) / VR (Virtual Reality) / MR (Mixed Reality) devices and can 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, base stations and networks may be implemented as wireless devices, and a specific wireless device (200a) may operate as a base station / network node to other wireless devices.

[0799] Wireless devices (100a to 100f) can be connected to a network (300) through a base station (200). Artificial Intelligence (AI) technology may be applied to the wireless devices (100a to 100f), and wireless devices (100a to 100f) can be connected to an AI server (400) through the network (300). The network (300) can be configured using a 3G network, a 4G (e.g., LTE) network, a 5G (e.g., NR) network, or a 6G network. Wireless devices (100a to 100f) may communicate with each other through the base station (200) / network (300), but they may also communicate directly (e.g., sidelink communication) without going through the base station / network. For example, vehicles (100b-1, 100b-2) can communicate directly (e.g., V2V (Vehicle to Vehicle) / V2X (Vehicle to everything) communication). Also, IoT devices (e.g., sensors) can communicate directly with other IoT devices (e.g., sensors) or other wireless devices (100a to 100f).

[0800] Wireless communication / connection (150a, 150b, 150c) can be established between wireless devices (100a~100f) / base station (200) and base station (200) / base station (200). Here, wireless communication / connection can be achieved through various wireless access technologies (e.g., 5G NR), such as uplink / downlink communication (150a), sidelink communication (150b) (or D2D communication), and inter-base station communication (150c) (e.g., relay, IAB (Integrated Access Backhaul)). Through wireless communication / connection (150a, 150b, 150c), wireless devices and base stations / wireless devices, and base stations and base stations can transmit / receive wireless signals to / from each other. For example, wireless communication / connection (150a, 150b, 150c) can transmit / receive signals through various physical channels. To this end, based on various proposals of various embodiments of the present disclosure, at least some of the following may be performed: various configuration information setting processes for transmitting / receiving wireless signals, various signal processing processes (e.g., channel encoding / decoding, modulation / demodulation, resource mapping / demapping, etc.), resource allocation processes, etc.

[0801] Meanwhile, NR supports multiple numerologies (or subcarrier spacing (SCS)) to support various 5G services. For example, when the SCS is 15 kHz, it supports a wide area in traditional cellular bands; when the SCS is 30 kHz / 60 kHz, it supports dense-urban, lower latency, and wider carrier bandwidth; and when the SCS is 60 kHz or higher, it supports a bandwidth greater than 24.25 GHz to overcome phase noise.

[0802] The NR frequency band can be defined by two types of frequency ranges (FR1, FR2). The numerical values ​​of the frequency ranges may change, for example, the frequency ranges of the two types (FR1, FR2) may be as shown in Table 3 below. For convenience of explanation, among the frequency ranges used in the NR system, FR1 may mean "sub 6GHz range" and FR2 may mean "above 6GHz range" and may be referred to as millimeter wave (mmW).

[0803]

[0804] Frequency Range designationCorresponding frequency rangeSubcarrier SpacingFR1450MHz-6000MHz15, 30, 60kHzFR224250MHz-52600MHz60, 120, 240kHz

[0805]

[0806] As described above, the numerical value of the frequency range of the NR system may change. For example, FR1 may include a band of 410 MHz to 7125 MHz as shown in Table 4 below. That is, FR1 may include a frequency band of 6 GHz (or 5850, 5900, 5925 MHz, etc.) or higher. For example, the frequency band of 6 GHz (or 5850, 5900, 5925 MHz, etc.) or higher included within FR1 may include an unlicensed band. The unlicensed band may be used for various purposes, for example, for communication for vehicles (e.g., autonomous driving).

[0807] Frequency Range designationCorresponding frequency rangeSubcarrier SpacingFR141MHz-7125MHz15, 30, 60kHzFR224250MHz-52600MHz60, 120, 240kHz

[0808] According to various embodiments of the present disclosure, the communication system (1) may support terahertz (THz) wireless communication. THz wireless communication is wireless communication using THz waves having a frequency of approximately 0.1 to 10 THz (1 THz = 10¹² Hz), and may refer to terahertz (THz) band wireless communication using a very high carrier frequency of 100 GHz or higher. The frequency band expected to be used for THz wireless communication may be a D-band (110 GHz to 170 GHz) or H-band (220 GHz to 325 GHz) band, which has low propagation loss due to molecular absorption in the air.

[0809]

[0810] Wireless devices applicable to the present disclosure

[0811] Hereinafter, examples of wireless devices to which various embodiments of the present disclosure are applied will be described.

[0812] FIG. 48 illustrates a wireless device that can be applied to various embodiments of the present disclosure.

[0813] Referring to FIG. 48, the first wireless device (100) and the second wireless device (200) can transmit and receive wireless signals through various wireless access technologies (e.g., LTE, NR). Here, {the first wireless device (100), the second wireless device (200)} may correspond to {wireless device (100x), base station (200)} and / or {wireless device (100x), wireless device (100x)} of FIG. 47.

[0814] 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 descriptions, functions, procedures, proposals, methods and / or operation sequences disclosed herein. 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). The memory (104) may be connected to the processor (102) and may store various information related to the operation of the processor (102). For example, the memory (104) may store software code containing instructions for performing some or all of the processes controlled by the processor (102) or for performing the descriptions, functions, procedures, proposals, methods, and / or operation sequence diagrams disclosed in this document. Here, the processor (102) and the memory (104) may be part of a communication modem / circuit / chip designed to implement wireless communication technology (e.g., LTE, NR). The transceiver (106) may be connected to the processor (102) and may transmit and / or receive wireless signals through one or more antennas (108). The transceiver (106) may include a transmitter and / or receiver. The transceiver (106) may be combined with an RF (Radio Frequency) unit. In various embodiments of the present disclosure, the wireless device may refer to a communication modem / circuit / chip.

[0815] 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 descriptions, functions, procedures, proposals, methods and / or operation sequences disclosed in this document. 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 descriptions, functions, procedures, proposals, methods, and / or sequences of operation disclosed in this document. 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 various embodiments of this disclosure, a wireless device may refer to a communication modem / circuit / chip.

[0816] Hereinafter, hardware elements of the wireless device (100, 200) will be described in more detail. Although not limited thereto, one or more protocol layers may be implemented by one or more processors (102, 202). For example, one or more processors (102, 202) may implement one or more layers (e.g., functional layers such as PHY, MAC, RLC, PDCP, RRC, SDAP). One or more processors (102, 202) may generate one or more Protocol Data Units (PDUs) and / or Service Data Units (SDUs) according to the descriptions, functions, procedures, proposals, methods, and / or flowcharts of operation disclosed in this document. One or more processors (102, 202) may generate messages, control information, data, or information according to the descriptions, functions, procedures, proposals, methods, and / or flowcharts of operation disclosed in this document. 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 document 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 descriptions, functions, procedures, proposals, methods, and / or flowcharts disclosed in this document.

[0817] 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 descriptions, functions, procedures, proposals, methods, and / or flowcharts disclosed in this document may be implemented using firmware or software, and the firmware or software may be implemented to include modules, procedures, functions, etc. Firmware or software configured to perform the descriptions, functions, procedures, proposals, methods, and / or operation sequences disclosed in this document may be contained 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 descriptions, functions, procedures, proposals, methods, and / or operation sequences disclosed in this document may be implemented using firmware or software in the form of code, instructions, and / or sets of instructions.

[0818] 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, code, 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.

[0819] 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 document 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 descriptions, functions, procedures, proposals, methods and / or operation flowcharts, etc., disclosed in this document 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 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 receive user data, control information, wireless signals / channels, etc., as described in the descriptions, functions, procedures, proposals, methods, and / or flowcharts of operation disclosed in this document through one or more antennas (108, 208). In this document, one or more antennas may be multiple physical antennas or multiple logical antennas (e.g., antenna ports). One or more transceivers (106, 206) can convert 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.

[0820] FIG. 49 illustrates another example of a wireless device that can be applied to various embodiments of the present disclosure.

[0821] According to FIG. 49, the wireless device may include at least one processor (102, 202), at least one memory (104, 204), at least one transceiver (106, 206), and one or more antennas (108, 208).

[0822] The difference between the example of the wireless device described in FIG. 48 and the example of the wireless device in FIG. 49 is that in FIG. 48, the processor (102, 202) and the memory (104, 204) are separated, whereas in the example of FIG. 49, the memory (104, 204) is included in the processor (102, 202).

[0823] Here, since the specific descriptions of the processor (102, 202), memory (104, 204), transceiver (106, 206), and one or more antennas (108, 208) are as described above, the descriptions of the repeated descriptions will be omitted to avoid unnecessary repetition of descriptions.

[0824] Hereinafter, examples of signal processing circuits to which various embodiments of the present disclosure are applied are described.

[0825] FIG. 50 illustrates a signal processing circuit for a transmission signal.

[0826] Referring to FIG. 50, the signal processing circuit (1000) may include a scrambler (1010), a modulator (1020), a layer mapper (1030), a precoder (1040), a resource mapper (1050), and a signal generator (1060). Although not limited thereto, the operation / function of FIG. 50 may be performed in the processor (102, 202) and / or transceiver (106, 206) of FIG. 48. The hardware elements of FIG. 50 may be implemented in the processor (102, 202) and / or transceiver (106, 206) of FIG. 48. For example, blocks 1010 through 1060 may be implemented in the processor (102, 202) of FIG. 48. Additionally, blocks 1010 to 1050 may be implemented in the processor (102, 202) of FIG. 48, and block 1060 may be implemented in the transceiver (106, 206) of FIG. 48.

[0827] The codeword can be converted into a wireless signal through the signal processing circuit (1000) of FIG. 50. Here, the codeword is an encoded bit sequence of an information block. The information block may include a transmission block (e.g., UL-SCH transmission block, DL-SCH transmission block). The wireless signal can be transmitted through various physical channels (e.g., PUSCH, PDSCH).

[0828] Specifically, a codeword can be converted into a scrambled bit sequence by a scrambler (1010). The scrambled sequence used for scrambling is generated based on an initialization value, which may include ID information of a wireless device, etc. The scrambled bit sequence can be modulated into a modulation symbol sequence by a modulator (1020). The modulation method may include pi / 2-BPSK (pi / 2-Binary Phase Shift Keying), m-PSK (m-Phase Shift Keying), m-QAM (m-Quadrature Amplitude Modulation), etc. The complex modulation symbol sequence can be mapped to one or more transmission layers by a layer mapper (1030). The modulation symbols of each transmission layer can be mapped to the corresponding antenna port(s) by a precoder (1040) (precoding). The output z of the precoder (1040) can be obtained by multiplying the output y of the layer mapper (1030) by an N*M precoding matrix W. Here, N is the number of antenna ports and M is the number of transmission layers. Here, the precoder (1040) can perform precoding after performing transform precoding (e.g., DFT transform) on the complex modulation symbols. Additionally, the precoder (1040) can perform precoding without performing transform precoding.

[0829] A resource mapper (1050) can map the modulation symbols of each antenna port to a time-frequency resource. The time-frequency resource may include multiple symbols (e.g., CP-OFDMA symbols, DFT-s-OFDMA symbols) in the time domain and multiple subcarriers in the frequency domain. A signal generator (1060) generates a radio signal from the mapped modulation symbols, and the generated radio signal can be transmitted to another device through each antenna. To this end, the signal generator (1060) may include an Inverse Fast Fourier Transform (IFFT) module, a Cyclic Prefix (CP) inserter, a Digital-to-Analog Converter (DAC), a frequency uplink converter, etc.

[0830] The signal processing process for a received signal in a wireless device can be configured as the inverse of the signal processing process (1010–1060) of FIG. 50. For example, a wireless device (e.g., 100, 200 in FIG. 48) can receive a wireless signal from the outside through an antenna port / transceiver. The received wireless signal can be converted into a baseband signal through a signal restorer. To this end, the signal restorer may include a frequency downlink converter, an analog-to-digital converter (ADC), a CP remover, and a Fast Fourier Transform (FFT) module. Subsequently, the baseband signal can be restored into a codeword through a resource de-mapper process, a postcoding process, a demodulation process, and a de-scrambling process. The codeword can be restored into the original information block through decoding. Accordingly, a signal processing circuit (not shown) for a received signal may include a signal restorer, a resource de-mapper, a postcoder, a demodulator, a de-scrambler, and a decoder.

[0831] Hereinafter, examples of wireless device applications to which various embodiments of the present disclosure are applied will be described.

[0832] FIG. 51 illustrates another example of a wireless device applicable to various embodiments of the present disclosure. The wireless device may be implemented in various forms depending on the use-example / service (see FIG. 47).

[0833] Referring to FIG. 51, the wireless device (100, 200) corresponds to the wireless device (100, 200) of FIG. 48 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. 48. For example, the transceiver(s) (114) may include one or more transceivers (106, 206) and / or one or more antennas (108, 208) of FIG. 48. 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).

[0834] The additional element (140) can 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. 47, 100a), a vehicle (Fig. 47, 100b-1, 100b-2), an XR device (Fig. 47, 100c), a portable device (Fig. 47, 100d), a home appliance (Fig. 47, 100e), an IoT device (Fig. 47, 100f), a digital broadcasting terminal, a hologram 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. 47, 400), a base station (Fig. 47, 200), a network node, etc. Wireless devices can be used in a movable or fixed location depending on the use—e.g., service.

[0835] In FIG. 51, 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 partially 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 connected via a wire, and the control unit (120) and the first unit (e.g., 130, 140) may be connected wirelessly 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 and / or a combination thereof.

[0836] Hereinafter, an implementation example of FIG. 51 will be described in more detail with reference to the drawings.

[0837] FIG. 52 illustrates a portable device applicable to various embodiments of the present disclosure. The portable device may include a smartphone, a smartpad, a wearable device (e.g., a smartwatch, smart glasses), a portable computer (e.g., a laptop, etc.). The portable device may be referred to as an MS (Mobile Station), UT (user terminal), MSS (Mobile Subscriber Station), SS (Subscriber Station), AMS (Advanced Mobile Station), or WT (Wireless terminal).

[0838] Referring to FIG. 52, the portable device (100) may include an antenna unit (108), a communication unit (110), a control unit (120), a memory unit (130), a power supply unit (140a), an interface unit (140b), and an input / output unit (140c). The antenna unit (108) may be configured as part of the communication unit (110). Blocks 110 to 130 / 140a to 140c each correspond to blocks 110 to 130 / 140 of FIG. 51.

[0839] The communication unit (110) can transmit and receive signals (e.g., data, control signals, etc.) with other wireless devices and base stations. The control unit (120) can control the components of the portable device (100) to perform various operations. The control unit (120) may include an AP (Application Processor). The memory unit (130) can store data / parameters / programs / code / commands required for the operation of the portable device (100). Additionally, the memory unit (130) can store input / output data / information, etc. The power supply unit (140a) supplies power to the portable device (100) and may include wired / wireless charging circuits, batteries, etc. The interface unit (140b) can support the connection between the portable device (100) and other external devices. The interface unit (140b) may include various ports (e.g., audio input / output ports, video input / output ports) for connection with external devices. The input / output unit (140c) can receive or output video information / signals, audio information / signals, data, and / or information input from a user. The input / output unit (140c) may include a camera, a microphone, a user input unit, a display unit (140d), a speaker and / or a haptic module, etc.

[0840] For example, in the case of data communication, the input / output unit (140c) acquires information / signals (e.g., touch, text, voice, image, video) input from the user, and the acquired information / signals can be stored in the memory unit (130). The communication unit (110) converts the information / signals stored in the memory into wireless signals and can directly transmit the converted wireless signals to another wireless device or to a base station. Additionally, the communication unit (110) can receive wireless signals from another wireless device or base station and then restore the received wireless signals to their original information / signals. The restored information / signals can be stored in the memory unit (130) and then output in various forms (e.g., text, voice, image, video, haptic) through the input / output unit (140c).

[0841] FIG. 53 illustrates a vehicle or autonomous vehicle applicable to various embodiments of the present disclosure.

[0842] Vehicles or autonomous vehicles can be implemented as mobile robots, vehicles, trains, manned or unmanned aerial vehicles (AVs), ships, etc.

[0843] Referring to FIG. 53, a vehicle or autonomous vehicle (100) may include an antenna unit (108), a communication unit (110), a control unit (120), a driving unit (140a), a power supply unit (140b), a sensor unit (140c), and an autonomous driving unit (140d). The antenna unit (108) may be configured as part of the communication unit (110). Blocks 110 / 130 / 140a to 140d correspond to blocks 110 / 130 / 140 of FIG. 51, respectively.

[0844] The communication unit (110) can transmit and receive signals (e.g., data, control signals, etc.) with external devices such as other vehicles, base stations (e.g., base stations, roadside base stations (Roadside units), etc.), and servers. The control unit (120) can perform various operations by controlling elements of the vehicle or autonomous vehicle (100). The control unit (120) may include an Electronic Control Unit (ECU). The driving unit (140a) can drive the vehicle or autonomous vehicle (100) on the ground. The driving unit (140a) may include an engine, motor, power train, wheels, brakes, steering device, etc. The power supply unit (140b) supplies power to the vehicle or autonomous vehicle (100) and may include wired / wireless charging circuits, batteries, etc. The sensor unit (140c) can obtain vehicle status, surrounding environment information, user information, etc. The sensor unit (140c) may include an IMU (inertial measurement unit) sensor, a collision sensor, a wheel sensor, a speed sensor, an inclination sensor, a weight detection sensor, a heading sensor, a position module, a vehicle forward / reverse sensor, a battery sensor, a fuel sensor, a tire sensor, a steering sensor, a temperature sensor, a humidity sensor, an ultrasonic sensor, an illuminance sensor, a pedal position sensor, etc. The autonomous driving unit (140d) may implement technologies such as maintaining the driving lane, technologies for automatically adjusting speed such as adaptive cruise control, technologies for automatically driving along a predetermined path, and technologies for automatically setting a path and driving when a destination is set.

[0845] For example, the communication unit (110) can receive map data, traffic information data, etc. from an external server. The autonomous driving unit (140d) can generate an autonomous driving path and a driving plan based on the acquired data. The control unit (120) can control the drive unit (140a) so that the vehicle or the autonomous vehicle (100) moves along the autonomous driving path according to the driving plan (e.g., speed / direction control). During autonomous driving, the communication unit (110) can acquire the latest traffic information data from an external server non-periodically and can acquire surrounding traffic information data from surrounding vehicles. Additionally, during autonomous driving, the sensor unit (140c) can acquire vehicle status and surrounding environment information. The autonomous driving unit (140d) can update the autonomous driving path and the driving plan based on the newly acquired data / information. The communication unit (110) can transmit information regarding the vehicle location, autonomous driving path, driving plan, etc. to an external server. An external server can predict traffic information data in advance using AI technology, etc., based on information collected from vehicles or autonomous vehicles, and can provide the predicted traffic information data to vehicles or autonomous vehicles.

[0846] FIG. 54 illustrates a vehicle applicable to various embodiments of the present disclosure. The vehicle may also be implemented as a means of transport, a train, an aircraft, a ship, etc.

[0847] Referring to FIG. 54, the vehicle (100) may include a communication unit (110), a control unit (120), a memory unit (130), an input / output unit (140a), and a position measurement unit (140b). Here, blocks 110 to 130 / 140a to 140b correspond to blocks 110 to 130 / 140 of FIG. 51, respectively.

[0848] The communication unit (110) can transmit and receive signals (e.g., data, control signals, etc.) with external devices such as other vehicles or base stations. The control unit (120) can control the components of the vehicle (100) to perform various operations. The memory unit (130) can store data / parameters / programs / codes / commands that support various functions of the vehicle (100). The input / output unit (140a) can output AR / VR objects based on information within the memory unit (130). The input / output unit (140a) may include a HUD. The position measurement unit (140b) can acquire position information of the vehicle (100). The position information may include absolute position information of the vehicle (100), position information within the driving line, acceleration information, position information relative to surrounding vehicles, etc. The position measurement unit (140b) may include GPS and various sensors.

[0849] For example, the communication unit (110) of the vehicle (100) can receive map information, traffic information, etc. from an external server and store it in the memory unit (130). The location measurement unit (140b) can acquire vehicle location information through GPS and various sensors and store it in the memory unit (130). The control unit (120) creates a virtual object based on map information, traffic information, and vehicle location information, etc., and the input / output unit (140a) can display the created virtual object on the glass window inside the vehicle (1410, 1420). In addition, the control unit (120) can determine whether the vehicle (100) is operating normally within the driving line based on the vehicle location information. If the vehicle (100) deviates abnormally from the driving line, the control unit (120) can display a warning on the glass window inside the vehicle through the input / output unit (140a). Additionally, the control unit (120) can broadcast a warning message regarding a driving abnormality to surrounding vehicles through the communication unit (110). Depending on the situation, the control unit (120) can transmit the vehicle's location information and information regarding the driving / vehicle abnormality to relevant authorities through the communication unit (110).

[0850] FIG. 55 illustrates an XR device applicable to various embodiments of the present disclosure. The XR device may be implemented as an HMD, a Head-Up Display (HUD) equipped in a vehicle, a television, a smartphone, a computer, a wearable device, a home appliance, digital signage, a vehicle, a robot, etc.

[0851] Referring to FIG. 55, the XR device (100a) may include a communication unit (110), a control unit (120), a memory unit (130), an input / output unit (140a), a sensor unit (140b), and a power supply unit (140c). Here, blocks 110 to 130 / 140a to 140c correspond to blocks 110 to 130 / 140 of FIG. 51, respectively.

[0852] The communication unit (110) can transmit and receive signals (e.g., media data, control signals, etc.) with external devices such as other wireless devices, mobile devices, or media servers. The media data may include video, images, sound, etc. The control unit (120) can perform various operations by controlling the components of the XR device (100a). For example, the control unit (120) may be configured to control and / or perform procedures such as video / image acquisition, (video / image) encoding, metadata generation, and processing. The memory unit (130) may store data / parameters / programs / codes / commands required for driving the XR device (100a) or creating an XR object. The input / output unit (140a) acquires control information, data, etc. from the outside and can output the created XR object. The input / output unit (140a) may include a camera, microphone, user input unit, display unit, speaker and / or haptic module, etc. The sensor unit (140b) can obtain XR device status, surrounding environment information, user information, etc. The sensor unit (140b) may include a proximity sensor, an illuminance sensor, an accelerometer, a magnetic sensor, a gyroscope, an inertial sensor, an RGB sensor, an IR sensor, a fingerprint recognition sensor, an ultrasonic sensor, a light sensor, a microphone and / or radar, etc. The power supply unit (140c) supplies power to the XR device (100a) and may include a wired / wireless charging circuit, a battery, etc.

[0853] For example, the memory unit (130) of the XR device (100a) may contain information (e.g., data, etc.) necessary for creating an XR object (e.g., AR / VR / MR object). The input / output unit (140a) may receive a command to operate the XR device (100a) from the user, and the control unit (120) may operate the XR device (100a) according to the user's operation command. For example, if the user intends to watch movies, news, etc. through the XR device (100a), the control unit (120) may transmit content request information to another device (e.g., mobile device (100b)) or a media server through the communication unit (130). The communication unit (130) may download / stream content such as movies, news, etc. from another device (e.g., mobile device (100b)) or a media server to the memory unit (130). The control unit (120) controls and / or performs procedures such as video / image acquisition, (video / image) encoding, and metadata generation / processing for the content, and can generate / output an XR object based on information about the surrounding space or real object acquired through the input / output unit (140a) / sensor unit (140b).

[0854] Additionally, the XR device (100a) is wirelessly connected to the mobile device (100b) through the communication unit (110), and the operation of the XR device (100a) can be controlled by the mobile device (100b). For example, the mobile device (100b) can act as a controller for the XR device (100a). To this end, the XR device (100a) can acquire three-dimensional position information of the mobile device (100b), and then generate and output an XR object corresponding to the mobile device (100b).

[0855] FIG. 56 illustrates a robot applicable to various embodiments of the present disclosure. Robots may be classified into industrial, medical, domestic, military, etc., depending on the purpose or field of use.

[0856] Referring to FIG. 56, the robot (100) may include a communication unit (110), a control unit (120), a memory unit (130), an input / output unit (140a), a sensor unit (140b), and a driving unit (140c). Here, blocks 110 to 130 / 140a to 140c correspond to blocks 110 to 130 / 140 of FIG. 51, respectively.

[0857] The communication unit (110) can transmit and receive signals (e.g., driving information, control signals, etc.) with external devices such as other wireless devices, other robots, or control servers. The control unit (120) can control the components of the robot (100) to perform various operations. The memory unit (130) can store data / parameters / programs / codes / commands that support various functions of the robot (100). The input / output unit (140a) can acquire information from outside the robot (100) and output information to outside the robot (100). The input / output unit (140a) may include a camera, microphone, user input unit, display unit, speaker and / or haptic module, etc. The sensor unit (140b) can obtain internal information of the robot (100), surrounding environment information, user information, etc. The sensor unit (140b) may include a proximity sensor, an illuminance sensor, an accelerometer, a magnetic sensor, a gyroscope, an inertial sensor, an IR sensor, a fingerprint recognition sensor, an ultrasonic sensor, a light sensor, a microphone, a radar, etc. The driving unit (140c) may perform various physical movements, such as moving robot joints. Additionally, the driving unit (140c) may enable the robot (100) to travel on the ground or fly in the air. The driving unit (140c) may include an actuator, a motor, a wheel, a brake, a propeller, etc.

[0858] FIG. 57 illustrates an AI device applicable to various embodiments of the present disclosure.

[0859] AI devices can be implemented as stationary devices or mobile devices, such as TVs, projectors, smartphones, PCs, laptops, digital broadcasting terminals, tablet PCs, wearable devices, set-top boxes (STBs), radios, washing machines, refrigerators, digital signage, robots, vehicles, etc.

[0860] Referring to FIG. 57, the AI ​​device (100) may include a communication unit (110), a control unit (120), a memory unit (130), an input / output unit (140a / 140b), a learning processor unit (140c), and a sensor unit (140d). Blocks 110 to 130 / 140a to 140d each correspond to blocks 110 to 130 / 140 of FIG. 51.

[0861] The communication unit (110) can transmit and receive wired and wireless signals (e.g., sensor information, user input, learning model, control signal, etc.) with external devices such as other AI devices (e.g., f. W1, 100x, 200, 400) or an AI server (200) using wired and wireless communication technology. To do this, the communication unit (110) can transmit information within the memory unit (130) to an external device or transmit signals received from an external device to the memory unit (130).

[0862] The control unit (120) can determine at least one executable operation of the AI ​​device (100) based on information determined or generated using a data analysis algorithm or a machine learning algorithm. The control unit (120) can perform the determined operation by controlling the components of the AI ​​device (100). For example, the control unit (120) can request, search, receive, or utilize data from the learning processor unit (140c) or the memory unit (130), and can control the components of the AI ​​device (100) to execute a predicted operation or an operation determined to be desirable among at least one executable operation. Additionally, the control unit (120) can collect historical information, including the operation content of the AI ​​device (100) or user feedback regarding the operation, and store it in the memory unit (130) or the learning processor unit (140c), or transmit it to an external device such as an AI server (Fig. W1, 400). The collected historical information can be used to update the learning model.

[0863] The memory unit (130) can store data that supports various functions of the AI ​​device (100). For example, the memory unit (130) can store data obtained from the input unit (140a), data obtained from the communication unit (110), output data from the learning processor unit (140c), and data obtained from the sensing unit (140). Additionally, the memory unit (130) can store control information and / or software code required for the operation / execution of the control unit (120).

[0864] The input unit (140a) can acquire various types of data from outside the AI ​​device (100). For example, the input unit (120) can acquire training data for model training and input data to which the training model is applied. The input unit (140a) may include a camera, a microphone and / or a user input unit, etc. The output unit (140b) can generate output related to visual, auditory, or tactile senses, etc. The output unit (140b) may include a display unit, a speaker and / or a haptic module, etc. The sensing unit (140) can obtain at least one of internal information of the AI ​​device (100), surrounding environment information of the AI ​​device (100), and user information using various sensors. The sensing unit (140) may include a proximity sensor, an illuminance sensor, an accelerometer, a magnetic sensor, a gyroscope, an inertial sensor, an RGB sensor, an IR sensor, a fingerprint recognition sensor, an ultrasonic sensor, a light sensor, a microphone and / or radar, etc.

[0865] The learning processor unit (140c) can train a model composed of an artificial neural network using learning data. The learning processor unit (140c) can perform AI processing together with the learning processor unit of the AI ​​server (Fig. W1, 400). The learning processor unit (140c) can process information received from an external device through the communication unit (110) and / or information stored in the memory unit (130). Additionally, the output value of the learning processor unit (140c) can be transmitted to / be transmitted to an external device through the communication unit (110) and / or stored in the memory unit (130).

[0866] The claims described in various embodiments of the present disclosure may be combined in various ways. For example, the technical features of the method claims of various embodiments of the present disclosure may be combined to be implemented as a device, and the technical features of the device claims of various embodiments of the present disclosure may be combined to be implemented as a method. Furthermore, the technical features of the method claims and the technical features of the device claims of various embodiments of the present disclosure may be combined to be implemented as a device, and the technical features of the method claims and the technical features of the device claims of various embodiments of the present disclosure may be combined to be implemented as a method.

Claims

1. A method performed by a terminal (user equipment, UE), A step of transmitting information on UE capability to a base station (BS); A step of receiving configuration information for a first serving window and one or more candidate windows from the base station based on the above terminal capability, The first serving window and the one or more candidate windows include combinations of context information and beam information, and The above context information includes location information of the terminal; A step of transmitting a report message of beam quality measured for the first serving window and the one or more candidate windows to the base station based on the above setting information; A step of receiving information from the base station instructing a switching from the first serving window to a second serving window, which is one of the one or more candidate windows; A step comprising transmitting or receiving a signal to or from the base station based on beam information corresponding to the second serving window, method.

2. In Paragraph 1, The above one or more candidate windows are related to a movement path predicted based on the location and direction of movement of the terminal, method.

3. In Paragraph 1, The second serving window is associated with the explicit path of the terminal determined based on the measured beam quality for the first serving window and the one or more candidate windows of the terminal at the point where the mobility of the terminal changes, and The switching from the first serving window to the second serving window is performed without resetting the new serving window or the new candidate window. method.

4. In Paragraph 1, Each of the above-mentioned first serving window and the above-mentioned one or more candidate windows is associated with at least one AI / ML model (artificial intelligence / machine learning model, AI / ML model) identifier, and The above at least one AI / ML model identifier is determined by the terminal or indicated to the terminal by the base station, method.

5. In Paragraph 1, The first serving window and the one or more candidate windows are related to beam information of a plurality of beams for determining an optimal beam, and The first serving window and the one or more candidate windows are associated with different context information and different beam information, and The above optimal beam is the beam with the best quality among a plurality of beams corresponding to the above optimal beam, and Transmission or reception between the above terminal and the above base station is based on the optimal beam, method.

6. In Paragraph 1, The above context information further includes a camera-based image from the terminal, radar-based target object data from the terminal, lidar-based object sensing data from the terminal, gyro sensor-based posture data from the terminal, GPS-based position data from the terminal, the mobile type of the terminal (smartphone, car, drone, satellite), measurement data for one or more of RSSI (received signal strength indicator), RSRP (reference signal received power), and CIR (channel impulse response) based on multiple RATs (radio access technologies) from the terminal, measurement data for one or more of RSSI (received signal strength indicator), RSRP (reference signal received power), and CIR (channel impulse response) based on multiple frequencies from the terminal, and one or more of interference power, Doppler spread measurement, and CSI (channel state information) feedback information based on multiple TRPs (transmission reception points) from the terminal. The above beam information includes one or more of the identifier (ID) of the transmission beam (Tx beam) or reception beam (Rx beam) of the downlink (DL), L1-RSRP (layer one-reference signal received power), and CIR (channel impulse response) based quality, method.

7. In Paragraph 1, If the measured beam quality for the first serving window and the one or more candidate windows is all smaller than the threshold beam quality, the method further includes the step of receiving second setting information for a plurality of new candidate windows and a third serving window. method.

8. In a method performed by a base station (BS), A step of transmitting information on terminal capabilities (UE capability) to a terminal (user equipment, UE); A step of transmitting configuration information for a first serving window and one or more candidate windows to the terminal based on the terminal capability, The first serving window and the one or more candidate windows include combinations of context information and beam information, and The above context information includes location information of the terminal; A step of receiving a report message of beam quality measured for the first serving window and the one or more candidate windows from the terminal based on the above setting information; A step of transmitting information to the terminal instructing a switching from the first serving window to a second serving window, which is one of the one or more candidate windows; A step comprising transmitting or receiving a signal to or from the terminal based on beam information corresponding to the second serving window. method.

9. In Paragraph 8, The above one or more candidate windows are related to a movement path predicted based on the location and direction of movement of the terminal, method.

10. In Paragraph 8, The second serving window is associated with the explicit path of the terminal determined based on the measured beam quality for the first serving window and the one or more candidate windows of the terminal at the point where the mobility of the terminal changes, and The switching from the first serving window to the second serving window is performed without resetting the new serving window or the new candidate window. method.

11. In Paragraph 8, Each of the above-mentioned first serving window and the above-mentioned one or more candidate windows is associated with at least one AI / ML model (artificial intelligence / machine learning model, AI / ML model) identifier, and The above at least one AI / ML model identifier is determined by the terminal or indicated to the terminal by the base station, method.

12. In Paragraph 8, The first serving window and the one or more candidate windows are related to beam information of a plurality of beams for determining an optimal beam, and The first serving window and the one or more candidate windows are associated with different context information and different beam information, and The above optimal beam is the beam with the best quality among a plurality of beams corresponding to the above optimal beam, and Transmission or reception between the above terminal and the above base station is based on the optimal beam, method.

13. In Paragraph 8, The above context information further includes a camera-based image from the terminal, radar-based target object data from the terminal, lidar-based object sensing data from the terminal, gyro sensor-based posture data from the terminal, GPS-based position data from the terminal, the mobile type of the terminal (smartphone, car, drone, satellite), measurement data for one or more of RSSI (received signal strength indicator), RSRP (reference signal received power), and CIR (channel impulse response) based on multiple RATs (radio access technologies) from the terminal, measurement data for one or more of RSSI (received signal strength indicator), RSRP (reference signal received power), and CIR (channel impulse response) based on multiple frequencies from the terminal, and one or more of interference power, Doppler spread measurement, and CSI (channel state information) feedback information based on multiple TRPs (transmission reception points) from the terminal. The above beam information includes one or more of the identifier (ID) of the transmission beam (Tx beam) or reception beam (Rx beam) of the downlink (DL), L1-RSRP (layer one-reference signal received power), and CIR (channel impulse response) based quality, method.

14. In Paragraph 8, If the measured beam quality for the first serving window and the one or more candidate windows is all smaller than the threshold beam quality, the method further includes the step of receiving second setting information for a plurality of new candidate windows and a third serving window. method.

15. In the terminal, Transmitter / Receiver; At least one processor; and It includes at least one memory that is operablely connectable to the at least one processor and stores instructions for performing operations when executed by the at least one processor. The above operations are, Comprising all steps of the method according to any one of claims 1 to 7, Terminal.

16. Regarding base stations, Transmitter / Receiver; At least one processor; and It includes at least one memory that is operablely connectable to the at least one processor and stores instructions for performing operations when executed by the at least one processor. The above operations are, Comprising all steps of the method according to any one of claims 8 through 14, Base station.

17. In a control device for controlling a terminal, At least one processor; and It includes at least one memory operably connected to the above at least one processor, and The above at least one memory stores instructions for performing operations based on execution by the above at least one processor, and The above operations are, Comprising all steps of the method according to any one of claims 1 to 7, controller.

18. In a control device for controlling a base station, At least one processor; and It includes at least one memory operably connected to the above at least one processor, and The above at least one memory stores instructions for performing operations based on execution by the above at least one processor, and The above operations are, Comprising all steps of the method according to any one of claims 8 through 14, controller.

19. In one or more non-transitory computer-readable media storing one or more instructions, The above one or more instructions perform operations based on being executed by one or more processors, and The above operations are, Comprising all steps of the method according to any one of claims 1 to 7, Computer-readable media.

20. In one or more non-transitory computer-readable media storing one or more instructions, The above one or more instructions perform operations based on being executed by one or more processors, and The above operations are, Comprising all steps of the method according to any one of claims 8 through 14, Computer-readable media.

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