Device and method for configuring hybrid-field beam codebook in wireless communication system

Abstract

The present disclosure relates to a device and method for configuring a hybrid field beam codebook in a wireless communication system. Specifically, the present disclosure relates to a device and method for performing a variable set B configuration for a hybrid-field beam codebook in a wireless communication system integrated with artificial intelligence (AI) (AI radio-AI for communications (AI4C)).

Description

Device and method for configuring a hybrid field beam codebook in a wireless communication system

[0001] The present disclosure relates to an apparatus and method for configuring a hybrid field beam codebook in a wireless communication system. Specifically, the present disclosure relates to an apparatus and method for performing a variable set B configuration for a hybrid field beam codebook in a wireless communication system incorporating artificial intelligence (AI) (AI radio - AI4C (AI for communications)).

[0002]

[0003] For E-MIMO (Extremely Large-Scale Massive MIMO System), the beam codebook size—corresponding to the number of codewords—is considerably large. Therefore, the exhaustive scanning method, which involves performing beam sweeping on all beam codewords within the codebook to find the optimal beam, incurs excessive overhead. Consequently, it is practically impossible to implement an exhaustive scanning method based on beam sweeping in an E-MIMO environment. Instead, measurements can be performed on beams within a set with smaller cardinality, the results input into an AI / ML model, and the optimal beam can be determined based on the output of the AI / ML model. This set with smaller cardinality can be referred to as Set B. Therefore, for beam management using an AI / ML model, it is essential to first consider how to configure Set B. The configuration method for Set B, which has lower cardinality than the entire set of beam codewords represented as Set A, can generally vary over time. Furthermore, configuring Set B differently over time offers advantages in terms of beam information irradiation efficiency compared to irradiating fixed beams by setting the same Set B every time. However, as the hybrid-field beam codebook is a recently proposed invention, there is no existing method for configuring a variable Set B for the hybrid codebook.

[0004]

[0005] To solve the aforementioned problems, the present disclosure provides an apparatus and method for configuring a hybrid field beam codebook in a wireless communication system.

[0006] The present disclosure provides an apparatus and method for performing a Variable Set B Configuration for a Hybrid-Field Beam Codebook in a wireless communication system incorporating artificial intelligence (AI) (AI radio - AI4C (AI for communications)).

[0007] 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.

[0008]

[0009] According to various embodiments of the present disclosure, a method performed by a first node comprises the steps of: receiving from a second node first setting information of a first set of codewords corresponding to some of all codewords, wherein the first setting information comprises some or all of a plurality of first vertex codewords, and the plurality of first vertex codewords are codewords among the entire set of codewords for which no adjacent codewords exist in an angle region; receiving from the second node a first reference signal based on the first setting information; transmitting to the second node information related to a measurement result based on the reference signal; receiving from the second node second setting information of a second set of codewords modified from the first set of codewords based on the measurement result; and receiving from the second node a second reference signal based on the second setting information.

[0010] According to various embodiments of the present disclosure, a method performed by a second node comprises the steps of: transmitting to a first node first setting information of a first set of codewords corresponding to some of all codewords; wherein the first setting information comprises some or all of a plurality of first vertex codewords, and the plurality of first vertex codewords are codewords in which there are no adjacent codewords in an angle region among the entire set of codewords; transmitting to the first node a first reference signal based on the first setting information; receiving from the first node information related to a measurement result based on the reference signal; transmitting to the first node second setting information of a second set of codewords modified from the first set of codewords based on the measurement result; and transmitting to the first node a second reference signal based on the second setting information.

[0011] According to various embodiments of the present disclosure, a first node in a wireless communication system comprises: a transceiver; at least one processor; and at least one memory operably connected to the at least one processor and storing instructions for performing operations when executed by the at least one processor, wherein the operations include all steps of a method of operating the first node according to various embodiments of the present disclosure.

[0012] According to various embodiments of the present disclosure, a second node in a wireless communication system comprises: a transceiver; at least one processor; and at least one memory operably connected to the at least one processor and storing instructions for performing operations when executed by the at least one processor, wherein the operations include all steps of a method of operating the second node according to various embodiments of the present disclosure.

[0013] According to various embodiments of the present disclosure, a control device for controlling a first node in a wireless communication system comprises: at least one processor; and at least one memory operably connected to the at least one processor, wherein the at least one memory stores instructions for performing operations based on execution by the at least one processor, and the operations include all steps of a method of operating the first node according to various embodiments of the present disclosure.

[0014] According to various embodiments of the present disclosure, a control device for controlling a second node in a wireless communication system comprises: at least one processor; and at least one memory operably connected to the at least one processor, wherein the at least one memory stores instructions for performing operations based on execution by the at least one processor, and the operations include all steps of a method of operating the second node according to various embodiments of the present disclosure.

[0015] 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 first node according to various embodiments of the present disclosure.

[0016] 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 second node according to various embodiments of the present disclosure.

[0017]

[0018] To solve the aforementioned problems, the present disclosure may provide an apparatus and method for configuring a hybrid field beam codebook in a wireless communication system.

[0019] The present disclosure may provide an apparatus and a method for performing a Variable Set B Configuration for a Hybrid-Field Beam Codebook in a wireless communication system incorporating artificial intelligence (AI) (AI radio - AI4C (AI for communications)).

[0020]

[0021] 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.

[0022] Figure 1 is a diagram illustrating physical channels used in 3GPP systems and an example of typical signal transmission.

[0023] Figure 2 is a diagram illustrating the system structure of a New Generation Radio Access Network (NG-RAN).

[0024] Figure 3 is a diagram illustrating the functional division between NG-RAN and 5GC.

[0025] Figure 4 is a diagram illustrating an example of a 5G usage scenario.

[0026] Figure 5 is a diagram illustrating an example of a communication structure that can be provided in a 6G system.

[0027] Figure 6 is a schematic diagram illustrating an example of a perceptron structure.

[0028] Figure 7 is a schematic diagram illustrating an example of a multilayer perceptron structure.

[0029] Figure 8 is a schematic diagram illustrating an example of a deep neural network.

[0030] Figure 9 is a schematic diagram illustrating an example of a convolutional neural network.

[0031] Figure 10 is a schematic diagram illustrating an example of a filter operation in a convolutional neural network.

[0032] Figure 11 is a schematic diagram illustrating an example of a neural network structure in which a recurrent loop exists.

[0033] Figure 12 is a schematic diagram illustrating an example of the operational structure of a recurrent neural network.

[0034] Figure 13 is a diagram illustrating an example of an electromagnetic spectrum.

[0035] Figure 14 is a diagram illustrating an example of a THz communication application.

[0036] FIG. 15 is a diagram illustrating an example of an electronic device-based THz wireless communication transceiver.

[0037] FIG. 16 is a diagram illustrating an example of a method for generating a THz signal based on an optical element.

[0038] FIG. 17 is a diagram illustrating an example of a THz wireless communication transceiver based on an optical element.

[0039] FIG. 18 is a diagram illustrating the structure of a photonic source-based transmitter.

[0040] Figure 19 is a diagram illustrating the structure of an optical modulator.

[0041] FIG. 20 is a diagram illustrating an example of the inference procedure for beam management in a system applicable to the present disclosure.

[0042] FIG. 21 is a drawing illustrating an example of a near-field region and a far-field region separated by the Rayleigh distance in a system applicable to the present disclosure.

[0043] FIG. 22 is a diagram illustrating an example of a comparison between the polar-domain representation and the angular-domain representation in a system applicable to the present disclosure.

[0044] FIG. 23 is a diagram illustrating an example of beam patterns for far-field (above figure: 32 antennas) and near-field (below figure: 256 antennas) in a system applicable to the present disclosure.

[0045] FIG. 24 is a drawing illustrating an example of various types of beam configuration options for beam measurement in a system applicable to the present disclosure.

[0046] FIG. 25 is a diagram illustrating an example of the beam gain of a conventional DTF codebook in a 16-antenna array in a system applicable to the present disclosure.

[0047] FIG. 26 is a diagram illustrating an example of the beam gain of a conventional DTF codebook in E-MIMO with 512 antennas in a system applicable to the present disclosure.

[0048] FIG. 27 is a diagram illustrating an example of the broadside (0°) beam gain of a near-field polar codebook in E-MIMO with 512 antennas in a system applicable to the present disclosure.

[0049] FIG. 28 is a diagram illustrating an example of the near end-fire (60°) beam gain of polar codebook in E-MIMO with 512 antennas in a system applicable to the present disclosure.

[0050] FIG. 29 is a drawing illustrating an example of an angle-dependent boundary of a near-field region in a system applicable to the present disclosure.

[0051] FIG. 30 is a drawing illustrating an example of a different number of codewords depending on angle in a hybrid-field beam codebook in a system applicable to the present disclosure.

[0052] FIG. 31 is a drawing illustrating an example of a broadside (0°) beam gain of a hybrid-field codebook in a system applicable to the present disclosure.

[0053] FIG. 32 is a drawing illustrating an example of the near end-fire (60°) beam gain of a hybrid codebook in a system applicable to the present disclosure.

[0054] FIG. 33 is a diagram illustrating an example of an extreme case of a distance-sparse scheme for Set B configuration in a system applicable to the present disclosure.

[0055] FIG. 34 is a diagram illustrating an example of vertex codewords for variable Set B configuration in a system applicable to the present disclosure.

[0056] FIG. 35 is a drawing illustrating an example of a region boundary for a variable Set B configuration in a system applicable to the present disclosure.

[0057] FIG. 36 is a drawing illustrating an example of intra-region indexing with a zigzag pattern for variable Set B configuration in a system applicable to the present disclosure.

[0058] FIG. 37 is a diagram illustrating an example of the operation process of a first node in a system applicable to the present disclosure.

[0059] FIG. 38 is a diagram illustrating an example of the operation process of a second node in a system applicable to the present disclosure.

[0060] FIG. 39 illustrates a communication system (1) applicable to various embodiments of the present disclosure.

[0061] FIG. 40 illustrates a wireless device that can be applied to various embodiments of the present disclosure.

[0062] FIG. 41 illustrates another example of a wireless device that can be applied to various embodiments of the present disclosure.

[0063] FIG. 42 illustrates a signal processing circuit for a transmission signal.

[0064] FIG. 43 shows another example of a wireless device applicable to various embodiments of the present disclosure.

[0065] FIG. 44 illustrates a portable device applicable to various embodiments of the present disclosure.

[0066] FIG. 45 illustrates a vehicle or autonomous vehicle applicable to various embodiments of the present disclosure.

[0067] FIG. 46 illustrates a vehicle applicable to various embodiments of the present disclosure.

[0068] FIG. 47 illustrates an XR device applied to various embodiments of the present disclosure.

[0069] FIG. 48 illustrates a robot applicable to various embodiments of the present disclosure.

[0070] FIG. 49 illustrates an AI device applicable to various embodiments of the present disclosure.

[0071]

[0072] 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."

[0073] 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."

[0074] 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."

[0075] 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.”

[0076] 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."

[0077] Technical features described individually within one drawing in various embodiments of the present disclosure may be implemented individually or simultaneously.

[0078]

[0079] 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.

[0080]

[0081] 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.

[0082]

[0083] 3GPP LTE

[0084] - 36.211: Physical channels and modulation

[0085] - 36.212: Multiplexing and channel coding

[0086] - 36.213: Physical layer procedures

[0087] - 36.300: Overall description

[0088] - 36.331: Radio Resource Control (RRC)

[0089] 3GPP NR

[0090] - 38.211: Physical channels and modulation

[0091] - 38.212: Multiplexing and channel coding

[0092] - 38.213: Physical layer procedures for control

[0093] - 38.214: Physical layer procedures for data

[0094] - 38.300: NR and NG-RAN Overall Description

[0095] - 38.331: Radio Resource Control (RRC) protocol specification

[0096]

[0097] Physical Channel and Frame Structure

[0098] Physical channels and general signal transmission

[0099] Figure 1 is a diagram illustrating physical channels used in 3GPP systems and an example of typical signal transmission.

[0100] 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.

[0101]

[0102] 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.

[0103]

[0104] 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).

[0105]

[0106] 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).

[0107]

[0108] 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.

[0109]

[0110] 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.

[0111]

[0112] Structure of uplink and downlink channels

[0113] Downlink Channel Structure

[0114] 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.

[0115]

[0116] (1) Physical Downlink Sharing Channel (PDSCH)

[0117] 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.

[0118]

[0119] (2) Physical Downlink Control Channel (PDCCH)

[0120] 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.

[0121] 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.

[0122]

[0123] Uplink Channel Structure

[0124] 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.

[0125] (1) Physical uplink shared channel (PUSCH)

[0126] 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, 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.

[0127] (2) Physical uplink control channel (PUCCH)

[0128] 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.

[0129]

[0130] The following describes new radio access technology (new RAT, NR).

[0131] 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.

[0132]

[0133] Figure 2 is a diagram illustrating the system structure of a New Generation Radio Access Network (NG-RAN).

[0134] 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.

[0135]

[0136] Figure 3 is a diagram illustrating the functional division between NG-RAN and 5GC.

[0137] 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.

[0138]

[0139] Figure 4 is a diagram illustrating an example of a 5G usage scenario.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] Next, we will examine in more detail the multiple usage examples included within the triangle of Fig. 4.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154]

[0155] 6G System General

[0156] 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 representing an example of the requirements for a 6G system.

[0157]

[0158] Per device peak data rate1TbpsE2E latency1msMaximum spectral efficiency100bps / HzMobility supportUp to 1000km / hrSatellite integrationFullyAIFullyAutonomous vehicleFullyXRFullyHaptic CommunicationFully

[0159] 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.

[0160]

[0161] Figure 5 is a diagram illustrating an example of a communication structure that can be provided in a 6G system.

[0162] 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.

[0163] - 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.

[0164] - 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).

[0165] - 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.

[0166] - 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.

[0167] Some general requirements regarding the new network characteristics of 6G mentioned above may be as follows.

[0168] - 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.

[0169] - 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.

[0170] - 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.

[0171] - 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.

[0172] - 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.

[0173]

[0174] Core implementation technology of 6G systems

[0175]

[0176] Artificial Intelligence

[0177] 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 to a very limited extent. 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.

[0178] 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.

[0179] Recently, attempts to integrate AI with wireless communication systems have emerged, but these have primarily focused on the application layer and network layer, particularly deep learning in the field of wireless resource management and allocation. However, such research is increasingly advancing toward the MAC layer and physical layer, with attempts to combine deep learning with wireless transmission, particularly at the physical layer. AI-based physical layer transmission refers to the application of 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.

[0180] 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.

[0181] However, the application of DNNs for transmission at the physical layer may have the following problems.

[0182] 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.

[0183] 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.

[0184] Below, we will take a closer look at machine learning.

[0185] Machine learning refers to a series of operations for training machines to create machines capable of performing tasks that humans can or find difficult to do. Machine learning requires data and learning models. Data learning methods in machine learning can be broadly classified into three types: supervised learning, unsupervised learning, and reinforcement learning.

[0186] 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.

[0187] 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 through backpropagation, the connection weights of each node in each layer of the neural network can be updated. 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 improved by using a low learning rate in the later stages of training.

[0188] The learning method may vary depending on the characteristics of the data. For example, if the goal is to accurately predict data transmitted from the transmitting end at the receiving end in a communication system, it is desirable to perform learning using supervised learning rather than unsupervised learning or reinforcement learning.

[0189] Learning models correspond 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.

[0190] 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).

[0191] An artificial neural network is an example of connecting multiple perceptrons.

[0192]

[0193] Figure 6 is a schematic diagram illustrating an example of a perceptron structure.

[0194] 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.

[0195] 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.

[0196]

[0197] Figure 7 is a schematic diagram illustrating an example of a multilayer perceptron structure.

[0198] 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.

[0199] 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 the 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).

[0200]

[0201] Figure 8 is a schematic diagram illustrating an example of a deep neural network.

[0202] 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.

[0203] Meanwhile, depending on how multiple perceptrons are connected to each other, various artificial neural network structures different from the aforementioned DNN can be formed.

[0204]

[0205] Figure 9 is a schematic diagram illustrating an example of a convolutional neural network.

[0206] 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.

[0207] 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.

[0208]

[0209] Figure 10 is a schematic diagram illustrating an example of a filter operation in a convolutional neural network.

[0210] 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.

[0211] 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).

[0212] 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.

[0213] 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.

[0214]

[0215] Figure 11 is a schematic diagram illustrating an example of a neural network structure in which a recurrent loop exists.

[0216] 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.

[0217]

[0218] Figure 12 is a schematic diagram illustrating an example of the operational structure of a recurrent neural network.

[0219] Referring to Fig. 12, the recurrent neural network operates on the input data sequence in a predetermined time sequence.

[0220] 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.

[0221] 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).

[0222] 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.

[0223] Recently, attempts to integrate AI with wireless communication systems have emerged, but these have primarily focused on the application layer and network layer, particularly deep learning in the field of wireless resource management and allocation. However, such research is increasingly advancing toward the MAC layer and physical layer, with attempts to combine deep learning with wireless transmission, particularly at the physical layer. AI-based physical layer transmission refers to the application of 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.

[0224] THz (Terahertz) communication

[0225] 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.

[0226]

[0227] Figure 13 is a diagram illustrating an example of an electromagnetic spectrum.

[0228] 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.

[0229] Optical wireless technology

[0230] 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.

[0231] FSO Backhaul Network

[0232] 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.

[0233] Massive MIMO technology

[0234] 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.

[0235] blockchain

[0236] 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.

[0237] 3D Networking

[0238] 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.

[0239] Quantum communication

[0240] 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.

[0241] unmanned aerial vehicles

[0242] 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.

[0243] Cell-free Communication

[0244] 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.

[0245] Wireless Information and Energy Transmission Integration

[0246] 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.

[0247] Integration of Sensing and Communication

[0248] 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.

[0249] Integration of access backhaul networks

[0250] 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.

[0251] Holographic beam forming

[0252] 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.

[0253] Big data analysis

[0254] 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.

[0255] Large Intelligent Surface (LIS)

[0256] 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.

[0257]

[0258] Terahertz (THz) wireless communication general

[0259]

[0260] 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.

[0261]

[0262] Figure 14 is a diagram illustrating an example of a THz communication application.

[0263] 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.

[0264] Table 2 below shows an example of a technology that can be used in THz waves.

[0265] 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

[0266]

[0267] 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.

[0268]

[0269] FIG. 15 is a diagram illustrating an example of an electronic device-based THz wireless communication transceiver.

[0270] 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.

[0271]

[0272] FIG. 16 is a diagram illustrating an example of a method for generating a THz signal based on an optical element.

[0273] FIG. 17 is a diagram illustrating an example of a THz wireless communication transceiver based on an optical element.

[0274] 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.

[0275]

[0276] The structure of a photoelectric converter (or photoelectric converter) is described with reference to FIGS. 18 and 19.

[0277] FIG. 18 is a diagram illustrating the structure of a photonic source-based transmitter.

[0278] Figure 19 is a diagram illustrating the structure of an optical modulator.

[0279] 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.

[0280] 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.

[0281] 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.

[0282] 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).

[0283]

[0284] Various embodiments of the present disclosure will be described in more detail below.

[0285]

[0286] The present disclosure relates to an apparatus and method for configuring a hybrid field beam codebook in a wireless communication system. Specifically, the present disclosure relates to an apparatus and method for performing a variable set B configuration for a hybrid field beam codebook in a wireless communication system incorporating artificial intelligence (AI) (AI radio - AI4C (AI for communications)).

[0287] The present disclosure proposes a method for configuring Set B differently over time when using a hybrid-field beam codebook as Set A and setting Set B as a subset of Set A.

[0288]

[0289] The symbols / abbreviations / terms used in this disclosure are as follows.

[0290] - CSI: Channel State Information

[0291] - BM: beam management

[0292] - E-MIMO: extremely-large scale massive MIMO (system)

[0293] - DFT: Discrete Fourier Transform

[0294] - AI: artificial intelligence

[0295] - ML: machine learning

[0296] - DL: Deep Learning

[0297] - NN: (artificial) Neural Network

[0298] - RNN: Recurrent Neural Network

[0299] - LSTM: Long short-term memory

[0300] - UE: User Equipment (Terminal)

[0301] - BS: Base Station

[0302] - NW: Network

[0303] - Tx: Transmit / Transmitter / Transmission, Transmitter

[0304] - Rx: Receive / Receiver / Reception, reception

[0305] - TDD: Time Division Duplex

[0306] - FDD: Frequency Division Duplex

[0307] - AWGN: Additive White Gaussian Noise

[0308] - MRT: Maximum-ratio transmission

[0309] - MSE: mean squared error

[0310] -VAE: variational autoencoder

[0311] - VQ-VAE: vector quantized variational autoencoder

[0312] - PMF: probability mass function

[0313] - CSI-RS: channel state information-reference signal

[0314] - HPE / HPD: hierarchical prior encoder / decoder

[0315]

[0316] Notations: Lower-case (or upper-case) italic letters represent scalars. Lower-case bold-face letters and upper-case bold-face letters represent vectors and matrices, respectively. Calligraphic letters represent sets. For example, and represents scalar, vector, matrix, and set. represents the set of complex numbers, and represents an m by n dimensional complex space. represents the expectation operator. I signifies an identity matrix of appropriate dimensions. Superscript represents the Hermitian transpose. and represents the trace and expectation operator, respectively. represents the Euclidean norm of the vector. silver Represents a zero-mean circularly symmetric complex Gaussian distribution with as its covariance matrix. An arbitrary set and Regarding, is a set and Represents the Cartesian product of, is a set It represents the n-ary Cartesian power. In this disclosure, signals expressed as gradient vectors may be row vectors rather than column vectors, and thus can be understood as signals in a (Hermitian) transpose relationship with gradient vectors of the conventional expression, but are commonly used as gradient vectors in this disclosure.

[0317]

[0318] Technical problems solved by the present disclosure

[0319] AI / ML for NR air interfaces was discussed in the Rel-18 study item (3GPP TR 38.843), which examined aspects such as performance, complexity, and potential specification impact for three use cases, including beam management (BM). In particular, regarding the BM use case, BM-Case1 and BM-Case2 were considered by dividing downlink Tx beam prediction into spatial and temporal domains, and the key contents are cited below.

[0320]

[0321] Chapter 5. Use cases in 3GPP TR 38.843

[0322] 5 Use cases

[0323] The initial set of use cases includes the following:

[0324] Beam management, eg, beam prediction in time, and / or spatial domain for overhead and latency reduction, beam selection accuracy improvement [RAN1].

[0325]

[0326] Of the three total use cases, this disclosure considers the BM case and, for convenience of explanation, assumes that the prediction period is much shorter than the channel coherence time. That is, it considers the problem of beam prediction within the channel coherence time in a block fading channel model and limits it to predicting the beam for the current channel. However, this is merely for convenience of explanation, and the scope of this disclosure is not limited to the above assumption and the above situation.

[0327]

[0328] In the foregoing citations and the present disclosure, the term "prediction" does not necessarily mean a prediction of the future. In particular, in machine learning or statistical analysis, the term "prediction" is also used when estimation is performed on the current state based on data through classification or regression. Specifically, regarding the sub-use cases of BM, 3GPP TR 38.843 is cited as follows.

[0329]

[0330] Chapter 5.2 Beam management in 3GPP TR 38.843

[0331] 5.2 Beam Management

[0332] Finalization of representative sub-use cases

[0333] The following are selected as representative sub-use cases:

[0334] (1) BM-Case1: Spatial-domain Downlink beam prediction

[0335] This is a spatial domain downlink beam prediction for the beams of Set A based on the beam measurement results of Set B.

[0336] (1-1) Considerations (Consider):

[0337] Alt. 1): AI / ML model training and inference at the network side.

[0338] Alt. 2): AI / ML model training and inference at the UE side.

[0339] (1-2) Considerations:

[0340] Alt. i): Set A and Set B are different (Set B is NOT a subset of Set A).

[0341] Alt. ii): Set B is a subset of Set A.

[0342] Note: Set A is for downlink beam prediction. The codebook construction of Set A and Set B can be clarified by companies.

[0343] (1-3) AI / ML model input considerations:

[0344] Alt. 1): Only L1-RSRP measurement based on Set B is included.

[0345] Alt. 2): Includes L1-RSRP measurement based on Set B and assistance information.

[0346] Alt. 3): It includes the CIR based on Set B (CIR based on Set B).

[0347] Alt. 4): Includes L1-RSRP measurement based on Set B and the corresponding downlink Tx or Rx beam ID.

[0348] (2) BM-Case2: Temporal Downlink Beam Prediction

[0349] This is a time-domain downlink beam prediction for the beams of Set A based on the historical measurement results of the beams of Set B.

[0350] (2-1) Considerations (Consider):

[0351] Alt. 1): AI / ML model training and inference at the NW side.

[0352] Alt. 2): AI / ML model training and inference at the UE side.

[0353] (2-2) Considerations (Consider):

[0354] Alt. i): Set A and Set B are different (Set B is NOT a subset of Set A).

[0355] Alt. ii): Set B is a subset of Set A.

[0356] Alt. iii): Set A and Set B are the same.

[0357] (2-3) AI / ML model input considerations:

[0358] The following alternatives are included based on the results of K (K≥1) latest measurement instances:

[0359] Alt. 1): Only L1-RSRP measurement based on Set B is included.

[0360] Alt. 2): Includes L1-RSRP measurement based on Set B and assistance information.

[0361] Alt. 3): Includes L1-RSRP measurement based on Set B and the corresponding downlink Tx or Rx beam ID.

[0362] (2-4) F predictions for F future time instances can be obtained based on the output of the AI / ML model.

[0363] Each prediction is for each time instance. At least F=1.

[0364] Set B is a set of beams whose measurements are taken as inputs of the AI / ML model.

[0365]

[0366] Among the above sub-use cases, BM-Case 1, although terminologically referred to as prediction, signifies the process of estimating / determining the beam for the current situation (i.e., channel), while BM-Case 2 signifies the process of predicting the future beam based on historical measurement results regarding the past and present. Since this disclosure considers the situation of estimating / determining the beam for the current channel, the situation considered in this disclosure does not correspond to BM-Case 2 but can be said to be closer to BM-Case 1. However, the assumed situation considered above is merely for convenience of explanation, and the scope of this disclosure is not limited to the above situation. That is, this disclosure can be applied to other use cases such as BM-Case 2.

[0367]

[0368] FIG. 20 is a diagram illustrating an example of the inference procedure for beam management in a system applicable to the present disclosure.

[0369] Figure 20 corresponds to Figure 6.3.1-1: An example of the inference procedure for beam management in 3GPP TR 38.843.

[0370] To understand the operation of existing AI / ML-based beam management (i.e., inference procedure), some of the content regarding evaluation in TR 38.843 is quoted below.

[0371] Chapter 6.3 Beam management in 3GPP TR 38.843

[0372] 6.3 Beam Management

[0373] 6.3.1 Evaluation Assumptions, Methodology, and KPIs

[0374] Figure 6.3.1-1 shows an example of the inference procedure for beam management for BM-Case1 and BM-Case2.

[0375] Measurements based on Set B of beams are used as model input.

[0376] Additionally, beam ID information may also be provided as input to the AI / ML model.

[0377] Based on model output (e.g., probability of each beam in Set A to be the Top-1 beam, predicted L1-RSRPs), Top-1 / N beam(s) among Set A of beams can be predicted and / or potentially with predicted L1-RSRPs (depending on the labeling).

[0378] In the evaluation, for BM-Case1, the measurements of Set B (otherwise stated) are used as model input to predict Top-1 / N beams from Set A, and for BM-Case2, the measurements from historic time instances are used as model input to perform temporal DL beam prediction of beams from Set A.

[0379] In the evaluation, the cases are considered:

[0380] Set A and Set B are different (Set B is NOT a subset of Set A),

[0381] If Set B is a subset of Set A,

[0382] In the case of BM-Case2, when Set A and Set B are the same.

[0383] And the performance of downlink Tx beam prediction and downlink Tx-Rx beam pair prediction is evaluated.

[0384] For both BM-Case1 and BM-Case2, the UE can report the prediction result to the NW based on the output of a UE-side model, or the NW can predict the Top-1 / N beam(s) based on the reported measurements of Set B for a NW-side model.

[0385] Figure 6.3.1-1: An example of the inference procedure for beam management

[0386] For dataset construction and performance evaluation (if applicable) in the AI / ML for beam management use case, a system-level simulation approach is adopted as baseline, and link-level simulation is optionally adopted.

[0387]

[0388] In the aforementioned 3GPP TR 38.843, Set B is defined as “Set B is a set of beams whose measurements are taken as inputs of the AI / ML model.” That is, Set B can be defined as a predefined set of beams to be used for measurements, specifically regarding which beams will be used to perform measurements on measurement results that may be provided as inputs to an AI / ML model for BM (i.e., beam management). In other words, it can be a set of beams to be measured for beam prediction.

[0389] Naturally, questions arise regarding how beams to be measured can be configured. This issue is an open problem, and discussions are also taking place at 3GPP based on research and proposals regarding it.

[0390]

[0391] FIG. 21 is a drawing illustrating an example of a near-field region and a far-field region separated by the Rayleigh distance in a system applicable to the present disclosure.

[0392] Figure 21 corresponds to the near-field region and the far-field region separated by the Rayleigh distance in “Cui, Mingyao, and Linglong Dai. “Channel estimation for extremely large-scale MIMO: Far-field or near-field?.” IEEE Transactions on Communications 70.4 (2022): 2663-2677”.

[0393] Meanwhile, the extremely large-scale massive MIMO system (E-MIMO) is being considered as a key enabling technology for 6G wireless communication because it can enhance spectral and energy efficiency, as well as link reliability, as the number of antennas increases to hundreds or thousands or more. However, in E-MIMO, as the number of antennas increases, the aperture size of the array becomes relatively larger, and the Rayleigh distance reaches hundreds of meters. For terminals located closer to the base station than the Rayleigh distance, they are situated in the so-called near-field region, rendering the planar wavefront assumption in the far-field region no longer valid. In other words, when modeling the near-field channel, a more accurate assumption regarding a spherical wavefront is required. Figure 21 illustrates a radiation field broadly classified into near-field and far-field based on the Rayleigh distance. In Figure 21, D represents the aperture size of the array, and λ represents the wavelength. h_n represents the channel gain from the user equipment (UE) antenna to the nth antenna element of the base station array, y_n represents the position vector of the nth antenna element, x_user represents the position vector of the UE antenna, and the origin or reference point can be assumed to be the position of the first antenna element on the antenna array.In addition, Δd represents the distance between adjacent antennas, and θ represents the angle of arrival (AoA) or angle of departure (AoD), which is the angle formed by the direction of the wavefront and the direction of the antenna array.

[0394]

[0395] FIG. 22 is a diagram illustrating an example of a comparison between the polar-domain representation and the angular-domain representation in a system applicable to the present disclosure.

[0396] Figure 22 corresponds to the comparison between the polar-domain representation and the angular-domain representation in “Cui, Mingyao, and Linglong Dai. “Channel estimation for extremely large-scale MIMO: Far-field or near-field?.” IEEE Transactions on Communications 70.4 (2022): 2663-2677”.

[0397] Therefore, in the E-MIMO environment, when modeling and estimating the channel, not only conventional angular information but also distance information must be considered. For example, while the array response vector (steering vector) representing the channel (matrix or vector) was previously simply a function of angle, in the near-field environment of E-MIMO, it can be given as a function of both angle and distance. Figure 22 compares the far-field steering vector representing the far-field channel as a conventional angular-domain representation with the near-field steering vector capable of representing the near-field channel in E-MIMO.

[0398] In Fig. 22, the left side represents the near-field steering vector b(θ_n,r_n), which is given as a function of both angle and distance, and the right side represents the conventional far-field steering vector a(θ_n), which is given only as a function of distance. Here, n is the sampling index. In the reference paper “Cui, Mingyao, and Linglong Dai. “Channel estimation for extremely large-scale MIMO: Far-field or near-field?.” IEEE Transactions on Communications 70.4 (2022): 2663-2677”, a polar-domain representation of the near-field channel of E-MIMO was proposed as shown in Fig. 22.

[0399]

[0400] FIG. 23 is a diagram illustrating an example of beam patterns for far-field (above figure: 32 antennas) and near-field (below figure: 256 antennas) in a system applicable to the present disclosure.

[0401] Figure 23 shows an example of a beam pattern that can be formed when beamforming is performed toward a terminal located at the same absolute position (30°, 5m) from a base station with a number of antennas N of 32 (above figure) or 256 (below figure), respectively.

[0402] In Figure 23, for a base station with 32 antennas (above figure), a terminal located at (30°, 5m) from the base station is in the far-field area, but when the number of base station antennas increases to 256 (below figure), a terminal at the same location is in the near-field area. Therefore, it can be observed that the beam pattern generated when N=32 is affected only by the angle, but when N=256, the beam pattern is dependent on both the angle and the distance.

[0403]

[0404] FIG. 24 is a drawing illustrating an example of various types of beam configuration options for beam measurement in a system applicable to the present disclosure.

[0405] In an E-MIMO environment where not only the Angular domain but also the distance domain must be considered, the question of “how can beams to be measured be configured?”—which was previously presented as an open problem in AI / ML-based beam management—becomes more prominent. Various methods are possible for configuring beams to be measured in E-MIMO. For example, Figure 24 illustrates examples of various configuration methods for beams to be measured.

[0406] In an E-MIMO environment, as shown in Fig. 24, a more efficient search method is required because two-dimensional (2D) beam search across both the angular domain and the distance domain is necessary. Additionally, when constructing a beam codebook, the size of the codebook becomes much larger than before. Therefore, in the case of an exhaustive search that performs measurements on the entire beam set, the beam training overhead becomes excessively large to the point where it is practically unmanageable, making it impossible to realize. Accordingly, research is currently being conducted on configuration methods for beams to be measured in E-MIMO.

[0407] First, in 3GPP TR 38.843, there is a study on how to design the entire set of beams, also referred to as Set A, titled “Cui, Mingyao, and Linglong Dai. “Channel estimation for extremely large-scale MIMO: Far-field or near-field?.” IEEE Transactions on Communications 70.4 (2022): 2663-2677”. That is, the reference paper “Cui, Mingyao, and Linglong Dai. "Channel estimation for extremely large-scale MIMO: Far-field or near-field?." IEEE Transactions on Communications 70.4 (2022): 2663-2677” designed a codebook for all beams to effectively represent the near-field region of E-MIMO, and in this disclosure, the beam codebook of the reference paper “Cui, Mingyao, and Linglong Dai. "Channel estimation for extremely large-scale MIMO: Far-field or near-field?." IEEE Transactions on Communications 70.4 (2022): 2663-2677” will be referred to as the polar codebook or polar-domain near-field codebook. In addition to the above polar codebook, a hybrid-field beam codebook has been designed considering the hybrid-field.

[0408] In 3GPP TR 38.843, Set A refers to the set of all possible beams as a prediction for the optimal beam when the optimal beam is predicted in the same format as the output of an AI / ML model; therefore, the design of the above near-field polar codebook or hybrid-field codebook can be seen as ultimately referring to the configuration of Set A in E-MIMO.

[0409] Since beam codebooks for E-MIMO have a significantly large codebook size corresponding to the number of codewords, the all-out search method, which finds the optimal beam by performing beam sweeping on all beam codewords within the codebook, incurs too much overhead. Therefore, it is practically impossible to implement an all-out search method based on beam sweeping in an E-MIMO environment. Instead, as shown in Figure 6.3.1-1 of 3GPP TR 38.843 (i.e., Figure 20 of the present disclosure), measurements can be performed on beams within a set with smaller cardinality, the results can be input into an AI / ML model, and the optimal beam can be determined based on the output of the AI / ML model. The set with smaller cardinality can be referred to as Set B. Therefore, for beam management using an AI / ML model, it is essential to first consider how to configure Set B.

[0410] In this disclosure, the hybrid-field beam codebook is referred to as the hybrid-field codebook, hybrid codebook, etc. Additionally, it is assumed that Set B is a subset of Set A. Although this disclosure assumes the hybrid field beam codebook to be Set A, the content and scope of the various embodiments of this disclosure are not limited to the hybrid codebook.

[0411] The configuration method for Set B, which has a smaller cardinality than the entire set of beam codewords expressed as Set A, can generally vary over time. Setting Set B differently over time offers advantages in terms of the efficiency of beam information investigation, compared to setting the same Set B every time to irradiate fixed beams. In this disclosure, the method of setting Set B differently over time is referred to as “variable” Set B. In contrast to Variable Set B, the method of continuously setting a constant Set B regardless of time can be referred to as fixed Set B.

[0412] However, as the hybrid-field beam codebook is a recently proposed invention, there is no existing method for configuring variable Set B for the hybrid codebook. Therefore, the present disclosure proposes a method for configuring variable Set B when the hybrid codebook is assumed to be Set A.

[0413]

[0414] Composition of various embodiments of the present disclosure

[0415] In various embodiments of the present disclosure, a variable Set B configuration method for a hybrid-field beam codebook is proposed. Prior to this, the hybrid-field beam codebook considered in the present disclosure is described.

[0416]

[0417] FIG. 25 is a diagram illustrating an example of the beam gain of a conventional DTF codebook in a 16-antenna array in a system applicable to the present disclosure.

[0418] FIG. 26 is a diagram illustrating an example of the beam gain of a conventional DTF codebook in E-MIMO with 512 antennas in a system applicable to the present disclosure.

[0419] FIG. 27 is a diagram illustrating an example of the broadside (0°) beam gain of a near-field polar codebook in E-MIMO with 512 antennas in a system applicable to the present disclosure.

[0420] FIG. 28 is a diagram illustrating an example of the near end-fire (60°) beam gain of polar codebook in E-MIMO with 512 antennas in a system applicable to the present disclosure.

[0421]

[0422] 1. Hybrid-field beam codebook

[0423] In the prior art, a new beam codebook was proposed to solve the problems of conventional beam codebooks. FIGS. 25 to 28 show the beam gain according to distance of conventional beam codebooks.

[0424] Figures 25 and 26 show the beam gain of a conventional DFT codebook, and Figures 27 and 28 show the beam gain of a near-field polar codebook.

[0425] Figure 25 shows the beam gain of a conventional DFT codebook for a MIMO environment with a relatively small number of antennas (16), and Figure 26 shows the beam gain for an E-MIMO environment with 512 antennas.

[0426] FIG. 27 shows the beam gain of a near-field polar codebook in the front direction (0°) of an antenna array, FIG. 28 shows the beam gain of a near-field polar codebook in the side direction (60°) of an antenna array, and the number of antennas is 512, assuming an E-MIMO environment.

[0427] Figures 25 and 26 respectively show the beam gain according to distance when beamforming is performed in the frontal direction of the antenna array. In Figures 25 and 26, the dotted lines represent the reference distance distinguishing the near field and far field from the frontal direction of the antenna. As shown in Figure 25, in the case of a multi-antenna system with 16 antennas, most of the area is the far field, so users located at all distances can be effectively supported. On the other hand, as shown in Figure 26, in the case of an E-MIMO system with 512 antennas, users located in the near field region experience a problem of reduced beam gain. This is because the planar wavefront assumption is no longer valid in the near field region, and the environment follows a spherical wavefront propagation environment. Consequently, beam management performance degrades when using conventional beam management techniques in very large-scale multi-antenna (E-MIMO) systems. To address these problems of the conventional DFT codebook, a near-field codebook has been proposed.

[0428] FIGS. 27 and 28 respectively show the gains of beams corresponding to codewords in the near-field polar codebook in the broadside region (φ=0°) and the near end-fire region (φ=60°) when the carrier frequency is 30 GHz and the number of antennas is 512. As shown in FIGS. 27 and 28, using the polar codebook reduces beam gain in the far field, so it cannot effectively support all regions. In addition, it can be seen that unnecessary beam codewords are inefficiently extracted within the minimum distance (10 m) indicated by the dotted line in the near end-fire region (φ=60°), which is the lateral direction of the antenna array.

[0429]

[0430] FIG. 29 is a drawing illustrating an example of an angle-dependent boundary of a near-field region in a system applicable to the present disclosure.

[0431] To solve the problem of the above polar codebook, the existing invention uses an angle-dependent effective Rayleigh distance as shown in Fig. 29. ...was considered. Here, λ represents the wavelength of the signal, D represents the aperture size of the antenna array, and φ represents the physical angle from the front direction of the antenna array.

[0432]

[0433] FIG. 30 is a drawing illustrating an example of a different number of codewords depending on angle in a hybrid-field beam codebook in a system applicable to the present disclosure.

[0434] In Fig. 30, m is the angular index, and N m is the number of quantization levels for each m. For the m-th angle, N m A number of distances are sampled, and beams corresponding to the sampled distances constitute the codebook as codewords. In Fig. 29, it can be observed that in the near-field polar codebook, the number of distance samples is constant regardless of the angle, whereas in the hybrid-field codebook, the number of distance samples varies depending on the angle. As can be seen in Fig. 29, since the distance to the boundary of the near-field region is maximum in the frontal direction of the antenna array, N in the hybrid codebook m In addition, it can be confirmed through Fig. 30 that it is maximum at the broadside and decreases as it approaches end-fire. That is, N, the number of quantization levels m This varies depending on the angle φ.

[0435] N in the Hybrid codebook m is specifically It can be set as follows. Here, is the floor function (maximum integer function), Θ m represents the spatial angle corresponding to the angular index m, and d m,1 represents the maximum distance that is first significantly sampled for the m-th spatial angle, and r min is the minimum sampled distance d finally extracted as the minimum distance of coverage (e.g., 10 m). m,Nm Ga d m,Nm >r min It is set to satisfy the condition, but, Even if d m,1Even when the above conditions are not satisfied, at least one distance is an operation required to be extracted, and C. Here, N t represents the number of antennas in the array, d represents the antenna spacing, and λ represents the wavelength of the signal, and is a function About It is a constant value satisfying . In this case, is the Fresnel integral function, and ρ represents the beam gain at the coverage boundary. At the coverage boundary, the beam gains of codewords corresponding to two adjacent sampled distances become equal, and the coverage boundary can be the harmonic mean of the two sampled distances. Meanwhile, the spatial angle is the sine value of the physical angle φ, Θ m can be sampled evenly. Θ m The largest value among the sampled distances for can be set to be equal to the boundary distance between the far-field and near-field regions. That is, the largest sampled distance It could be. Also, Θ m For , the n-th largest sampled distance d m,n silver It can be determined as follows.

[0436]

[0437] FIG. 31 is a drawing illustrating an example of a broadside (0°) beam gain of a hybrid-field codebook in a system applicable to the present disclosure.

[0438] FIG. 32 is a drawing illustrating an example of the near end-fire (60°) beam gain of a hybrid codebook in a system applicable to the present disclosure.

[0439] Figures 31 and 32 show the gains of beams corresponding to codewords in the hybrid-field beam codebook in the broadside and near-end-fire directions of the antenna array, respectively. Comparing Figures 31 and 32 with Figures 27 and 28, it can be observed that, unlike the polar codebook, the beam gain does not decrease in the far-field region in the hybrid codebook. The fact that the beam gain does not decrease in the far-field region when using the hybrid codebook is d m,1 This can be seen as the effect of setting it to be equal to the boundary distance between the far-field and near-field regions. Comparing Fig. 32 and Fig. 28, unlike the polar codebook, the minimum coverage distance r in the hybrid codebook is min It can be confirmed that unnecessary beam codewords are not wasted within (e.g., 10 m). As shown in FIGS. 31 and 32, using a hybrid codebook allows for efficient and effective support of both near-field and far-field regions while maintaining the beam gain ρ of the coverage boundary in both the broadside and near-end-fire directions of the antenna array.

[0440]

[0441] 2. Characteristics and assumptions of hybrid codebook

[0442] The following describes the characteristics of the hybrid codebook explained earlier. Considering the characteristics of the hybrid-field beam codebook described below, a Variable Set B configuration is proposed.

[0443]

[0444] (1) Inverted U-shape (stepped mound)

[0445] Observing Fig. 30 again, it can be seen that the shape of the entire codebook resembles a stepped mound, similar to a stone tomb. In a hybrid-field beam codebook, the number of quantization levels N m It depends on this angular index m, and N in the middle region corresponding to the broadside (frontal direction) of the antenna array within the entire range of m. m This is the maximum, and as you go toward both ends, N m This is because it decreases. In this disclosure, the characteristic of the entire outline of the hybrid codebook is referred to as an “inverted U-shape.”

[0446] In Fig. 30, the distance index can be denoted as n, and N m Similar to , it increases in the order progressing from bottom to top along the y-axis, and a distance index can be assigned. As mentioned in the previous section 1), d m,1 Let us recall that this is the largest sampled distance. Therefore, the smaller the distance index n, the larger the corresponding physical distance value. That is, without loss of generality, d m,1 >d m,2 >...>d m,NmThat is, a distance index of 1 means the codeword corresponding to the maximum physical distance among the codewords for each angle. Here, the maximum distance is the distance corresponding to the boundary between the near-field and the far-field. At this time, it can be noted that for any angular index m, there must exist a codeword with a distance index of 1.

[0447]

[0448] (2) Equal Importance Across Codewords as a Prior

[0449] If the codebook is ideally well-designed, regarding the problem of finding the optimal beam, it can be assumed as a prior probability that each beam codeword can carry the same amount of information when irradiated. Therefore, fundamentally, no codeword can be prioritized over another in measurement. In other words, the importance among codewords can be equal.

[0450]

[0451] (3) Highly Correlated Adjacent Codewords

[0452] When measuring a beam, there will be a high level of correlation between adjacent codewords. Here, adjacent codewords refer to codewords where the difference between an angular index m or a distance index n is 1 or less. Since there is a high level of correlation between adjacent codewords, including them in Set B may provide potentially redundant information to the AI / ML model, and the overhead for beam measurement may increase, leading to reduced efficiency.

[0453]

[0454] (4) Codeword Density Variation (across the angle-domain)

[0455] (1) As mentioned in the Inverted U-shape (stepped mound), the number of codewords is highest in the broadside (frontal direction of the antenna array) and decreases as it goes towards the end-fire (side direction). Looking at Fig. 30, the codeword density changes in the angle-domain. The hybrid-field beam codebook has a high codeword density in the broadside region, and the codeword density decreases as it goes towards the end-fire, and can contain only a single codeword at both ends.

[0456]

[0457] (5) Observation

[0458] FIG. 24 illustrates an exhaustive search method that completely examines all codewords present in a hybrid codebook and a partial search method that examines only some codewords within the codebook. In this case, the partial search method may mean performing measurements only on beams included in Set B, which has less cardinality than the entire codebook (Set A), as shown in Figure 6.3.1-1 of 3GPP TR 38.843 (i.e., FIG. 20 of the present disclosure), and inputting the measurement results into an AI / ML model.

[0459]

[0460] Note that in Fig. 24, unlike in Fig. 30, the entire codebook is represented as a U-shape rather than an inverted U-shape. That is, the y-axis in Fig. 30 is represented as the y-axis in Fig. 24 flipped vertically.

[0461] In this disclosure, a situation is considered where Set B is a subset of Set A; therefore, the types of the partial search method of FIG. 24 may vary depending on the method of selecting beam codewords to be measured from the entire hybrid-field beam codebook. The following is a description of the three types of Set B configuration methods proposed in the prior art.

[0462] (a) Angle-sparse method (type 1): Type 1 of FIG. 24 is a method of constructing Set B using only codewords corresponding to some selected angular indices among all angular indices, and in this disclosure, it will be referred to as the angle-sparse method. In the angle-sparse method, the values ​​of the selected angular indices for each distance index may form an arithmetic sequence, but due to the codeword density variation mentioned earlier in (4) Codeword Density Variation (across the angle-domain), the number of angular indices (and corresponding codewords) selected according to the distance index may vary.

[0463] (b) Distance-sparse method (type 2): Type 2 of FIG. 24 is a method of constructing Set B using only codewords corresponding to some selected distance indices among all distance indices, and in this disclosure, it will be referred to as the distance-sparse method. In the distance-sparse method, the values ​​of the distance indices selected for each angular index may form an arithmetic sequence, but due to the codeword density variation mentioned in (4), the number of distance indices (and corresponding codewords) selected according to the angular index may vary.

[0464] (c) 2D-sparse method (type 3): Type 3 of FIG. 24 is a method that includes codewords in Set B for which the remainder of the sum of the angular index and the distance index divided by a preset value is the same (e.g., divisible), and in this disclosure, it is referred to as a 2D-sparse method.

[0465] In the prior art [7], it was observed that the beam prediction accuracy performance for three types of Set B configuration methods improves in the order of angle-sparse, distance-sparse, and 2D-sparse. Here, beam prediction accuracy can be evaluated through performance indicators such as normalized gain or achievable rate for the predicted beam. In this disclosure, it is noted that the distance-sparse method has a performance advantage over the angle-sparse method. Angle-sparse can be viewed as a method that observes information in the distance-domain in more detail than in the angle-domain, whereas in the distance-sparse method, information in the angle-domain is observed in more detail. Since the distance-sparse method has a performance advantage, it can be assumed that the angle-domain is more informative (containing more useful information) than the distance-domain.

[0466] In the three types of Set B configuration methods mentioned above, the fact that the codewords constituting Set B change over time was not taken into account, and therefore, all of types 1 to 3 can be viewed as "fixed" Set B configurations.

[0467]

[0468] 3. Proposed Configuration for Variable Set B

[0469] Below, we propose a variable Set B configuration considering the characteristics of the hybrid-field beam codebook described earlier. Before proposing the Set B configuration method, we first examine an extreme case of the distance-sparse method (type 2 in Fig. 24), taking into account the fact that the angle-domain described in (5) Observation is more informative (contains more useful information) than the distance-domain.

[0470]

[0471] FIG. 33 is a diagram illustrating an example of an extreme case of a distance-sparse scheme for Set B configuration in a system applicable to the present disclosure.

[0472] Figure 33 illustrates the extreme distance-sparse method considered in this section. In Figure 33, blue represents the beam codewords selected as Set B, while yellow and blue represent the codewords of Set A (i.e., the entire codebook). In the extreme case of the distance-sparse method, the number of distance indices selected per angular index can be up to 2. In the extreme distance-sparse method, only some angular indices m can be selected as codewords for Set B. Among the aforementioned some angular indices m, N mFor m where =1, only one distance index can be selected, and N m For m ≥ 2, two distance indices corresponding to the endpoints of the distance-domain can be selected.

[0473] Generally, to estimate the whole in one dimension, it may be necessary to first know information about the endpoints. When the distance-sparse method, which has a performance advantage over angle-sparse, is applied to an extreme (Fig. 33), codewords can be selected only from the endpoints based on the distance index.

[0474] In Figure 33, it can be seen that multiple codewords with the same or similar angular indices are included as Set B. As previously discussed in (3) Highly Correlated Adjacent Codewords, since adjacent codewords are highly correlated with each other, including codewords with the same / similar angular indices as Set B reduces the efficiency of obtaining angle-domain information. This is because potentially redundant information may be provided to the AI / ML model, and the overhead for beam measurement may be large.

[0475] Therefore, the following proposes a Set B configuration that enhances the efficiency of acquiring angle domain information from the aforementioned extreme distance-sparse method. The configuration method for Set B can generally vary over time, and configuring Set B differently over time offers advantages in terms of the efficiency of beam information investigation, compared to irradiating fixed beams by setting the same Set B every time. Accordingly, the present disclosure proposes a “variable” Set B configuration method.

[0476] In this disclosure, a region-partitioned subsampling method is proposed as a variable Set B configuration. Region-partitioned subsampling refers to a method in which the entire codebook is divided into at least one region(s) according to given criteria, and the selection of codewords to be included in Set B is determined for each region according to a certain rule. In this section, the variable Set B configuration method based on region-partitioned subsampling proposed in this disclosure is described in the following five steps (Step 1) through (Step 5).

[0477]

[0478] (Step 1) Vertex Codeword

[0479] FIG. 34 is a diagram illustrating an example of vertex codewords for variable Set B configuration in a system applicable to the present disclosure.

[0480] FIG. 34 shows a vertex codeword selected according to the setting method proposed in the present disclosure. In FIG. 34, the thick lines at the top and bottom of the area represent the selected vertex codewords, and the background portion of the area and the thick lines at the top and bottom of the area represent the codewords of Set A (i.e., the entire codebook). Specifically, the method for determining the vertex codeword is as follows.

[0481] (1) When distance index n > 1: The first and last codewords (of the set of codewords with the same distance index) in each row are determined as vertices. Accordingly, codewords that do not have adjacent codewords to the left or right in the angle domain are selected as vertices. When moving one step to the left or right (angle index m) from the corresponding codeword, there are no more codewords in the codebook.

[0482] (2) When the distance index n=1: Among the vertices determined in the above case (when the distance index n>1), any two codewords that are closest to each other are selected as a pair (except for the pair of codewords with the largest distance index), the angle index is the arithmetic mean of the angle indices of the two selected codewords, and the codewords with a distance index of 1 are determined as vertices. However, if the average value is not an integer, the angle index is set to a value that is closer to the angle index of the codeword with the larger distance index among the selected vertex pairs.

[0483] Figure 34 shows vertex codewords determined by the above method. When comparing Figure 34 with Figure 33, it can be seen that in the extreme distance-sparse method, codewords with the same / similar angular index are all included in Set B, whereas in the proposed vertex setting method, potentially redundant information is removed, thereby improving the efficiency of obtaining angle domain information.

[0484] Therefore, if the vertex codewords proposed in this disclosure are preferentially included in Set B, relatively sufficient information can be efficiently obtained in the angle-domain, whereas only a maximum of two codeword beams can be included for each distance index. However, the case where the distance index is 1 is excluded, because the codeword with distance index n=1 corresponds to the maximum physical distance and is the only distance index that exists for all angle indices. In extreme cases, considering the case where no UE exists within the near-field region boundary, service may be sufficient with only the codewords with n=1. From this perspective, n=1 can be considered to be of great importance, and thus more than two codeword beams can be set as vertices.

[0485] Vertex codewords are codewords corresponding to the edge portions of the inverted-U shape examined in (1) Inverted U-shape (stepped mound) of 2. Characteristics and assumption of hybrid codebook, and they serve to facilitate the understanding of the overall outline information of the codebook. Furthermore, if we examine the positions of the set vertex codewords (for the case where n > 1), it can be seen that they correspond to points that appropriately reflect the codeword density variation examined in (4) Codeword Density Variation (across the angle-domain) of 2. Characteristics and assumption of hybrid codebook. In FIG. 34, the vertex codewords for n > 1 correspond to the number of codewords N according to the angular index m. m This is because it exists in this changing position.

[0486] Including vertex codewords preferentially in Set B in the manner proposed in this disclosure has the advantage of effectively reflecting the inverted-U shape and codeword density variation, which are characteristics of the entire codebook. Furthermore, since the efficiency of acquiring angle domain information has been enhanced from the extreme cases of the distance-sparse method, which is superior in performance to angular-sparse, the vertex codeword setting method can be considered a design that takes into account trends observed from existing experimental results. Therefore, if vertex codewords are included preferentially in Set B, they can be considered codewords serving as appropriate reference points. In other words, it is desirable for the vertex codewords of this disclosure to be included preferentially in Set B.

[0487] Below, a method for constructing variable Set B by performing region-partitioned subsampling based on the set vertices is proposed step-by-step.

[0488]

[0489] (Step 2) Region Boundary

[0490] FIG. 35 is a drawing illustrating an example of a region boundary for a variable Set B configuration in a system applicable to the present disclosure.

[0491] Regions are distinguished based on the vertices determined in the previous (Step 1) Vertex Codeword. Here, a region refers to a set of codewords having adjacent angular and distance indices within a codebook. Therefore, a region can be treated as a set of ordered pairs consisting of angular and distance indices and can be visualized on a plane, such as a pixel grid. FIG. 35 shows codewords corresponding to a region boundary set in the manner proposed in this disclosure. In FIG. 35, the uppermost and lowermost parts of the boundary lines represent selected vertex codewords, the middle part of the boundary lines represents codewords corresponding to the region boundary, and the background of the region, the middle part of the boundary lines, and the uppermost and lowermost parts of the boundary lines represent codewords of Set A (i.e., the entire codebook). Each codeword is displayed corresponding to each pixel in a grid such as FIG. 35. The boundary separating the region consists of codewords corresponding to pixels selected to represent straight lines connecting vertices, and the pixels corresponding to the straight lines serving as the region boundary can be determined according to Bresenham's line algorithm.

[0492] As can be seen in FIG. 35, each region separated by connecting vertices on a pixel grid can be represented as a rasterized quadrilateral. FIG. 35 shows that in each region, the codeword density, which was previously examined in (4) Codeword Density Variation (across the angle-domain), does not change significantly and remains relatively uniform. Therefore, it can be said that it is reasonable to perform region-partitioning on the entire codebook in the manner proposed in this disclosure.

[0493]

[0494] (Step 3) Region-Specific Frequency

[0495] (Step 2) The subsampling frequency at which a codeword from each region (region index: r) determined at the region boundary is included in Set B may vary depending on the region. Region-specific subsampling interval k r This can be set differently depending on region r, and the subsampling interval k r It is used later (Step 5) in Codeword Selection. k r There may exist some kind of rule that determines . For example, k rcan be set inversely proportional to the number of codewords constituting each region, in which case the frequency becomes proportional to the codeword density. Additionally, an r-independent subsampling interval can be set regardless of the region. Region-specific subsampling interval k r The rules for determining [this] are merely examples, and the scope of the present disclosure is not limited thereto.

[0496] (Step 4) Intra-Region Indexing

[0497] FIG. 36 is a drawing illustrating an example of intra-region indexing with a zigzag pattern for variable Set B configuration in a system applicable to the present disclosure.

[0498] Within each region, an index can be assigned to each codeword according to a certain rule, and the assigned intra-region index is utilized in the subsequent (Step 5) Codeword Selection. For example, intra-region indexing can be performed using methods such as Top-to-bottom & left-to-right scanning, a zigzag pattern (serpentine path), or a spiral. Top-to-bottom & left-to-right scanning is a method of assigning intra-region indices by scanning the pixel grid representing the codebook from top to bottom, similar to how humans read documents like books, while proceeding from left to right at the same height. A zigzag pattern (serpentine path) is a method of assigning indices by moving back and forth in a winding manner as shown in Fig. 36; when the boundary of a region is encountered in the direction of travel, the path is deviated from, but indexing is performed in the reverse direction from an adjacent row (or column) in accordance with the order of travel. The spiral method refers to a method of assigning an index to all codewords within a region without duplication or omission by rotating through adjacent codewords centered on one codeword. The rules for performing intra-region indexing are merely examples, and the scope of the present disclosure is not limited thereto.

[0499]

[0500] (Step 5) Codeword Selection

[0501] mod (index, k for each area rCodewords having this specific value (selection value) are selected and included in Set B. Here, the index is the intra-region index determined in (Step 4) Intra-Region Indexing, and k r represents the region-specific subsampling interval determined in (Step 5) Codeword Selection. Variable Set B can be constructed by varying the selection value over time. The above selection value can generally vary depending on the region, 0, 1, ..., k r It can be set to one of the values ​​-1.

[0502] Region-specific subsampling interval k r and / or the selection value can, of course, be used for the purpose of excluding all codewords included in a specific region from Set B. For example, k r Set to 0 or the selection value k r Setting the value to a large integer above can be agreed upon as excluding the corresponding region. Therefore, in cases where it is sufficient to select and investigate only a portion of the entire regions of the codebook, the present disclosure can reduce the overhead for beam measurement and improve efficiency.

[0503]

[0504] 4. Signaling Information

[0505] At least one of the information below, or the Set B configuration itself determined by the above method, may be signaled from the BS to the UE as configuration information related to measurement. When information about the Set B configuration itself is signaled, the configuration information may be represented as a bitmap.

[0506] (a) Region boundary information: The region boundary determined in the previous (Step 2) Region Boundary may be given to the UE in the form of a bitmap, or information about the vertices determined in the previous (Step 1) Vertex Codeword may be given to the UE in the form of vertex beam codeword indices.

[0507] (b) Region-specific subsampling interval: k determined at the Region-Specific Frequency (Step 3) mentioned earlier r Values ​​can be transmitted to the UE. k r Since it is defined for each region within the entire codebook, there can be as many values ​​as there are regions.

[0508] (c) Indicator for Intra-region Indexing Method: The UE can be instructed to use methods such as top-to-bottom & left-to-right scanning, zigzag patterns (serpentine path), or spirals, as described in the previous (Step 4) Intra-Region Indexing. Since the Intra-region Indexing method can be configured differently for each region, there may be as many indicators as there are regions.

[0509] (d) Selection value: The selection values ​​required in the previous (Step 5) Codeword Selection can be delivered to the UE. The selection value can be defined as a value compared with the remainder obtained by dividing the intra-region index by the region-specific subsampling interval to select codewords to be included in Set B by region; since it is defined for each region within the entire codebook, there may be as many values ​​as there are regions.

[0510]

[0511] Effects of various embodiments of the present disclosure

[0512] The expected effects of the various embodiments of the present disclosure are as follows.

[0513] Various embodiments of the present disclosure can provide standard operation definitions for variable Set B configuration schemes of a hybrid-field beam codebook.

[0514]

[0515] The characteristic configurations of various embodiments of the present disclosure are as follows.

[0516] (1) A base station method for performing beam prediction based on an AI / ML model, comprising: determining setting information related to measurement; transmitting the setting information to a terminal; transmitting a reference signal to the terminal based on the setting information; and receiving information related to a measurement result generated based on the reference signal from the terminal, wherein the setting information includes at least one of region boundary information, region-specific subsampling interval information, intra-region indexing pattern indicator information, and selection value information.

[0517] (2) A method in which at least the selection value information among the above setting information changes over time (variable).

[0518] (3) A method in which some beams must be included as the initial configuration of Set B in the above configuration information, and the beams that must be included are vertex codewords.

[0519] (4) The above vertex codewords include codewords corresponding to the minimum and maximum values ​​of the angular index among the set of codewords of the same distance index.

[0520] (5) The above vertex codewords include codewords in which the average of the angular indices of any two codewords closest to each other among the vertices included in claim 4 is the angular index and 1 is the distance index.

[0521]

[0522] [Explanation regarding the 1st node claim]

[0523] The embodiments described above will be explained in detail below with reference to FIG. 37 regarding the operation of the first node. 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.

[0524] FIG. 37 is a diagram illustrating an example of the operation process of a first node in a system applicable to the present disclosure.

[0525] According to various embodiments of the present disclosure, a method performed by a first node in a communication system is provided.

[0526] According to various embodiments of the present disclosure, each of the first node and the second node may correspond to either a terminal or a base station in a wireless communication system.

[0527] The embodiment of FIG. 37 may further include, prior to step S3701, one or more of the steps of: the first node receiving one or more synchronization signals from the second node; the first node receiving system information from the second node; the first node receiving configuration information from the second node; and the first node receiving control information from the second node.

[0528] The embodiment of FIG. 37 may further include, prior to step S3701, one or more of the steps of: the first node transmitting a random access preamble to the second node; the first node receiving a random access response (RAR) from the second node; the first node transmitting a random access message 3 to the second node; and the first node receiving a contention resolution message from the second node. Message 3 is the first PUSCH transmission scheduled by the RAR with a RAR UL grant.

[0529] In step S3701, the first node receives first configuration information of a first set of codewords corresponding to some of the total codewords from the second node. The first configuration information includes some or all of a plurality of first vertex codewords. The plurality of first vertex codewords are codewords among the total codewords for which there are no adjacent codewords in the angle region.

[0530] In step S3702, the first node receives a first reference signal based on the first setting information from the second node.

[0531] In step S3703, the first node transmits information related to the measurement result based on the reference signal to the second node.

[0532] In step S3704, the first node receives second setting information of the second codeword set modified from the first codeword set from the second node based on the measurement result.

[0533] In step S3705, the first node receives a second reference signal based on the second setting information from the second node.

[0534]

[0535] According to various embodiments of the present disclosure, the plurality of first vertex codewords may include a codeword corresponding to the minimum value of the angle index and a codeword corresponding to the maximum value of the angle index among the plurality of codewords associated with the same distance index.

[0536] According to various embodiments of the present disclosure, the first setting information may further include a second vertex codeword. A second angle index associated with the second vertex codeword may correspond to the average of the angle indices of any two first vertex codewords that are closest to each other among the plurality of first vertex codewords. The second distance index of the second vertex codeword may be 1.

[0537] According to various embodiments of the present disclosure, the second setting information may include one or more of a region-specific subsampling interval or a selection value for a plurality of regions into which the entire codewords are divided. The plurality of regions may be divided based on the plurality of first vertex codewords and the second vertex codeword. The selection value may be a value related to the remainder obtained by dividing the intra-region index of the codewords included in each of the plurality of regions by the region-specific subsampling interval.

[0538] According to various embodiments of the present disclosure, the intra-region index may be an index assigned based on a rule set for codewords included in each of the plurality of regions. The second setting information may further include intra-region indexing pattern indicator information related to the set rule.

[0539] According to various embodiments of the present disclosure, the second setting information may relate to the adjustment of the selection of codewords within each of the plurality of regions based on the measurement results related to the performance information of the first codeword set. The second setting information may change over time. The second codeword set may include one or more codewords among the first codeword set for which the selection value corresponds to a specific value. The specific value may change over time.

[0540] According to various embodiments of the present disclosure, the region-specific subsampling interval may be based on the codeword density of each of the plurality of regions.

[0541]

[0542] According to various embodiments of the present disclosure, a first node is provided in a communication system. The first node 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 first node according to FIG. 37.

[0543]

[0544] According to various embodiments of the present disclosure, an apparatus for controlling a first node 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 operation of the first node according to FIG. 37 based on execution by the at least one processor.

[0545]

[0546] 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 first node according to FIG. 37.

[0547]

[0548] [Explanation regarding the 2nd node claim]

[0549] The embodiments described above will be explained in detail below with reference to FIG. 38 regarding the operation of the second node. 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.

[0550] FIG. 38 is a diagram illustrating an example of the operation process of a second node in a system applicable to the present disclosure.

[0551] According to various embodiments of the present disclosure, a method performed by a second node in a communication system is provided.

[0552] According to various embodiments of the present disclosure, each of the first node, the second node, and the third node may correspond to either a terminal or a base station in a wireless communication system.

[0553] The embodiment of FIG. 38 may further include, prior to step S3801, one or more of the steps of: the second node transmitting one or more synchronization signals to the first node; the second node transmitting system information to the first node; the second node transmitting configuration information to the first node; and the second node transmitting control information to the first node.

[0554] The embodiment of FIG. 38 may further include, prior to step S3801, one or more of the steps of: the second node receiving a random access preamble from the first node; the second node transmitting a random access response (RAR) to the first node; the second node receiving a random access message 3 from the first node; and the second node transmitting a contention resolution message to the first node. Message 3 is the first PUSCH transmission scheduled by the RAR with a RAR UL grant.

[0555] In step S3801, the second node transmits first configuration information of a first set of codewords corresponding to some of the total codewords to the first node. The first configuration information includes some or all of a plurality of first vertex codewords. The plurality of first vertex codewords are codewords among the total codewords for which there are no adjacent codewords in the angle region.

[0556] In step S3802, the second node transmits a first reference signal based on the first setting information to the first node.

[0557] In step S3803, the second node receives information related to the measurement result based on the reference signal from the first node.

[0558] In step S3804, the second node transmits second setting information of the second codeword set modified from the first codeword set to the first node based on the measurement result.

[0559] In step S3805, the second node transmits a second reference signal based on the second setting information to the first node.

[0560]

[0561] According to various embodiments of the present disclosure, the plurality of first vertex codewords may include a codeword corresponding to the minimum value of the angle index and a codeword corresponding to the maximum value of the angle index among the plurality of codewords associated with the same distance index.

[0562] According to various embodiments of the present disclosure, the first setting information may further include a second vertex codeword. A second angle index associated with the second vertex codeword may correspond to the average of the angle indices of any two first vertex codewords that are closest to each other among the plurality of first vertex codewords. The second distance index of the second vertex codeword may be 1.

[0563] According to various embodiments of the present disclosure, the second setting information may include one or more of a region-specific subsampling interval or a selection value for a plurality of regions into which the entire codewords are divided. The plurality of regions may be divided based on the plurality of first vertex codewords and the second vertex codeword. The selection value may be a value related to the remainder obtained by dividing the intra-region index of the subsampled codewords of each of the plurality of regions by the region-specific subsampling interval.

[0564] According to various embodiments of the present disclosure, the intra-region index may be an index assigned based on a rule set for codewords included in each of the plurality of regions. The first setting information may further include intra-region indexing pattern indicator information related to the set rule.

[0565] According to various embodiments of the present disclosure, the second setting information may relate to the adjustment of the selection of codewords within each of the plurality of regions based on the measurement results related to the performance information of the first codeword set. The second setting information may change over time. The second codeword set may include one or more codewords among the first codeword set for which the selection value corresponds to a specific value. The specific value may change over time.

[0566] According to various embodiments of the present disclosure, the region-specific subsampling interval may be based on the codeword density of each of the plurality of regions.

[0567]

[0568] According to various embodiments of the present disclosure, a second node is provided in a communication system. The second node includes a transceiver and at least one processor, and the at least one processor may be configured to perform the operation method of the second node according to FIG. 38.

[0569]

[0570] According to various embodiments of the present disclosure, an apparatus for controlling a second node 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 operation of the second node according to FIG. 38 based on execution by the at least one processor.

[0571]

[0572] 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 second node according to FIG. 38.

[0573]

[0574] Communication systems applicable to the present disclosure

[0575] FIG. 39 illustrates a communication system (1) applicable to various embodiments of the present disclosure.

[0576] Referring to FIG. 39, 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.

[0577] 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).

[0578] 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.

[0579] 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.

[0580] 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).

[0581]

[0582] Frequency Range designationCorresponding frequency rangeSubcarrier SpacingFR1450MHz-6000MHz15, 30, 60kHzFR224250MHz-52600MHz60, 120, 240kHz

[0583]

[0584] 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).

[0585] Frequency Range designationCorresponding frequency rangeSubcarrier SpacingFR141MHz-7125MHz15, 30, 60kHzFR224250MHz-52600MHz60, 120, 240kHz

[0586] 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.

[0587]

[0588] Wireless devices applicable to the present disclosure

[0589] Hereinafter, examples of wireless devices to which various embodiments of the present disclosure are applied will be described.

[0590] FIG. 40 illustrates a wireless device that can be applied to various embodiments of the present disclosure.

[0591] Referring to FIG. 40, 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. 39.

[0592] 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.

[0593] 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.

[0594] 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.

[0595] 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.

[0596] 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.

[0597] 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.

[0598] FIG. 41 illustrates another example of a wireless device that can be applied to various embodiments of the present disclosure.

[0599] According to FIG. 41, 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).

[0600] The difference between the example of the wireless device described in FIG. 40 and the example of the wireless device in FIG. 41 is that in FIG. 40, the processor (102, 202) and the memory (104, 204) are separated, whereas in the example of FIG. 41, the memory (104, 204) is included in the processor (102, 202).

[0601] 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.

[0602] Hereinafter, examples of signal processing circuits to which various embodiments of the present disclosure are applied are described.

[0603] FIG. 42 illustrates a signal processing circuit for a transmission signal.

[0604] Referring to FIG. 42, 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. 42 may be performed in the processor (102, 202) and / or transceiver (106, 206) of FIG. 40. The hardware elements of FIG. 42 may be implemented in the processor (102, 202) and / or transceiver (106, 206) of FIG. 40. For example, blocks 1010 through 1060 may be implemented in the processor (102, 202) of FIG. 40. Additionally, blocks 1010 to 1050 may be implemented in the processor (102, 202) of FIG. 40, and block 1060 may be implemented in the transceiver (106, 206) of FIG. 40.

[0605] The codeword can be converted into a wireless signal through the signal processing circuit (1000) of FIG. 42. 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).

[0606] 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.

[0607] 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.

[0608] 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. 42. For example, a wireless device (e.g., 100, 200 in FIG. 40) 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.

[0609] Hereinafter, examples of wireless device applications to which various embodiments of the present disclosure are applied will be described.

[0610] FIG. 43 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. 39).

[0611] Referring to FIG. 43, the wireless device (100, 200) corresponds to the wireless device (100, 200) of FIG. 40 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. 40. For example, the transceiver(s) (114) may include one or more transceivers (106, 206) and / or one or more antennas (108, 208) of FIG. 40. 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).

[0612] 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. 39, 100a), a vehicle (Fig. 39, 100b-1, 100b-2), an XR device (Fig. 39, 100c), a portable device (Fig. 39, 100d), a home appliance (Fig. 39, 100e), an IoT device (Fig. 39, 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. 39, 400), a base station (Fig. 39, 200), a network node, etc. Wireless devices can be used in a movable or fixed location depending on the use—e.g., service.

[0613] In FIG. 43, 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.

[0614] Hereinafter, an implementation example of FIG. 43 will be described in more detail with reference to the drawings.

[0615] FIG. 44 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).

[0616] Referring to FIG. 44, 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. 43.

[0617] 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.

[0618] 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).

[0619] FIG. 45 illustrates a vehicle or autonomous vehicle applicable to various embodiments of the present disclosure.

[0620] Vehicles or autonomous vehicles can be implemented as mobile robots, vehicles, trains, manned or unmanned aerial vehicles (AVs), ships, etc.

[0621] Referring to FIG. 45, 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 each correspond to blocks 110 / 130 / 140 of FIG. 43.

[0622] 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.

[0623] 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.

[0624] FIG. 46 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.

[0625] Referring to FIG. 46, 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. 43, respectively.

[0626] 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.

[0627] 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).

[0628] FIG. 47 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.

[0629] Referring to FIG. 47, 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. 43, respectively.

[0630] 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.

[0631] 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).

[0632] 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).

[0633] FIG. 48 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.

[0634] Referring to FIG. 48, 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. 43, respectively.

[0635] 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.

[0636] FIG. 49 illustrates an AI device applicable to various embodiments of the present disclosure.

[0637] 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.

[0638] Referring to FIG. 49, 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. 43.

[0639] 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).

[0640] 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.

[0641] 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).

[0642] 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.

[0643] The learning processor unit (140c) can train a model composed of an artificial neural network using training 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 an external device through the communication unit (110) and / or stored in the memory unit (130).

[0644] 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

In a method performed by the first node, A step of receiving first setting information of a first set of codewords corresponding to some of the total codewords from a second node, The above-mentioned first setting information includes some or all of a plurality of first vertex codewords, and The plurality of first vertex codewords are codewords among the entire set of codewords for which no adjacent codeword exists in the angle region; A step of receiving a first reference signal based on the first setting information from the second node; A step of transmitting information related to the measurement result based on the reference signal to the second node; A step of receiving second setting information of a second codeword set modified from the first codeword set from the second node based on the above measurement result; A step comprising receiving a second reference signal based on the second setting information from the second node, method. In Article 1, The plurality of first vertex codewords above include a codeword corresponding to the minimum value of the angle index and a codeword corresponding to the maximum value of the angle index among the plurality of codewords associated with the same distance index. method. In Article 1, The above first setting information further includes a second vertex codeword, and The second angle index associated with the second vertex codeword corresponds to the average of the angle indices of any two first vertex codewords closest to each other among the plurality of first vertex codewords, and The second distance index of the above second vertex codeword is 1, method. In Paragraph 3, The second setting information above includes one or more of a region-specific subsampling interval or a selection value for a plurality of regions into which the entire codewords are divided, and The plurality of regions are divided based on the plurality of first vertex codewords and the second vertex codeword, and The above selection value is a value related to the remainder obtained by dividing the intra-region index of the codewords included in each of the above plurality of regions by the region-specific subsampling interval, method. In Paragraph 4, The intra-region index is an index assigned based on a rule set for codewords included in each of the aforementioned multiple regions, and The above second setting information further includes intra-region indexing pattern indicator information related to the above-mentioned set rule, method. In Paragraph 4, The second setting information is related to the adjustment of the selection of codewords within each of the plurality of regions based on the measurement result related to the performance information of the first codeword set, and The above second setting information changes over time, and The second codeword set above includes one or more codewords among the first codeword set in which the selected value corresponds to a specific value, and The above specific value changes over time, method. In Paragraph 4, The above region-specific subsampling interval is based on the codeword density of each of the above plurality of regions, method. In a method performed by a second node, A step of transmitting first configuration information of a first set of codewords corresponding to some of the total codewords to a first node, The above-mentioned first setting information includes some or all of a plurality of first vertex codewords, and The plurality of first vertex codewords are codewords among the entire set of codewords for which no adjacent codeword exists in the angle region; A step of transmitting a first reference signal based on the first setting information to the first node; A step of receiving information related to a measurement result based on the above reference signal from the first node; A step of transmitting second setting information of a second codeword set modified from the first codeword set to the first node based on the above measurement result; A step comprising transmitting a second reference signal based on the second setting information to the first node. method. In Article 8, The plurality of first vertex codewords above include a codeword corresponding to the minimum value of the angle index and a codeword corresponding to the maximum value of the angle index among the plurality of codewords associated with the same distance index. method. In Article 8, The above first setting information further includes a second vertex codeword, and The second angle index associated with the second vertex codeword corresponds to the average of the angle indices of any two first vertex codewords closest to each other among the plurality of first vertex codewords, and The second distance index of the above second vertex codeword is 1, method. In Article 10, The second setting information above includes one or more of a region-specific subsampling interval or a selection value for a plurality of regions into which the entire codewords are divided, and The plurality of regions are divided based on the plurality of first vertex codewords and the second vertex codeword, and The above selection value is a value related to the remainder obtained by dividing the intra-region index of the subsampled codewords of each of the above plurality of regions by the region-specific subsampling interval, method. In Article 11, The intra-region index is an index assigned based on a rule set for codewords included in each of the aforementioned multiple regions, and The above first setting information further includes intra-region indexing pattern indicator information related to the above-mentioned set rule, method. In Article 11, The second setting information is related to the adjustment of the selection of codewords within each of the plurality of regions based on the measurement result related to the performance information of the first codeword set, and The above second setting information changes over time, and The second codeword set above includes one or more codewords among the first codeword set in which the selected value corresponds to a specific value, and The above specific value changes over time, method. In Article 11, The above region-specific subsampling interval is based on the codeword density of each of the above plurality of regions, method. At the first node, 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, Node 1. In the second node, 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, Node 2. In a control device for controlling a first node, 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. In a control device for controlling a second node, 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. 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. 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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