Method and device for beam management in wireless communication system

Abstract

This method may comprise steps in which a wireless user device in a wireless communication system: receives, from a base station, at least one wide beam on the basis of approximate wide beam sweeping; receives at least one narrow beam determined on the basis of the measurement of the at least one wide beam, and determines an optimum narrow beam so as to transmit, to the base station, information about the optimum narrow beam; and receives data transmitted on the basis of the optimum narrow beam.

Description

Beam management method and device in a wireless communication system

[0001] The present disclosure relates to a method and device for managing beams in a wireless communication system. Specifically, the present disclosure relates to a method and device for managing beams based on artificial intelligence / machine learning (AI / ML).

[0002]

[0003] The International Telecommunication Union (ITU) is developing the International Mobile Telecommunication (IMT) framework and standards, and is currently discussing fifth-generation (5G) communications through a program called "IMT for 2020 and beyond."

[0004] To meet the requirements presented in "IMT for 2020 and beyond," the 3rd Generation Partnership Project (3GPP) NR (New Radio) system is being discussed to support various numerologies based on time-frequency resource units, taking into account various scenarios, service requirements, and potential system compatibility.

[0005] Additionally, 5G communications can support the transmission of physical signals or physical channels through multiple beams to overcome adverse channel conditions such as high path loss, phase noise, and frequency offset that occur at high carrier frequencies. Through this, 5G communications can support applications such as enhanced Mobile Broadband (eMBB), massive Machine Type Communications (mMTC), and Ultra Reliable and Low Latency Communication (URLLC).

[0006]

[0007] The technical problem of the present disclosure is a method and device for managing a beam in a wireless communication system.

[0008] The technical problem of the present disclosure is a method and device for managing a beam based on AI / ML.

[0009] The technical problem of the present disclosure is a method and device for performing learning on an AI / ML model based on a beam / beam pair and deriving an optimal beam through an inference operation of the learned AI / ML model.

[0010] The technical problem of the present disclosure is a method and device for predicting a beam in a spatial domain based on AI / ML.

[0011] The technical problem of the present disclosure is a procedure and signaling for managing a beam based on AI / ML.

[0012] The technical problems to be achieved in the present disclosure are not limited to the technical problems mentioned above, and other technical problems not mentioned can be clearly understood by a person having ordinary skill in the technical field to which the present disclosure belongs from the description below.

[0013]

[0014] According to one aspect of the present disclosure, a method may include the steps of: receiving at least one wide beam from a base station based on a rough wide beam sweep; receiving at least one narrow beam determined based on a measurement of the at least one wide beam; determining an optimal narrow beam and transmitting information about the optimal narrow beam to the base station; and receiving data transmitted based on the optimal narrow beam.

[0015] Also, according to one aspect of the present disclosure, a wireless user device includes at least one processor and a memory storing instructions for causing the wireless user device to perform a specific operation by the at least one processor, wherein the specific operation may include: receiving at least one wide beam from a base station based on a rough wide beam sweep, receiving at least one narrow beam determined based on a measurement of the at least one wide beam, determining an optimal narrow beam and transmitting information about the optimal narrow beam to the base station, and receiving data transmitted based on the optimal narrow beam.

[0016] Additionally, the following may be commonly applied:

[0017] According to one aspect of the present disclosure, when an AI / ML (artificial intelligence) model exists in a base station, the base station can transmit an entire beam to a wireless user device based on beam sweeping, acquire a data set obtained from the wireless user device to perform training on the AI / ML model, acquire a measurement value for at least one wide beam from the wireless user device to provide it as an input to the trained AI / ML model, and generate at least one narrow beam as a result value through inference of the AI / ML model to transmit the result to the wireless user device.

[0018] In addition, according to one aspect of the present disclosure, when an AI / ML model exists in a wireless user device, the wireless user device may receive an entire beam from a base station based on beam sweeping, acquire a data set based on the received entire beam to perform learning for the AI / ML model, perform measurement for at least one wide beam to provide it as an input for the learned AI / ML model, and generate at least one narrow beam as a result value through inference of the AI / ML model to transmit information about the at least one narrow beam to the base station.

[0019]

[0020] According to the present disclosure, a beam management method in a wireless communication system can be provided.

[0021] According to the present disclosure, a method for managing a beam based on AI / ML can be provided.

[0022] According to the present disclosure, a method can be provided for performing learning on an AI / ML model based on a beam / beam pair and deriving an optimal beam through an inference operation of the learned AI / ML model.

[0023] According to the present disclosure, a method and device for predicting a beam in a spatial domain based on AI / ML are provided.

[0024] According to the present disclosure, a procedure and signaling for managing a beam based on AI / ML can be provided.

[0025] According to the present disclosure, a procedure and signaling for managing a beam based on AI / ML in a wireless communication system can be provided.

[0026] The effects that can be obtained from the present disclosure are not limited to the effects mentioned above, and other effects that are not mentioned will be clearly understood by a person having ordinary skill in the art to which the present disclosure pertains from the description below.

[0027]

[0028] FIG. 1 is a drawing for explaining the frame structure of a wireless communication system to which the present disclosure can be applied.

[0029] FIG. 2 is a diagram showing the resource structure of a wireless communication system to which the present disclosure can be applied.

[0030] FIG. 3 is a diagram illustrating CSI-RS resources to which the present disclosure can be applied.

[0031] FIG. 4 is a diagram illustrating a type 1 CSI applicable to the present disclosure.

[0032] FIG. 5 is a diagram illustrating a method of using multiple SRSs that can be applied to the present disclosure.

[0033] FIG. 6 is a diagram illustrating a method for performing non-codebook based precoding that can be applied to the present disclosure.

[0034] FIG. 7 is a diagram illustrating CSI omission applicable to the present disclosure.

[0035] Figure 8 is a diagram illustrating AI / ML model life cycle management applicable to the present disclosure.

[0036] FIG. 9 is a diagram illustrating a method for performing AI / ML model training based on Type 1 applicable to the present disclosure.

[0037] FIG. 10 is a diagram illustrating a method for performing AI / ML model training based on Type 2 applicable to the present disclosure.

[0038] FIG. 11 is a diagram illustrating a method for performing AI / ML model training based on Type 3 applicable to the present disclosure.

[0039] FIG. 12 is a diagram illustrating a type of beam management procedure that can be applied to the present disclosure.

[0040] FIG. 13 is a diagram illustrating a method for performing beam prediction in a spatial domain applicable to the present disclosure.

[0041] FIG. 14 is a diagram illustrating a method for performing an AI / ML beam management procedure on the network side applicable to the present disclosure.

[0042] FIG. 15 is a diagram illustrating a method for performing an AI / ML beam management procedure on a terminal side applicable to the present disclosure.

[0043] FIG. 16 is a diagram illustrating a method in which an AI / ML model is learned on a base station side applicable to the present disclosure, inference is performed on the AI / ML model on a terminal side, and a beam management procedure is performed.

[0044] FIG. 17 is a flowchart of a method for performing beam management applicable to the present disclosure.

[0045] Figure 18 is a drawing showing a base station device and a terminal device to which the present disclosure can be applied.

[0046]

[0047] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the attached drawings so that those skilled in the art can easily implement the present disclosure. However, the present disclosure may be implemented in various different forms and is not limited to the embodiments described herein.

[0048] In describing embodiments of the present disclosure, detailed descriptions of known configurations or functions will be omitted if they are deemed to obscure the gist of the present disclosure. Furthermore, portions of the drawings that are irrelevant to the description of the present disclosure have been omitted, and similar portions are designated with similar reference numerals.

[0049] In the present disclosure, when a component is said to be "connected," "coupled," or "connected" to another component, this may include not only a direct connection, but also an indirect connection in which another component exists in between. Furthermore, when a component is said to "include" or "have" another component, unless otherwise specifically stated, this does not exclude the other component, but rather implies that the other component may be included.

[0050] In this disclosure, terms such as first, second, etc. are used solely to distinguish one component from another, and do not limit the order or importance of components unless specifically stated otherwise. Accordingly, within the scope of this disclosure, a first component in one embodiment may be referred to as a second component in another embodiment, and similarly, a second component in one embodiment may be referred to as a first component in another embodiment.

[0051] In this disclosure, distinct components are used to clearly illustrate their respective characteristics, and do not necessarily imply that the components are separated. That is, multiple components may be integrated into a single hardware or software unit, or a single component may be distributed into multiple hardware or software units. Therefore, even if not specifically mentioned, such integrated or distributed embodiments are also included within the scope of this disclosure.

[0052] In the present disclosure, the components described in various embodiments are not necessarily essential components, and some may be optional components. Therefore, embodiments comprising a subset of the components described in one embodiment are also within the scope of the present disclosure. Furthermore, embodiments including other components in addition to the components described in various embodiments are also within the scope of the present disclosure.

[0053] The present disclosure describes a wireless communication network, and operations performed in the wireless communication network may be performed in a process of controlling the network and transmitting or receiving a signal in a system (e.g., a base station) that manages the wireless communication network, or in a process of transmitting or receiving a signal in a terminal connected to the wireless network.

[0054] It is self-evident that various operations performed for communication with terminals in a network consisting of multiple network nodes including a base station can be performed by the base station or other network nodes other than the base station. 'Base station (BS)' can be replaced by terms such as fixed station, Node B, eNodeB (eNB), ng-eNB, gNodeB (gNB), and access point (AP). In addition, 'terminal' can be replaced by terms such as UE (User Equipment), MS (Mobile Station), MSS (Mobile Subscriber Station), SS (Subscriber Station), and non-AP station (non-AP STA).

[0055] In the present disclosure, transmitting or receiving a channel means transmitting or receiving information or a signal through the channel. For example, transmitting a control channel means transmitting control information or a signal through the control channel. Similarly, transmitting a data channel means transmitting data information or a signal through the data channel.

[0056] In the following description, the term NR (New Radio) system is used for the purpose of distinguishing the system to which various examples of the present disclosure are applied from existing systems; however, the scope of the present disclosure is not limited by this term.

[0057] NR systems support a variety of subcarrier spacings (SCS) to accommodate diverse scenarios, service requirements, and potential system compatibility. Furthermore, NR systems can support the transmission of physical signals / channels across multiple beams to overcome challenging channel conditions, such as high path loss, phase noise, and frequency offsets that occur at high carrier frequencies. This enables NR systems to support applications such as enhanced Mobile Broadband (eMBB), massive Machine Type Communications (mMTC) / ultra Machine Type Communications (uMTC), and Ultra Reliable and Low Latency Communications (URLLC).

[0058] Hereinafter, 5G mobile communication technology can be defined to include not only the NR system, but also the existing LTE-A (Long Term Evolution-Advanced) system and LTE (Long Term Evolution) system. 5G mobile communication may include technology that operates in consideration of backward compatibility with previous systems as well as the newly defined NR system. Therefore, the 5G mobile communication below may include technology that operates based on the NR system and technology that operates based on previous systems (e.g., LTE-A, LTE), and is not limited to a specific system.

[0059] First, we would like to briefly explain the physical resource structure of the wireless communication system to which the present invention is applied.

[0060] FIG. 1 is a drawing for explaining an NR frame structure to which the present disclosure can be applied.

[0061] In NR, the basic unit of time domain is It can be, , and N can be 4096. Meanwhile, in LTE, the basic unit of the time domain is It can be, And, =2048. The constant for the multiplication relationship between the NR time base unit and the LTE time base unit is k= can be defined as

[0062] Referring to Figure 1, the time structure of a frame for downlink / uplink (DL / UL) transmission is can have. Here, one frame is It consists of 10 subframes corresponding to time. The number of consecutive OFDM symbols in each subframe is = It can be. In addition, each frame is divided into two half frames of the same size, half frame 1 can be composed of sub frames 0-4, and half frame 2 can be composed of sub frames 5-9.

[0063] represents the timing advance (TA) between the downlink (DL) and uplink (UL). Here, the transmission timing of the uplink transmission frame i is determined based on the downlink reception timing at the terminal, based on the following mathematical expression 1.

[0064] [Mathematical Formula 1]

[0065]

[0066]

[0067] Here, It may be a TA offset value that occurs due to differences in duplex mode, etc. In FDD (Frequency Division Duplex), has a value of 0, but in TDD (Time Division Duplex), it takes into account the margin for DL-UL switching time. It can be defined as a fixed value. For example, in TDD (Time Division Duplex) of FR1 (Frequency Range 1), which is a frequency below 6 GHz, is 39936 or 25600 It could be 39936 is 20.327μs, and 25600 is 13.030μs. Also, at FR2 (Frequency Range 2), which is a millimeter wave (mmWave) frequency, is 13792 It could be. At this time, 13792 is 7.020 μs.

[0068] FIG. 2 is a diagram showing an NR resource structure to which the present disclosure can be applied.

[0069] Resource elements (REs) within a resource grid can be indexed according to each subcarrier spacing. Here, one resource grid can be created for each antenna port and each subcarrier spacing. Uplink and downlink transmission and reception can be performed based on the corresponding resource grid.

[0070] In the frequency domain, one Resource Block (RB) consists of 12 REs, and each of the 12 REs can be configured with an index (nPRB) for one RB. The index for an RB can be utilized within a specific frequency band or system bandwidth. The index for an RB can be defined as in the following mathematical expression 2. Here, represents the number of subcarriers per RB, and k represents the subcarrier index.

[0071] [Equation 2]

[0072]

[0073]

[0074] Different numerologies can be configured to meet the diverse services and requirements of NR systems. For example, while LTE / LTE-A systems can support a single subcarrier spacing (SCS), NR systems can support multiple SCSs.

[0075] A new numerology for NR systems supporting multiple SCSs can operate in frequency ranges or carriers such as below 3GHz, 3GHz-6GHz, 6GHZ-52.6GHz or above 52.6GHz to address the issue of not being able to use wide bandwidth in frequency ranges or carriers such as 700MHz or 2GHz.

[0076] Table 1 below shows examples of numerologies supported by the NR system.

[0077] [Table 1]

[0078]

[0079]

[0080] Referring to Table 1 above, the numeral can be defined based on the subcarrier spacing (SCS), cyclic prefix (CP) length, and number of OFDM symbols per slot used in the Orthogonal Frequency Division Multiplexing (OFDM) system. The above values ​​can be provided to the terminal through the upper layer parameters DL-BWP-mu and DL-BWP-cp for the downlink, and through the upper layer parameters UL-BWP-mu and UL-BWP-cp for the uplink.

[0081] In Table 1 above, when the subcarrier spacing setting index (u) is 2, the subcarrier spacing (Δf) is 60 kHz, and normal CP and extended CP can be applied. For other numerology indices, only normal CP can be applied.

[0082] A normal slot can be defined as the basic time unit used to transmit a single piece of data and control information in an NR system. The length of a normal slot can be set to the number of OFDM symbols, which is 14 by default. Furthermore, unlike slots, a subframe has an absolute time length equivalent to 1 ms in an NR system and can be used as a reference time for the length of other time intervals. Here, for coexistence or backward compatibility between LTE and NR systems, a time interval similar to an LTE subframe may be required in the NR standard.

[0083] For example, in LTE, data can be transmitted based on a unit of time called a Transmission Time Interval (TTI), which can be set to one or more subframes. Here, one subframe can be set to 1ms and can contain 14 OFDM symbols (or 12 OFDM symbols).

[0084] In addition, a non-slot can be defined in NR. A non-slot can mean a slot having a number that is at least one symbol smaller than a normal slot. For example, in the case of providing low latency such as URLLC service, latency can be reduced through a non-slot having a number of symbols smaller than a normal slot. Here, the number of OFDM symbols included in a non-slot can be determined by considering the frequency range. For example, a non-slot with a length of 1 OFDM symbol can be considered in a frequency range of 6 GHz or higher. As an additional example, the number of OFDM symbols defining a non-slot can include at least 2 OFDM symbols. Here, the range of the number of OFDM symbols included in a non-slot can be set as the length of a mini-slot up to a predetermined length (e.g., the normal slot length - 1). However, as a specification of a non-slot, the number of OFDM symbols may be limited to 2, 4, or 7 symbols, but is not limited thereto.

[0085] Additionally, for example, in unlicensed bands below 6 GHz, subcarrier spacing where u equals 1 and 2 may be used, and in unlicensed bands above 6 GHz, subcarrier spacing where u equals 3 and 4 may be used. For example, when u equals 4, it may be used for SSB (Synchronization Signal Block).

[0086] [Table 2]

[0087]

[0088]

[0089] Table 2 shows the number of OFDM symbols per slot for normal CP, by subcarrier spacing setting (u). ), number of slots per frame ( ), number of slots per subframe ( ) is shown. Table 2 shows the above-described values ​​based on a normal slot having 14 OFDM symbols.

[0090] [Table 3]

[0091]

[0092]

[0093] Table 3 shows the number of slots per frame and the number of slots per subframe based on normal slots with 12 OFDM symbols per slot when extended CP is applied (i.e., when u is 2 and the subcarrier spacing is 60 kHz).

[0094] As mentioned above, one subframe may correspond to 1ms on the time axis. Additionally, one slot may correspond to 14 symbols on the time axis. For example, one slot may correspond to 7 symbols on the time axis. Accordingly, the number of slots and symbols that can be considered within 10ms corresponding to one radio frame may be set differently. Table 4 may show the number of slots and symbols according to each SCS. In Table 4, the 480kHz SCS may not be considered, but is not limited to these examples.

[0095] [Table 4]

[0096]

[0097]

[0098] A terminal can calculate downlink CSI parameters (e.g., CQI, PMI, RI, L1-RSRP, etc.) through a DL (downlink) CSI (channel state information) reporting procedure and report the calculated CSI information to a base station. The base station can utilize the received CSI information for downlink data transmission. The CSI parameters are measurement values ​​associated with a channel state, and the base station can perform downlink data transmission based on the measurement values. The base station can instruct the terminal about data scheduling information based on the CSI parameters. The terminal generally reports the CSI calculation and derived CSI parameters to the base station as feedback information by receiving a CSI-RS transmitted by the base station. Alternatively, the terminal can measure the channel state through another downlink signal (e.g., SSB (synchronization signal block)) in addition to the CSI-RS and report the measured value to the base station.

[0099]

[0100] Select RI (rank indicator):

[0101] RI can be a CSI parameter that indicates the number of layers available for downlink transmission in a specific channel environment. RI can also indicate the maximum number of uncorrelated paths available for downlink transmission. Here, other CSI parameters (e.g., PMI, CQI) can be calculated based on the rank provided by RI.

[0102]

[0103] Select precoding matrix index (PMI):

[0104] A PMI may include a set of indices corresponding to a precoding matrix. The base station may apply the PMI reported by the terminal to downlink transmission. However, the base station may have the freedom to apply a precoding matrix other than the PMI. For example, the standard may support multiple types of codebooks to report the precoding matrix for the PMI. Each codebook type is described below.

[0105] Codebook type

[0106] - Type 1 single-panel codebooks

[0107] - Type 1 multi-panel codebooks

[0108] - Type 2 codebooks

[0109] - Enhanced Type 2 codebooks

[0110]

[0111] For example, PMI selection may be performed based on the codebook type, the number of transmission layers represented by RI values, and other CSI reporting configuration parameters (e.g., antenna panel dimensions). Each codebook may include a set of precoding matrices. For example, in NR, a dual-stage precoding matrix codebook design may be applied, but is not limited thereto.

[0112] The dual-stage precoding matrix may be as shown in Equation 3 below. In Equation 3, W1 may be a matrix representing a DFT (discrete Fourier transform) beam or a beam group for both polarizations according to the codebook type, and W2 may be selected from one of the following according to the codebook type.

[0113] W2 codebook type

[0114] - Beam selection from W1

[0115] - Weighting coefficients for the beams in W1

[0116] - Cophasing values ​​between two polarizations

[0117]

[0118] [Equation 3]

[0119]

[0120]

[0121] The terminal calculates a PMI value that represents the highest SINR (signal interference noise ratio) value at the receiver using the codebook selected for "Type I single-panel codebook" and "Type I multi-panel codebook" as the codebook type and all possible precoding matrices in the given channel environment. For example, a Type 1 codebook may be considered for PMI selection in a single user multi-input multi-output (SU-MIMO) scenario, but is not limited thereto.

[0122] A Type II codebook can consider a set of orthogonal DFT beams. In Equation 3, the W1 matrix contains information associated with the DFT beams, and the PMI selection function calculates beam amplitude scaling and cophasing values ​​(W2) for all DFT beams within each orthogonal beam group for a given channel environment and number of DFT beams. The terminal can report the i1 and i2 values ​​to the base station based on the PMI selection function, and each index corresponds to a precoding matrix (W) that provides the maximum SINR value. A Type II codebook-based PMI can provide more accurate channel information than a Type I codebook-based PMI. A Type II codebook can provide more accurate channel information because multiple beams are applied to estimate the channel eigenvector. For example, a Type II codebook-based PMI can be used in a MU (multi)-MIMO scenario because it considers multiple beams, but is not limited thereto. For example, a Type 2 codebook can only support up to Rank 2, while an enhanced Type 2 codebook can support up to Rank 4 with overhead reduction methods, which will be described later.

[0123] Furthermore, based on the above, CSI reporting performed by a terminal may consider explicit CSI feedback and implicit CSI feedback. Explicit CSI feedback is a method in which the terminal directly feeds back channel information (e.g., channel eigenvector / eigenvalue, SVD) indicating the channel state measured by the terminal, and may not consider how the base station processes the reported CSI. On the other hand, implicit CSI feedback may be a method in which a selected precoder matrix (desired precoder matrix) W within a set of candidate precoder matrices from a possible precoder codebook is indicated.

[0124] For example, CSI parameters (or CSI contents) related to CSI feedback may be as shown in Table 5 below, but may not be limited thereto.

[0125] [Table 5]

[0126]

[0127]

[0128] The CSI content within a CSI report can be set by different combinations of the CSI parameters in Table 5. Here, the CSI content within a CSI report can be set by reportQuantity.

[0129] For example, if set to "none", the base station may trigger aperiodic CSI-RS transmission. Aperiodic CSI-RS transmission may be triggered by the terminal for channel measurement and receiver parameter adjustment, and related CSI reporting may not be performed by the terminal. Aperiodic CSI-RS transmission may be, but is not limited to, aperiodic transmission of CSI-RS or TRS (tracking reference signal) for tracking purposes for synchronization purposes, or spatial aperiodic receive beam sweeping for the terminal to adjust the receive filter. The following operations do not require the base station to report aperiodic CSI to the terminal, but may operate within the same framework as aperiodic CSI requests.

[0130] - Aperiodic transmission of the Tracking Reference Signal (TRS or CSI-RS for tracking)

[0131] - Aperiodic Rx beam sweeping (so called P3 beam sweep) - adjusts the Rx spatial received filter to the UE

[0132]

[0133] Additionally, CSI contents within a single CSI report may be set as shown in Table 6 below, but may not be limited thereto. For example, the combinations of CSI contents in Table 6 below are representative combinations of CSI contents that a base station will primarily need.

[0134] [Table 6]

[0135]

[0136]

[0137] For example, “cri-RI-i1” in Table 6 can operate based on “Hybrid beamformed / non-precoded CSI acquisition operation” as shown in FIG. 3.

[0138] The base station can mainly use UE-specific beamformed CSI-RS (320) with several ports per CSI-RS resource for the purpose of CSI acquisition. However, in order for the base station to recognize how to beamform the CSI-RS for each UE, non-precoded CSI-RS resources (310) can be transmitted in the middle together with multiple antenna ports (e.g., 32 ports). Here, the UE can report PMI corresponding to all antenna ports (e.g., 32 ports) based on the received CSI-RS, thereby indicating a preferred beam direction. For example, beamforming of the CSI-RS can be based on the wideband / long-term part of the reported precoder. That is, the reported W1 metrics can be used to perform UE-specific CSI-RS beamforming. Additionally, the short-term subband CS (short-term / subband CSI) (W2) can be determined from the beamforming CSI-RS.

[0139] For example, non-precoded CSI-RS (310) can be used to recognize how CSI-RS beamforming is performed, and based on this, the terminal may only need to report CSI corresponding to W1 and not report for W2. This can reduce overhead.

[0140] Additionally, the terminal can select a PMI based on a specific number of transmit antennas. Specifically, the terminal can select a PMI based on a specific number of transmit antennas given by the number of antenna ports of a configured CSI-RS associated with the reporting configuration.

[0141] Additionally, in the case of MU-MIMO scheduling, the base station may select a PMI other than the PMI reported by the target terminal, taking into account interference or channel conditions from other terminals simultaneously scheduled. Considering the above, two types of CSI can be defined in a wireless communication system, as shown in Table 7 below.

[0142] [Table 7]

[0143]

[0144]

[0145] Specifically, type 1 CSI may have different sub-codebook formats based on different antenna configurations at the transmitter. As an example, FIG. 4 is a diagram illustrating type 1 CSI applicable to the present disclosure. Specifically, FIG. 4(a) may be a case where 16 antenna ports are configured with (N1, N2) = (4, 2). The precoding matrix W for type 1 single panel CSI may be expressed as the product of two matrices (W1, W2), each of which may be independently reported as a different part of the overall PMI. For example, W1 may reflect long-term frequency independent channel characteristics, and thus may report channel information corresponding to the entire bandwidth (Wideband reporting). For example, W1 may be as shown in Equation 4 below. W1 may indicate a set of beams pointing in different directions, and matrix B may define one beam. In mathematical expression 4, based on the 2 X 2 block structure, there can be two polarizations, and the selection of W1 can be a limited set selection with the same beam direction.

[0146] [Equation 4]

[0147]

[0148]

[0149] As a concrete example, for rank 1 or 2, a single beam or four neighboring beams can be indicated by the W1 matrix. The correct beam among the four neighboring beams can be selected by W2 reported for each subband, thereby selecting an optimized beam for each subband. Furthermore, W2 can provide cophasing between two polarizations. If W1 defines a single beam, then W2 in B, which is a single-column matrix, can only provide cophasing between two polarizations. On the other hand, if multiple neighboring beams are defined by W1, W2 can select one correct beam and provide cophasing between two polarizations within that beam.

[0150] On the other hand, if the rank is greater than 2, W1 can define N neighboring orthogonal beams, which can be as shown in Equation 5. In Equation 5, N beams used for transmission of R transmission layers can be indicated, and W2 can only provide cophasing between two polarizations. Here, transmission to the same terminal can be possible for up to 8 transmission layers.

[0151] [Equation 5]

[0152]

[0153]

[0154] On the other hand, W2 is short-term and can potentially indicate different channel characteristics depending on the frequency. W2 may not be reported in certain cases. Here, the base station can randomly select W2 for each physical resource block group (PRG), and the terminal can select the channel quality indicator (CQI) based on this assumption.

[0155] Also, Fig. 4(b) may be a Type 1 multi-panel CSI. Multi-panel CSI may be applied considering a case where multiple antenna panels are used at a base station. Here, coherence between different panels may be difficult to guarantee. For example, Fig. 4(b) may be a case where (N1, N2) = (4, 1) and there are 32 antenna ports by 4 panels, but the present invention is not limited thereto. Here, the difference between the single panel of Fig. 4(a) and the multi-panel of Fig. 4(b) is that coherence may be difficult to guarantee between the antenna ports of different antenna panels. In the multi-panel, W1 may be the same set of beams for different polarizations and panels, just like in the single panel. On the other hand, in the case of the multi-panel, W2 may provide cophasing not only between polarizations but also between multi-panels for each subband.

[0156] Type 2 CSI can provide channel information with higher spatial granularity than Type 1 CSI and can be primarily applied to MU-MIMO scenarios. For example, Type 2 CSI may be limited to a maximum of two ranks, while Enhanced Type 2 CSI may be extended to four ranks, but may not be limited to a specific form. For convenience of explanation, the terms Type 2 CSI and Enhanced Type 2 CSI are referred to below, but may not be limited thereto.

[0157] Here, in the above-described mathematical expressions 3 and 4, W1 may be a wideband report. Type 2 CSI may report up to four beams, corresponding to four columns within B. W2 may provide amplitude values ​​(partially wideband and partially subband reporting) and phase values ​​(subband reporting) for each of the four reported beams and two polarizations within B.

[0158] Type 2 CSI can provide more detailed channel model information than Type 1, including main ray and amplitude and phase information. Based on the CSI reported from multiple terminals, the base station can determine which combinations of terminals are appropriate for simultaneous transmission on the same time / frequency resource.

[0159] Additionally, enhanced Type 2 CSI (e.g., Rel-16 enhanced Type 2 CSI) may be considered. Enhanced Type 2 CSI can report a set of beams on the wideband, similar to Type 2 CSI, along with a set of combining coefficients on an additional narrowband. Here, the reported beams can be linearly combined using the combining coefficients to provide a set of precoder vectors for each transmission layer. For example, in the existing Type 2 CSI, the combining coefficient value can be reported independently for each subband. Therefore, even considering the fact that the channels between adjacent subbands are highly correlated, the existing Type 2 CSI must report a relatively large combining coefficient value on a per-subband basis, which can increase the overhead. Enhanced Type 2 CSI can utilize frequency correlation to reduce this overhead. Furthermore, enhanced Type 2 CSI can improve the frequency-domain granularity of the PMI report. For example, compression operations can be applied to subbands or half-subbands based on frequency domain units. While conventional Type 2 CSI uses one precoder per subband, enhanced Type 2 CSI can use a recommended precoder for each frequency domain unit. However, actual reporting can be performed together for all frequency units. Specifically, given layer k, the reported precoders for all frequency domain units can be expressed as in Equation 6 below, where N can be the number of frequency domain units.

[0160] [Equation 6]

[0161]

[0162]

[0163] Here, Equation 6 may not be a mapping of layers and antennas, but may mean a set of precoder vectors for the entire set of frequency domain units for layer k. For the entire k layers, there may be a set of k precoder vectors, each set may consist of N precoder vectors, and the layer and antenna port mapping for a specific frequency domain unit n may be as shown in Equation 6 below.

[0164] Additionally, in the expression for vectors in Equation 7, W1 can be the same as Equation 4 described above, which is the same as the existing Type 2 CSI.

[0165] [Equation 7]

[0166]

[0167] Here, the B columns can correspond to the L selected beams, and W1 can be the same for all frequency domain units. (wideband reporting) Also, W1 can be the same for all layers.

[0168] Additionally, as an example, the enhanced Type 2 CSI has a compression matrix (size M x N) can be considered. can be constructed as a set of row vectors from the DFT basis, and can provide a transformation from N dimensions in the frequency domain (N frequency domain units) to M dimensions in the smaller delay domain. can be frequency independent, but can be reported independently for each layer. That is, can be a single common metric for all frequency domain units, but can be reported independently for each layer. The number of rows can be equal to M in Equation 8. In Equation 8, R can be the number of frequency domain units per subframe (R=1 or 2), and p can be a configurable parameter that controls the amount of compression.

[0169] [Equation 8]

[0170]

[0171] Finally (2L x M) can perform mapping from the delay domain to the beam domain. For example, W2 of the existing Type 2 CSI can generate a similar matrix for all subbands, and the matrix can be mapped from the frequency (subband) domain to the beam domain. On the other hand, can perform allocation from a smaller delay domain to a beam domain. can be adjusted by the reported rank and compression factor parameters p, which allows reporting based on fewer parameters. In addition, You can adjust the amount of CSI to be reported through the value, which allows you to get a smaller Fewer parameters can be reported based on size.

[0172] Additionally, as an example, the enhanced Type 2 CSI may be expanded to a maximum of 4 reported ranks and a maximum number of selectable beams to 6, with reduced overhead and improved frequency domain granularity. Table 8 below may illustrate possible configurations of the enhanced Type 2 CSI, but is not limited thereto.

[0173] [Table 8]

[0174]

[0175]

[0176] Additionally, multi-antenna precoding can be considered in uplink transmission. PUSCH (physical uplink shared channel) MIMO precoding can support up to four layers. For uplink transmission, DFT-based precoding can only support single-user (SU) MIMO. For example, a terminal can perform codebook-based transmission and non-codebook-based transmission with two different PUSCH precoding modes. In case of codebook-based precoding, the uplink grant transmitted by the base station can include precoder-related information (e.g., PMI). It is assumed that the terminal uses the precoder provided by the base station in the uplink differently from the downlink transmission. Furthermore, coherence can be assumed between the terminal antennas in uplink MIMO transmission, and the phase of the signals transmitted on the two antennas can be adjusted. Here, if there is no coherence between the antenna ports, each antenna port may result in a random relative phase, which may mean that the use of antenna port specific weight factors is limited.

[0177] For example, the coherence between antenna ports can correspond to any one of full coherence, partial coherence, and no coherence, and the corresponding information can be provided as terminal capability information. In addition, the use of multiple sounding reference signals (SRS) for codebook-based PUSCH can be considered. FIG. 5 is a diagram illustrating a method of using multiple SRSs applicable to the present disclosure. Referring to FIG. 5, a terminal can transmit relatively large beams on a multi-port SRS. For example, the beams can correspond to different terminal antenna panels (in different directions), and each panel can include a set of antenna elements. That is, each panel can correspond to antenna ports of a multi-port SRS. The base station can transmit an SRI (SRS Resource Indicator) to the terminal to determine whether to perform transmission through which beam. The terminal can determine which transmission to perform within the selected beam based on the number of layers and precoder using SRI and precoder information.

[0178] FIG. 6 is a diagram illustrating a method for performing non-codebook-based precoding applicable to the present disclosure. Referring to FIG. 6, when performing non-codebook-based precoding, a terminal can perform transmission based on a precoder indication and channel measurement received from a base station. In codebook-based precoding, the terminal performs transmission based on an uplink precoder selected based on channel measurement by the base station, but in non-codebook, uplink transmission can be performed through a precoder selected based on measurement based on channel reciprocity. Here, each column of the precoder matrix W can be a digital beam for a corresponding layer. When the terminal selects a precoder for N layers, it can be regarded as selecting N different beam directions, and each beam can correspond to one possible layer.

[0179] For example, referring to FIG. 6, a terminal may be capable of PUSCH transmission based on the selected precoding. However, the precoder selected by the terminal based on downlink channel measurements may not be the optimal precoder from the base station's perspective. Considering the above, the base station may modify the precoder selected by the terminal. For example, the base station may remove some beams or some columns selected by the terminal from the precoder and transmit information about this to the terminal. Specifically, the base station may indicate the result of beam selection based on measured SRS information back to the terminal through SRI. Based on the indicated information, the terminal may perform only some beam transmissions, which may implicitly indicate reduced layer transmission.

[0180]

[0181] A terminal can transmit uplink control information (UCI) to a base station. For example, the terminal can simultaneously transmit UCI on a physical uplink control channel (PUCCH) and a PUSCH. Specifically, the terminal can be instructed with a "simultaneous PUCCH and PUSCH transmission" parameter through upper layer signaling or dynamic signaling, and can configure simultaneous PUCCH and PUSCH transmission for UCI. That is, the terminal can be configured to perform simultaneous transmission for PUCCH and PUSCH. Here, if PUCCH and PUSCH are transmitted on the same carrier, "non-single carrier waveform transmission" may occur. Accordingly, PUCCH and PUSCH can be transmitted based on a large transmission backoff (e.g., 10 dB) based on frequency location resource allocation size, transmission power, and other factors. On the other hand, when PUCCH and PUSCH are transmitted on different carriers, a large power backoff may or may not be required based on a single power amplitude (PA) or multiple PAs.

[0182] As another example, if simultaneous transmission on PUCCH and PUSCH is required, the UE may drop the PUCCH and transmit UCI on the PUSCH. For example, the UCI type piggybacked on the PUSCH may be at least one of HARQ (hybrid automatic repeat and request), CSI type 1, and CSI type 2. On the other hand, the SR (scheduling request) may not be piggybacked on the PUSCH and may be replaced with a BSR (buffer state report), but is not limited thereto.

[0183] Upper layer parameter beta The value is used to determine the amount of radio resources transmitted on the PUSCH, and this value can be indicated to the UE from the base station. The beta value can have a wide range based on the amount of radio resources. When the UE transmits UCI on the PUSCH, the UE can determine the amount of radio resources required for UCI transmission by considering the UCI payload size on the PUSCH and the PUSCH spectral efficiency along with the above-mentioned beta value. This can prevent unnecessary resource usage for UCI, and the corresponding operation can be controlled by upper-layer parameters.

[0184] Additionally, as an example, the size of the UCI information bits may vary depending on the UCI type, and thus different UCI handling methods may be considered. Consider the cases in Table 9 below.

[0185] [Table 9]

[0186]

[0187]

[0188]

[0189] [Table 10]

[0190]

[0191]

[0192] For example, in case of PUCCH-based subband CSI reporting and PUSCH-based reports with Type 1 CSI feedback, CSI Part 1 may include RI (if reported) and CSI-RS Resource Indicator (CRI) (if reported). In addition, CSI Part 1 may include CQI for the first codeword (first CW). CSI Part 2 may include CQI and PMI for the second CW when RI is greater than 4.

[0193] CSI Part 1 may additionally include an indication of the number of "non-zero wideband amplitude coefficients per layer" for Type 2 CSI feedback over PUSCH. Here, the "wideband amplitude coefficient" is part of Type 2 CB, and the PMI payload size may vary depending on whether the coefficient value is zero or not.

[0194] Additionally, CSI Part 1 and CSI Part 2 can each be set based on Table 11 and Table 12.

[0195] [Table 11]

[0196]

[0197]

[0198] [Table 12]

[0199]

[0200]

[0201] Additionally, diverse uplink data scheduling may be required. The beta value can be dynamically indicated to the UE, taking into account the potentially different QoS requirements for each UCI and UCI of the PUSCH. For example, the base station can indicate one of four different beta values ​​pre-configured through higher layers via downlink control information (DCI).

[0202] For example, when allocating UCI on PUSCH, HARQ may have the highest priority, followed by CSI Type 1 and CSI Type 2. The priority may be determined based on whether the UCI is mapped closely to the DMRS on the PUSCH on which it is transmitted.

[0203] Here, in case of UCI 1 or 2-bit HARQ, some REs can be reserved for puncturing by pretending to be 2-bit HARQ bits, and the corresponding information can be transmitted through puncturing. In addition, in case of UCI greater than 2-bit HARQ and CSI Part 1 and CSI Part 2, rate matching can be performed starting from the available REs from the right non-DMRS symbol after the first DMRS symbol, and HARQ can be performed first, followed by CSI Part 1 and CSI Part 2.

[0204] Additionally, for example, CSI Part 1 may not be allocated to REs reserved for HARQ to avoid being punctured by HARQ. On the other hand, CSI Part 2 and uplink data may be allocated to REs reserved for HARQ (i.e., potentially entailing puncturing).

[0205] UCI on PUSCH can be allocated in frequency order and then in time order. That is, UCI on PUSCH can be allocated starting from the lowest available RE of the lowest available symbol. As described above, the base station can decode UCI quickly. In order to maximize the frequency diversity gain for UCI on PUSCH within one uplink symbol, REs for UCI can be allocated in a 'frequency-distributed' manner, but is not limited thereto. Specifically, the distance (d) between two adjacent REs within one orthogonal frequency division multiplexing (OFDM) symbol to which UCI is allocated can be set to 1 (d=1) when the number of modulation symbols to be allocated for UCI is greater than or equal to the number of REs available within the uplink symbol.

[0206] Otherwise, d can be determined by floor(number of available REs in an uplink symbol / number of modulation symbols to be allocated for UCI). Here, d>1 for a UL symbol, and the allocation of UCI modulation symbols in that symbol can be frequency distributed. In addition, all UCI types can be allocated to all layers of PUSCH transmission and use the same PUSCH modulation. For example, CSI can be allocated only to the layer with the highest modulation coding scheme (MCS), but is not limited thereto.

[0207] For example, in UL MIMO transmission, 2 TBs are used in LTE, but in 5G NR, 4-layer UL MIMO transmissions are used for one TB, so CSI can be allocated only to the layer with the highest MCS.

[0208] Additionally, when frequency hopping is enabled on the PUSCH, UCI modulation symbols can be divided into two parts of approximately equal size. These two parts can be assigned to each of the two hops, and can be divided into approximately equal parts. Accordingly, the negative impact on uplink data due to UCI piggybacking on the PUSCH can be equally distributed across the two hops.

[0209]

[0210] Additionally, partial CSI omission can be considered in PUSCH-based CSI reporting. In PUSCH-based CSI reporting and Type 2 CSI reporting, the CSI payload size can dynamically change depending on the Rank Indicator (RI) selection. For example, when Type 2 CSI reporting is performed, the PMI payload when RI is 2 can be approximately twice as large as the PMI payload when RI is 1. Since the base station cannot recognize the RI selection in advance, it can perform scheduling by predicting the RI value that the UE is likely to select when performing PUSCH scheduling. Therefore, a misalignment due to RI selection may occur between the base station and the UE. If the RI selected by the UE is larger than the RI value expected by the base station, the CSI payload size may increase, which may prevent it from being included on the PUSCH. In other words, the code rate may be too high or uncoded systematic bits may not be included. In the above-described case, instead of dropping the CSI report, the terminal can report some high-priority CSI information to the base station via PUSCH through partial CSI omission.

[0211] As an example, FIG. 7 is a diagram illustrating CSI omission applicable to the present disclosure. Referring to FIG. 7, some (710) of the CSI information may be included in the report, and the remaining part (720) may be omitted from the report. As an example, the terminal may perform ordering of CSI contents within CSI Part 2. When multiple CSI reports are transmitted on the PUSCH, wideband CSI components (i.e., wideband PMI and CQI) in all CSI reports are allocated to the most significant bit (MSB) of the UCI, and subband CSI (each CSI report) may be allocated in the manner of "even SB CSI - odd SB CSI - even SB CSI", which may be as shown in FIG. 7.

[0212] For example, if the UCI code rate exceeds a threshold, some of the UCI least significant bit (LSB) bits may be omitted. Here, the omission (720) may be performed until the code rate falls below the threshold, which may be as shown in FIG. 7. As an example, in FIG. 7, "SB CSI for odd numbered" may be omitted first, but the present invention may not be limited thereto.

[0213] For example, a base station can interpolate PMI / CQI by estimating missing PMI / CQI values ​​in the frequency domain. That is, omission can be performed based on the above-described order so that the base station can estimate the maximum interpolation for some of the omitted SB CSIs. This can maintain higher performance than omitting all consecutive SB CSIs.

[0214]

[0215] For example, AI / ML (artificial intelligence / machine learning)-based operations may be performed below. The terms in Table 13 below may be used within the AI / ML-based framework, but may not be limited thereto.

[0216] [Table 13]

[0217]

[0218]

[0219]

[0220]

[0221] For example, deep learning, a subcategory of machine learning (ML), focuses on parameterizing multi-layer neural networks. Deep learning can be a method for learning data representation and has demonstrated high performance in image classification, speech recognition, and natural language processing. However, deep learning can require a large amount of data during the training process, and the computational complexity required to train deep learning-based models on large data sets can be high, necessitating high hardware performance.

[0222] Regarding AI / ML, AI / ML-based mobile communications can be implemented. For example, AI has been able to reduce CAPEX and OPEX costs by managing data from devices operating on existing wireless communication systems. Furthermore, the introduction of next-generation networks may require low operating costs and high return-to-cost compensation, and AI can help address the complexity and optimization challenges of network operations. Furthermore, network intelligence and automation may be critical issues in next-generation mobile communication systems, and these challenges can be addressed using AI / ML. For example, budget management, network management, equipment lifecycle management, service level agreement (SLA) management, network performance management, and network planning can be implemented using AI / ML. AI / ML can enhance the Quality of Experience (QoE) of network operation and usage, but is not limited to this. Furthermore, AI / ML can improve network quality and enhance the provision of more personalized services, but is not limited to a specific form.

[0223]

[0224] For example, AI / ML-based operations may consider two types of operations: model generation and inference operations. Model generation may be performed based on model training, model validation, model testing, and application stages. For example, model training may be performed based on at least one of input / output, pre-processing / post-processing, and online / offline application, but may not be limited thereto.

[0225] In addition, inference operations can be performed based on input / output, pre / post-processing, and application stages. A general AI / ML framework and an AI / ML model life cycle management (LCM) procedure can be performed based on the following steps. As an example, FIG. 8 is a diagram illustrating an AI / ML model life cycle management applicable to the present disclosure. Referring to FIG. 8, data collection for an AI / ML model can be performed. Here, the collected data can include data for model training, monitoring data for model management and performance monitoring, and inference data for model inference. Model training can perform model learning based on training data, and the learned and updated model can be stored. Here, the model can be stored based on a function or model identifier or an identifier related to the model and additional information, but is not limited to a specific form. In addition, model training can be performed based on model training control information based on model management and performance monitoring.

[0226] Model management and performance monitoring can control model inference based on monitoring data. Model management and performance monitoring can control the activation / deactivation / selection / switching / fallback of model inference, as well as other operations. Model management and performance monitoring can obtain information about the results of model inference. Model inference can obtain inference data, perform inference operations, and obtain result information. Model inference can receive models stored in storage and perform inference operations based on them.

[0227] In relation to the LCM (life cycle management) process, one AI / ML model may have one model ID and related information and / or model functions for AI / ML operations, as described above.

[0228] Actions within the LCM procedure

[0229] - Data Collection

[0230] - Model Training

[0231] - Functionality / model identification

[0232] - Model transfer

[0233] - Model Inference

[0234] - Functionality / model selection, activation, deactivation, switching and fallback operation

[0235] - Functionality / model monitoring

[0236] - Model update

[0237] - Model Storage

[0238] - UE capability

[0239]

[0240] For example, AI / ML-based operations in wireless communication systems can consider the following cases. However, this is only one example, and AIML-based operations can also be applied to other cases.

[0241] AI / ML-based operation of wireless communication systems

[0242] CSI (Channel State Information): compression (**), temporal prediction (*)

[0243] BM(Beam Management): spatial prediction (*), temporal prediction (*)

[0244] Positioning: direct (*), assisted (*)

[0245] (**) is a two-side model (*) is a single-side model

[0246]

[0247] As a concrete example, one can consider AI / ML model training collaborations for CSI compression using a two-sided model.

[0248] For example, with respect to CSI compression, AI / ML model inference can be performed on both the terminal and the base station. That is, AI / ML model inference can be performed on the two-side. To this end, a CSI generation part can be configured on the terminal, and a CSI reconstruction part corresponding to the CSI generation part can be configured on the base station. The CSI generation part is a part for CSI compression, and the CSI reconstruction part can be used to obtain (recover) more accurate CSI for better MU (multi-user) scheduling operation in a massive MIMO scenario, but is not limited thereto, and cooperation on the two-side AI / ML model can be performed in various forms.

[0249] As another example, considering the limitations of the terminal's capability or computing power, it may be considered that a third entity (e.g., a server for the terminal) performs AI / ML model training or inference operations. That is, the terminal-side operations (e.g., model training / inference / monitoring / switching / activation / deactivation / validation / testing) in the above-described AI / ML operations may include the operations of other entities and are not limited to a specific form.

[0250] For example, training collaboration can consider Types 1 through 3 below. In the following, all AI / ML model-related operations performed at the base station or network are assumed to be performed at the network level. However, this may not be limited. Since the "network" is an inclusive term that includes the base station, the two terms may be used interchangeably. What is performed at the network can encompass both the base station side and the broader network side. For convenience of explanation, the following description will be described as being performed at the network.

[0251]

[0252] For example, joint training can be performed based on Type 1. Type 1 can be a method in which training for an AI / ML model is performed on one side / entity. Other sides / entities can then obtain specific model parts through downloading / uploading, and CSI feedback operations can be performed based on these.

[0253] As a specific example, FIG. 9 is a diagram illustrating a method for performing AI / ML model training based on Type 1. Referring to FIG. 9, model training for the CSI generation part of the terminal and the CSI reconstruction part of the base station can be performed on the network (920) side in a joint training manner. Thereafter, the network side can provide the trained model information to the terminal (910) through downloading and can also apply it to the network (920). As another example, model training for the CSI generation part of the terminal and the CSI reconstruction part of the base station can be performed on the terminal (910) side in a joint training manner. Thereafter, the terminal side can apply the trained model information to the terminal (910) and provide it to the network (920) through an uplink. In other words, an AI / ML model can be trained on one side / entity, and the trained AI / ML model can be transferred to another side / entity.

[0254] As a specific example, network-side model 1 training can train the CSI generation part using a data set related to CSI generation for training purposes in the network, and train the CSI reconstruction part by transferring the result value into the training loop of the CSI reconstruction part. Here, the CSI reconstruction part located in the base station can be utilized for subsequent AI / ML model-based CSI feedback. On the other hand, the CSI generation part (i.e., CSI encoder) that performed model training by the base station can transfer the trained model information to the terminal via the wireless interface (UE download AI / ML model). The terminal can perform the AI / ML model-based CSI feedback operation using the downloaded CSI generation part model. That is, CSI compression can be performed through the CSI generation part to perform CSI feedback.

[0255] On the other hand, when the AI / ML model is trained on the terminal side, terminal-side Type 1 training can train the CSI generation part using a data set of CSI generation for training purposes at the terminal. Then, the output value can be provided to the training loop of the CSI reconstruction part, and the CSI reconstruction part can be trained based on the output value. For example, the CSI generation part located at the terminal can be used to generate CSI feedback information based on the AI / ML model. The CSI reconstruction part (i.e., the CSI decoder) that performed the model training by the base station can transmit the trained model information to the base station via the wireless interface (UE upload AI / ML model). Thereafter, the base station can perform the AI / ML model-based CSI feedback operation using the CSI reconstruction part model received through the terminal upload. That is, CSI feedback can be performed by recovering CSI feedback information through the CSI reconstruction part.

[0256] Additionally, AI / ML model joint training type 1 may require AI / ML model exchange. For example, AI / ML model inference may require a common reference for model inference and a common AI / ML model inference algorithm, which may require AL / ML model exchange. For example, terminal-specific joint training type 1 may perform AI / ML model transfer based on the open format of the AI / ML model structure recognized by the terminal. On the other hand, non-terminal-specific joint training type 1 may mean model transfer within an open format without the terminal being aware of the AI / ML model structure. For example, when training a terminal-side model on the network side, the terminal-specific model may be trained on the network side based on the terminal capabilities and the terminal model structure, but may not be limited to a specific form.

[0257]

[0258] As another example, FIG. 10 is a diagram illustrating a method for performing AI / ML model training based on Type 2 applicable to the present disclosure. Referring to FIG. 10 , Type 2 model training may be identical to Type 1 in that both the CSI generation part and the CSI reconstruction part are performed within a single, identical loop. However, Type 2 model training differs from Type 1 in that training is not performed on a specific side / entity, but on both sides / entities. That is, both the network and the terminal may be involved in the training.

[0259] For example, referring to FIG. 10, the terminal side can perform a forward propagation (FP) operation based on a data sample. That is, the terminal (1010) can generate a CSI feedback result and transmit the FP result as information thereon to the network side. Thereafter, the network (1020) can perform a CSI reconstruction operation based on the FP result, thereby training the CSI reconstruction part. The network side can generate BP (backward propagation) information (e.g., gradients) based on the above-described method and transmit the generated BP information back to the terminal side. The terminal side can perform training for the CSI generation part based on the BP information received from the network side. Here, the data set can be aligned on both the terminal side and the network side, and training can be performed through the above-described method.

[0260] Also, as an example, FIG. 11 is a diagram illustrating a method for performing AI / ML model training based on Type 3 applicable to the present disclosure. Referring to FIG. 11, Type 3 model training is a sequential training method, and unlike the joint training of Types 1 and 2, model training can be performed sequentially / independently for each node. That is, Type 3 model training can be performed based on the exchange of aligned data set information between a branch station and a terminal within a separate loop, without training the CSI generation part and the CSI reconstruction part within the same loop. For example, Type 3 model training can be distinguished into network-first training or terminal-first training, similar to Type 1 model training.

[0261] Referring to Figure 11, in the case of network-first training, the network may use the CSI generation part for training the CSI reconstruction model set on the network side. However, the CSI generation part is for training purposes and may not be used in subsequent model inference.

[0262] The network (1020) side inputs the CSI generation part's input values ​​( ) based on the corresponding CSI generation part and its output value ( ) and the final output value ( through model training of the CSI reconstruction part based on this ) can be generated. The CSI reconstruction part for which training is completed can be used for the AI / ML-based CSI feedback operation of the network. Thereafter, the network (1120) side can share the data set information (e.g., dataset including labels and intermediate results) utilized in the model training of the network side with the terminal after the model training is completed. The terminal (1110) side can apply the data set information to the CSI generation part training. Thereafter, a joint inference operation can be performed through the CSI generation part performed on the terminal (1110) side and the CSI reconstruction part performed on the network (1120). In addition, information on the CSI generation part trained on the terminal (1110) side (e.g., model / functionality identification process - information on available CSI generation parts) can be reported or transmitted to the network (1120). The network (1120) can select or optimize a CSI reconstruction part corresponding to the CSI generation part model of the terminal based on information about the CSI generation part obtained from the terminal (1110).

[0263] As an example, options for training a terminal-side CSI generation model for network-first type 3 joint training can be considered as shown in Table 14 below.

[0264] [Table 14]

[0265]

[0266]

[0267] The terminal-side CSI output format or the base station-side CSI input format may be as shown in Table 15 below. That is, the CSI generated by the terminal by considering AI / ML and transmitted to the base station may be in the form of Table 15 below. Option 1 in Table 15 may be a case where the terminal-side CSI output format or the base station-side CSI input format is in the form of a precoding matrix similar to the existing CSI feedback. The terminal may report CSI feedback to the base station in the form of a precoding matrix together with the confirmed layer number information. For example, the terminal may report CSI feedback in the angular-delay domain as the domain of option 1-b in Table 15, but may not be limited to the embodiment. (e.g., via spatial-frequency DFT domain transformation to angular / delay domain)

[0268] On the other hand, option 2 in Table 15 may be a case where the terminal-side CSI output format or the base station-side CSI input format is a channel matrix with explicit CSI feedback. That is, the explicit CSI feedback of option 2 may not include precoding vectors assigned to each layer as a channel matrix. The explicit channel matrix may be based on channels in the space-frequency domain (option 2-a) or based on channels in the angle-delay domain (option 2-b), but is not limited to a specific form. For example, in the angle-delay domain of option 2-b, loss may be caused by sub-selection of angle-delay domain-based indices, but is not limited thereto.

[0269] [Table 15]

[0270]

[0271]

[0272] Additionally, as an example, applicable new content and transmission methods between AI CSI reports for AI / ML-based CSI compression may be as shown in Tables 16 and 17 below. The priority applied by reflecting AI CSI reports may be determined based on Table 16 below.

[0273] [Table 16]

[0274]

[0275]

[0276] [Table 17]

[0277]

[0278]

[0279]

[0280] For example, the input values ​​for AI / ML-based encoders can be the eigenvectors of the raw channel matrix (H) or the ray channel matrix (V). Here, if the raw channel matrix is ​​the input, the eigenvector calculation may be performed at the base station, which may increase the complexity of the base station. For example, the raw channel matrix may contain unnecessary information for precoder selection, which may increase unnecessary feedback overhead.

[0281] Therefore, considering the above-described characteristics, the AI ​​reporting procedure can be performed by considering which CSI content information will be used as input values ​​for the AI ​​encoder. Furthermore, by determining which CSI content information may impact the AI ​​decoder and how its performance may change accordingly, the AI ​​CSI reporting procedure can be performed by combining appropriate input values ​​from the input values ​​in Tables 16 and 17 described above, but is not limited to a specific format.

[0282] For example, when comparing AI / ML-based quantized CSI compression information for an AI / ML-based CSI feedback mechanism with existing CSI report / contents, the AI / ML-based CSI report fields may have differences as shown in Table 18 below.

[0283] [Table 18]

[0284]

[0285]

[0286] FIG. 12 is a diagram showing a type of beam management procedure applicable to the present disclosure.

[0287] In wireless communication systems, digital precoding-based beamforming can be considered alongside analog beamforming as a beam management method. For example, while analog beamforming can be easily implemented, it can only perform transmit / receive beamforming in one direction at a time. Therefore, analog beamforming can suffer from time delays when multiple beam sweeps are required. Furthermore, a case can be considered where a large number of antennas are used for beamforming. Using a large number of antennas can narrow the beam width and increase the probability of beam tracking failure. If a UE fails beam tracking, it can trigger a beam recovery procedure to recover the beam. Furthermore, each cell can perform beam-based transmission and reception at multiple transmission points. However, since the UE is unaware of the beam-based transmissions of each cell, the beam management procedure can be UE-transparent. Beam correspondence can mean that a transmitting and receiving node have a single appropriate beam pair. For example, beam correspondence may not imply tracking a rapidly changing and frequency-selective channel. Furthermore, beam correspondence may not always require downlink and uplink transmissions to be performed on the same frequency carrier. Therefore, beam correspondence can also be applied to frequency division multiplex (FDD) systems.

[0288] FIG. 12 may be a beam management procedure type and beam management process applicable to the present disclosure. For example, referring to FIG. 12(a), in order to support selection of a base station (1210) transmission beam and a terminal (1220) reception beam, the terminal may perform measurements for selecting a transmission beam on multiple transmission beams. That is, the base station (1210) may perform multiple transmission beam transmissions based on various beam types, and the terminal (1220) may perform measurements on multiple transmission beams transmitted by the base station (1210). Thereafter, the terminal (1220) may select a transmission beam with the best reception quality based on the measurements. The terminal (1220) may report information on the selected transmission beam to the base station (1210) through explicit or implicit signaling. The base station (1210) may then consider the reported transmission beam information as information for narrower beam transmission. As an example, FIG. 12(a) may be, but is not limited to, a P-1 beam management process.

[0289] Referring to Fig. 12(b), the terminal (1240) can perform measurements for different narrow beams. That is, Fig. 12(b) may be a beam refinement procedure for selecting a narrower beam. For example, Fig. 12(b) may be, but is not limited to, a P-2 beam management process. The base station (1230) and the terminal (1240) may perform a procedure for selecting a rough transmission beam according to Fig. 12(a) and then determining a more detailed transmission beam within the transmission beam direction. Here, the base station (1230) may perform different CSI-RS (channel state information-reference signal) transmissions based on different beams. Here, the terminal (1240) may measure only the transmission beams without Rx sweeping. The terminal (1240) can report to the base station (1230) the L1-RSRP (reference signal received power) and CRI (CSI-RS Resource Indicator) for beams with the highest quality based on the measurement.

[0290] Referring to Fig. 12(c), the terminal (1260) can perform reception beam readjustment. Fig. 12(c) may be a P-3 beam management process, but is not limited thereto. Here, the base station (1250) can transmit a CSI-RS having the same transmission beam to the terminal (1260). The terminal (1260) can measure the quality of the reception beam through which the corresponding CSI-RS is received, and select the reception beam based on the quality. However, the terminal (1260) may not report any information about the reception beam selection to the base station (1250). Beam management can be performed based on the above, and the following describes a method of applying an AI / ML algorithm when performing beam management.

[0291]

[0292] Below, we describe a method for managing beams based on AI / ML algorithms. As described above, beams can be transmitted based on beam sweeping. As another example, beam transmission between a base station and a terminal can be performed based on sparse beam sweeping.

[0293] For example, in relation to beam transmission, beam sweeping overhead may be a problem on FR2, which performs beam transmission based on narrow beams. That is, a beam sweeping overhead problem may occur at a base station transmitting narrow beams on FR2. Considering the beam sweeping overhead described above, the base station and the terminal can perform a sparse beam sweeping operation. However, the sparse beam sweeping operation may have a limitation in that the accuracy of the beam management method is reduced because only a portion of the entire beams is used.

[0294] Considering the above, a beam management method based on AI / ML algorithms is described below. For example, at least one of the spatial and temporal characteristics of a channel can be learned through AI / ML algorithms. This can achieve better performance and reduced delay compared to non-AI / ML-based beam management techniques, thereby resolving the aforementioned issues.

[0295] In addition, in case of terminal movement, the optimal beam ID of the terminal may continuously change over time, but the base station may not perform frequent beam sweeping to keep the beam sweeping overhead low. Considering the above, the base station has no choice but to use the beam information obtained from the previous sweeping until the next beam sweeping cycle. For example, if the beam information obtained from the previous sweeping is currently valid, the base station and the terminal can perform communication based on the beam information obtained from the previous beam sweeping, and if the beam information obtained from the previous beam sweeping is different from the actual corresponding beam between the base station and the terminal, the performance of the data / control channels may be significantly degraded. Considering the above, AI / ML techniques can be incorporated into beam management, and procedures, methods, and signaling for this may be required. This is described below.

[0296] For example, an improved beam management technique may be applied. The improved beam management technique may refer to an operation of predicting a beam based on an AI / ML algorithm in the spatial domain. Here, beam prediction in the spatial domain may be performed through an inference operation of an AI / ML model based on an AI / ML algorithm. For example, the AI / ML model necessarily needs to perform a training procedure in order to perform an inference operation of predicting a beam in the spatial domain.

[0297] That is, the AI / ML model needs to be trained first through a learning process. The AI / ML model can build a dataset with measured beam pairs, labels (beam pair indices), and associated beam measurement results (e.g., L1-RSRP) within a neural network (NN) implemented for the learning process. The AI / ML model can perform at least one of pre-processing and post-processing operations with the built dataset during the AI / ML model training process, thereby optimizing the AI / ML model. The optimized AI / ML model can then perform a final test task using a different test dataset. Next, the AI / ML model that has completed training can be located at an actual base station or terminal and perform inference operations. Here, the AI / ML model that has actually completed training can be used to predict transmission and reception beams in the spatial domain.

[0298] For example, channel measurements of beam pairs performed by a terminal can be limited to a limited number of beam pair indices, and the results can be reported to a base station or the node where the AI / ML model is located. The AI / ML model can then infer the optimal K transmit beams or beam pairs and their corresponding performance metrics, thereby deriving the optimal beam.

[0299] FIG. 13 is a diagram illustrating a method for performing beam prediction in a spatial domain applicable to the present disclosure. The base station can predict beams in the spatial domain by considering up to 64 beam indices. However, this is only an example and may not be limited thereto. The base station can transmit to the terminal only transmission beams associated with 8 beam indices (1310) among all beam indices. The terminal can perform L1 measurement on beams corresponding to a subset of beams transmitted from the base station. As an example, in the case of a "one-sided AI / ML" model among the AI / ML models described above, a case in which the AI / ML model is located on the terminal side may be considered. However, this is only an example and may not be limited thereto. In the above-described case, the terminal can use L1 measurement values ​​for some beams (1310) corresponding to the above-described subset. Here, the AI / ML model of the terminal can generate all beams estimated as model output values, and the associated L1-RSRP can also be estimated. The terminal may select the best beam pair and corresponding L1 measurement value (1320) based on the model output value and report the corresponding CRI and L1-RSRP values ​​to the base station. The base station may then perform beam transmission based on the reported beam pair information for data / control channel downlink transmission.

[0300] By inferring behaviors using AI / ML models learned from beam management, we can obtain better beam management prediction results with lower complexity than existing beam management techniques. In other words, beam management performed through AI / ML models can significantly improve performance.

[0301] FIG. 14 is a diagram illustrating a method for performing an AI / ML beam management procedure on the network side applicable to the present disclosure.

[0302] Referring to FIG. 14, the base station (or network, 1410) can perform learning on an AI / ML model to generate an AI / ML model, as well as perform inference operations. For example, the base station (1410) can perform a beam sweeping operation using all transmission beams during the learning process, and the terminal (1420) can receive all of the beams and measure the channel quality of the beams.

[0303] Here, the terminal (1420) can obtain measurement values ​​and beam / beam pair ID information related to the measurement values ​​as a data set for learning. For example, the beam / beam pair ID information related to the measurement values ​​may include optimal beam / beam pair ID information. In addition, the measurement values ​​may include at least one of L1-RSRP (reference signal received power), RSRQ (reference signal received quality), SINR (signal interference noise ratio), and other measurement values, and are not limited to a specific form. The beam ID information may include, but is not limited to, beam-related ID information or RS resource information (e.g., CSI-RS Resource Indicator or SSB Resource Indicator). The terminal (1420) can report a data set for learning to the base station (1410), and the base station (1410) can perform learning on an AI / ML model based on the reported data set. The base station (1410) can learn the AI / ML model by repeating the above-described operation, thereby completing learning of the AI / ML model for beam prediction in the spatial domain.

[0304] Here, the input values ​​for learning the AI / ML model within the base station (1410) may include not only the data set information reported from the terminal (1420), but also at least one of the cell setting environment, antenna implementation and installation environment, network traffic / cell environment, terminal mobility, location, and other side information that the base station (1410) itself has. Through this, the base station (1410) can optimize the AI / ML model within the base station. In addition, the base station (1410) may perform at least one of pre-processing and post-processing (pre / post processing) on ​​the data used for learning the AI / ML model to perform additional correction work during the learning process, thereby increasing the accuracy of the result value for the final inference, but is not limited thereto.

[0305] AI / ML models on the base station side can be generated and trained through the aforementioned process, and inference operations can be performed using the trained AI / ML models. Here, the term "base station side" may refer to a specific node within the base station / transmission point or within the base station server / core network, but may not be limited to a specific location.

[0306] For example, a P1 procedure may be performed to utilize an AI / ML model for beam management. Here, the P1 procedure may be a procedure corresponding to the above-described FIG. 12(a). The base station (1410) may perform a rough wide SSB (synchronization signal block) beam sweeping (sparse / side SSB beam sweeping) through a beam sweeping operation for some beams through the P1 procedure.

[0307] The terminal (1420) can determine the signal quality and ID for the approximate wide transmission beam transmitted from the base station (1410). In FIG. 14, only some of the entire beams can be used as transmission beams for SSB transmission, and the terminal (1420) can receive the transmitted SSBs and perform measurement. Thereafter, the terminal (1420) can report channel quality information (e.g., RSRP) values ​​as measured beam-related information to the base station (1410). The base station (1410) can use the quality information for the approximate SSB beams obtained from the terminal (1420) and the related SSB resource index as input values ​​for the AI / ML model. The AI / ML model can infer information on the optimal K transmission beams through an inference process based on the input values. The base station (1410) can perform CSI-RS beam sweeping transmission using the optimal K transmission beams as the result values ​​obtained through the inference process. The terminal (1420) can select the most appropriate CSI-RS beam among the optimal K transmission beams through measurement. The terminal (1420) can report the selected optimal beam back to the base station (1410) in the form of a CSI-RS ID (CRI). The base station (1410) can perform optimal beam-based data / control signal transmission for the terminal by utilizing the CSI-RS beam corresponding to the optimal beam received from the terminal (1420). The operation may be a P2 procedure, which may correspond to FIG. 12(b).

[0308] Finally, the P3 procedure can be performed for beam selection at the terminal (1420). The P3 procedure may correspond to the procedure in FIG. 12(c). The terminal (1420) may perform measurements assuming the same transmission beam transmission for a certain period of time. The terminal (1420) may perform a reception beam sweeping operation to determine the optimal reception beam during that period. The terminal (1420) may determine the optimal reception beam through the above-described process, and beam correspondence may be created between the base station (1410) and the terminal (1420).

[0309] FIG. 15 is a diagram illustrating a method for performing an AI / ML beam management procedure on a terminal side applicable to the present disclosure.

[0310] Referring to FIG. 15, an AI / ML model can be learned and generated on the terminal (1520) side, and an inference operation can be performed. In FIG. 15, the base station (1510) can perform a beam sweeping operation using all transmission beams during the AI / ML model learning process. The terminal (1520) can receive all beams and measure the channel quality of the beams.

[0311] Here, the terminal (1520) can acquire measurement values ​​and beam / beam pair ID information related to the measurement values ​​as a data set for learning. For example, the beam / beam pair ID information related to the measurement values ​​may include optimal beam / beam pair ID information. In addition, the measurement values ​​may include at least one of L1-RSRP (reference signal received power), RSRQ (reference signal received quality), SINR (signal interference noise ratio), and other measurement values, and are not limited to a specific form. The beam ID information may include, but is not limited to, beam-related ID information or RS resource information (e.g., CSI-RS Resource Indicator or SSB Resource Indicator). The terminal (1520) can perform AI / ML model learning for beam prediction in the spatial domain through the acquired data set. In addition, the terminal (1520) can repeat the above-described process, thereby completing the generation and learning of the AI / ML model.

[0312] Here, the input values ​​of the AI / ML model may further include at least one of the following: cell configuration environment, antenna implementation and installation environment, network traffic / cell environment, terminal mobility, location, and other side information, in addition to data set information based on measurement values. Through this, the terminal (1520) may perform optimization of the AI / ML model within the terminal. In addition, the accuracy of the result value for the final inference may be increased by performing at least one of pre-processing and post-processing (pre / post processing) on ​​the data used for AI / ML model learning to perform additional correction work during the learning process, but may not be limited thereto.

[0313] Based on the above, the AI / ML model learned and generated can be located on the terminal (1520) side and can perform inference operations for beam prediction in the spatial domain. The term "terminal side" may refer to a specific node within the terminal (1520) device or the terminal (1520) manufacturer server side, and may not be limited to a specific form.

[0314] For example, the P1 procedure of FIG. 12(a) described above can be performed based on the AI / ML model located on the terminal side. The base station (1510) can perform sparse / wide SSB beam sweeping through the P1 procedure. The terminal (1520) can determine the signal quality and ID for the roughly wide transmission beam. In FIG. 15, only some beams within the entire beam sets can be used as transmission beams for SSB transmission. The terminal (1520) can receive the beam transmitted from the base station (1510) and perform measurement on the received beam to obtain channel quality information (e.g., RSRP) values. The terminal (1520) can use the obtained quality information for the roughly SSB beam and the related SSB resource index as input values ​​of the AI / ML model to infer information on the optimal K transmission beams through an inference process. The optimal K transmission beams obtained through the inference process can be used for CSI-RS beam sweeping transmission. The terminal (1520) can measure and select the most appropriate CSI-RS beam among the optimal K transmission beams. The selected optimal beam can be reported to the base station (1510) in the form of a CSI-RS ID (CRI). The base station (1510) can perform optimal beam-based data / control signal transmission for the terminal based on the CSI-RS beam corresponding to the optimal beam.

[0315] FIG. 16 is a diagram illustrating a method in which an AI / ML model is learned on a base station side applicable to the present disclosure, inference is performed on the AI / ML model on a terminal (1620) side, and a beam management procedure is performed.

[0316] Referring to FIG. 16, an AI / ML model can be learned and generated on the base station (1610) side. On the other hand, the inference operation of the AI / ML model can be performed on the terminal (1620) side. Referring to FIG. 16, during the AI / ML model learning process, the base station (1610) performs a beam sweeping operation using all transmission beams, and the terminal (1620) can receive all the beams and measure the channel quality of the beams.

[0317] Here, the terminal (1620) can obtain measurement values ​​and beam / beam pair ID information related to the measurement values ​​as a data set for learning. For example, the beam / beam pair ID information related to the measurement values ​​may include optimal beam / beam pair ID information. In addition, the measurement values ​​may include at least one of L1-RSRP (reference signal received power), RSRQ (reference signal received quality), SINR (signal interference noise ratio), and other measurement values, and are not limited to a specific form. The beam ID information may include, but is not limited to, beam-related ID information or RS resource information (e.g., CSI-RS Resource Indicator or SSB Resource Indicator). The terminal (1620) can report a data set for learning to the base station (1610), and the base station (1610) can perform learning on an AI / ML model based on the reported data set. The base station (1610) can learn the AI / ML model by repeating the above-described operation, thereby completing learning of the AI / ML model for beam prediction in the spatial domain.

[0318] Here, the input value of the AI / ML model within the base station (1610) may include not only the data set information reported from the terminal (1620), but also at least one of the cell setting environment, antenna implementation and installation environment, network traffic / cell environment, terminal mobility, location, and other side information that the base station (1610) itself has. Through this, the base station (1610) may perform optimization for the AI / ML model within the base station. In addition, the accuracy of the result value for the final inference may be increased by performing at least one of pre-processing and post-processing (pre / post processing) on ​​the data used for AI / ML model learning to perform additional correction work during the learning process, but may not be limited thereto.

[0319] Thereafter, the AI / ML model generated through the learning process performed on the base station (1610) side can be transferred to the terminal (1620) side. That is, AI / ML model information can be transmitted (downloaded) from the base station (1610) to the terminal (1620).

[0320] The terminal (1620) can perform an inference operation using an AI / ML model received from the base station (1610). Here, the terminal side may refer to a specific node corresponding to the internal part of the terminal device or the terminal manufacturer's server side, but is not limited thereto.

[0321] At the terminal side, the P1 procedure of FIG. 12(a) described above can be performed based on the AI / ML model. The base station (1610) can perform sparse / wide SSB beam sweeping through the P1 procedure. The terminal (1620) can determine the signal quality and ID for the sparse / wide SSB transmission beam. In FIG. 16, only some beams within the entire beam sets can be used as transmission beams for SSB transmission. The terminal (1620) can receive the beams transmitted from the base station and perform measurements on the received beams to obtain channel quality information (e.g., RSRP) values. The terminal (1620) can use the obtained quality information for the sparse SSB beams and the related SSB resource index as input values ​​of the AI / ML model to infer information on the optimal K transmission beams through an inference process. The optimal K transmission beams can be used for CSI-RS beam sweeping transmission using the results obtained through the inference process. The terminal (1620) can select the most appropriate CSI-RS beam among the optimal K transmission beams based on the measurement. The selected optimal beam can be reported to the base station (1610) in the form of a CSI-RS ID (CRI). The base station (1610) can perform optimal beam-based data / control signal transmission for the terminal based on the CSI-RS beam corresponding to the optimal beam.

[0322] As another example, an AI / ML model learned on the terminal side can be uploaded to a base station (1610), and the base station (1610) can use the AI / ML model to perform an inference procedure in a manner similar to FIG. 14. Specifically, an AI / ML model can be learned and generated on the terminal (1620) side.

[0323] The base station (1610) can perform a beam sweeping operation using all transmission beams during the AI / ML model learning process. The terminal (1620) can receive all beams and measure the channel quality of the beams. Here, the terminal (1620) can obtain measurement values ​​and beam / beam pair ID information related to the measurement values ​​as a data set for learning. For example, the beam / beam pair ID information related to the measurement values ​​can include optimal beam / beam pair ID information. In addition, the measurement values ​​can include at least one of L1-RSRP (reference signal received power), RSRQ (reference signal received quality), SINR (signal interference noise ratio), and other measurement values, and are not limited to a specific form. The beam ID information can include, but is not limited to, ID information or RS resource information (e.g., CSI-RS Resource Indicator or SSB Resource Indicator) related to the beam. The terminal (1620) can perform AI / ML model training for beam prediction in the spatial domain using the acquired data set. Furthermore, the terminal (1620) can repeat the above-described process, thereby completing the creation and training of the AI / ML model.

[0324] Here, the input values ​​of the AI / ML model may further include at least one of the following: cell configuration environment, antenna implementation and installation environment, network traffic / cell environment, terminal mobility, location, and other side information, in addition to data set information based on measurement values. Through this, the terminal (1520) may perform optimization of the AI / ML model within the terminal. In addition, by performing at least one of pre-processing and post-processing (pre / post processing) on ​​the data used for AI / ML model learning, additional correction work may be performed during the learning process, thereby increasing the accuracy of the result value for the final inference, but may not be limited thereto.

[0325] Based on the above, the AI / ML model learned and generated can be transmitted (uploaded) from the terminal (1620) to the base station (1610). The base station (1610) can perform an inference operation through the AI / ML model received from the terminal (1620). Here, the base station side may refer to a specific node corresponding to the inside of the base station / transmission point or the base station-side server / core network, but may not be limited to a specific location.

[0326] For example, a P1 procedure may be performed to utilize an AI / ML model for beam management. Here, the P1 procedure may be a procedure corresponding to the above-described FIG. 12(a). The base station (1610) may perform a rough wide SSB beam sweeping (sparse / side SSB beam sweeping) through a beam sweeping operation for some beams through the P1 procedure.

[0327] The terminal (1620) can determine the signal quality and ID for the approximate wide transmission beam transmitted from the base station (1610). In FIG. 16, only some of the entire beams can be used as transmission beams for SSB transmission, and the terminal (1620) can receive the transmitted SSBs and perform measurements on the received beams. Thereafter, the terminal (1620) can report channel quality information (e.g., RSRP) values ​​as measured beam-related information to the base station (1610). The base station (1610) can use the quality information for the approximate SSB beams obtained from the terminal (1620) and the related SSB resource index as input values ​​for the AI / ML model. The AI / ML model can infer information on the optimal K transmission beams through an inference process based on the input values. The base station (1610) can perform CSI-RS beam sweeping transmission using the optimal K transmission beams as the result values ​​obtained through the inference process. The terminal (1620) can measure and select the most appropriate CSI-RS beam among the optimal K transmission beams. The terminal (1620) can report the selected optimal beam back to the base station in the form of a CSI-RS ID (CRI). The base station (1610) can perform optimal beam-based data / control signal transmission for the terminal by utilizing the CSI-RS beam corresponding to the optimal beam received from the terminal (1620). The operation may be a P2 procedure, which may correspond to FIG. 12(b).

[0328] Finally, the P3 procedure can be performed for beam selection at the terminal (1620). The P3 procedure may correspond to the procedure in FIG. 12(c). The terminal (1620) may perform measurements assuming the same transmission beam transmission for a certain period of time. The terminal (1620) may perform a reception beam sweeping operation to determine the optimal reception beam during that period. The terminal (1620) may determine the optimal reception beam through the above-described process, and beam correspondence may be created between the base station (1610) and the terminal (1620).

[0329] In other words, depending on the interests and preferences of operators and equipment manufacturers, various methods for AI / ML model training and inference may exist, and at least one of the methods described above may be used. Utilizing at least one of the above methods can support an improved beam management method based on AI / ML models, ultimately providing improved system performance and implementation compared to existing beam management techniques.

[0330] FIG. 17 is a flowchart illustrating a method for performing beam management applicable to the present disclosure. Referring to FIG. 17, a wireless user device may receive at least one wide beam from a base station based on a rough wide beam sweeping (S1710). Here, an AI / ML model may exist in at least one of the base station and the wireless user device. If an AI / ML (artificial intelligence) model exists in the base station, the base station may transmit the entire beam to the wireless user device based on beam sweeping, and receive a data set acquired from the wireless user device to perform learning for the AI / ML model.

[0331] As another example, if an AI / ML model exists in a wireless user device, the wireless user device can receive the entire beam from the base station based on beam sweeping, obtain a data set based on the received entire beam, and perform training for the AI / ML model, as described above.

[0332] Here, at least one wide beam may be, but is not limited to, a sparse / wide SSB beam. For example, if the AI / ML model is located at the base station, the base station may obtain measurements for at least one wide beam from the wireless user device and provide them as input to the trained AI / ML model, and generate at least one narrow beam as a result value through inference of the AI / ML model and transmit it to the wireless user device.

[0333] On the other hand, when the AI / ML model is located in the wireless user device, the wireless user device can perform measurements for at least one wide beam and provide them as input to the learned AI / ML model, and generate at least one narrow beam as a result value through inference of the AI / ML model and transmit information about the at least one narrow beam to the base station.

[0334] The wireless user device can receive at least one determined narrow beam, determine an optimal narrow beam, and transmit information about the optimal narrow beam to the base station. (S1720) Here, the at least one narrow beam may be, but is not limited to, an optimal K CSI-RS beam (Top-k CSI-RS beam). Thereafter, the wireless user device can receive data transmitted based on the optimal narrow beam. (S1730) The optimal narrow beam may be, but is not limited to, an optimal CSI-RS beam.

[0335] Figure 18 is a drawing showing a base station device and a terminal device to which the present disclosure can be applied.

[0336] The base station device (1800) may include a processor (1820), an antenna unit (1812), a transceiver (1826), and a memory (1816).

[0337] The processor (1820) performs baseband-related signal processing and may include a higher layer processing unit (1830) and a physical layer processing unit (1840). The higher layer processing unit (1830) may process operations of a MAC (Medium Access Control) layer, an RRC (Radio Resource Control) layer, or higher layers. The physical layer processing unit (1840) may process operations of a physical (PHY) layer (e.g., uplink reception signal processing, downlink transmission signal processing). In addition to performing baseband-related signal processing, the processor (1820) may also control the overall operation of the base station device (1800).

[0338] The antenna unit (1812) may include one or more physical antennas, and when it includes multiple antennas, it may support MIMO (Multiple Input Multiple Output) transmission and reception. In addition, it may support beamforming.

[0339] The memory (1816) can store information processed by the processor (1820), software related to the operation of the base station device (1800), an operating system, applications, etc., and may also include components such as a buffer.

[0340] The processor (1820) of the base station (1800) may be configured to implement the operations of the base station in the embodiments described in the present invention.

[0341] The terminal device (1850) may include a processor (1870), an antenna unit (1862), a transceiver (1864), and a memory (1866). For example, the terminal device (1850) in the present invention may communicate with a base station device (1800). As another example, the terminal device (1850) in the present invention may perform sidelink communication with another terminal device. That is, the terminal device (1850) in the present invention refers to a device that can communicate with at least one of the base station device (1800) and another terminal device, and is not limited to communication with a specific device.

[0342] The processor (1870) performs baseband-related signal processing and may include a higher layer processing unit (1880) and a physical layer processing unit (1890). The higher layer processing unit (1880) may process operations of the MAC layer, the RRC layer, or higher layers. The physical layer processing unit (1890) may process operations of the PHY layer (e.g., downlink reception signal processing, uplink transmission signal processing). In addition to performing baseband-related signal processing, the processor (1870) may also control the overall operation of the terminal device (1850).

[0343] The antenna unit (1862) may include one or more physical antennas, and when it includes multiple antennas, it may support MIMO transmission and reception. In addition, it may support beamforming.

[0344] The memory (1866) can store information processed by the processor (1870), software related to the operation of the terminal device (1850), an operating system, applications, etc., and may also include components such as a buffer.

[0345] A terminal device (1850) according to an example of the present invention may be associated with a vehicle. For example, the terminal device (1850) may be integrated into, positioned in, or located on the vehicle. Furthermore, the terminal device (1850) according to the present invention may be the vehicle itself. Furthermore, the terminal device (1850) according to the present invention may be at least one of a wearable terminal, an AV / VR, an IoT terminal, a robot terminal, and a public safety terminal. The terminal device (1850) to which the present invention is applicable may include any type of communication device that supports interactive services utilizing sidelink for services such as Internet access, service execution, navigation, real-time information, autonomous driving, and safety and risk diagnosis. Furthermore, any type of communication device capable of sidelink operation or an AR / VR device or a sensor that performs relay operation may be included.

[0346] Here, the vehicles to which the present invention is applied may include autonomous vehicles, semi-autonomous vehicles, non-autonomous vehicles, etc. Meanwhile, while the terminal device (1850) according to one example of the present invention is described as being associated with a vehicle, one or more of the UEs may not be associated with a vehicle. This is merely an example, and should not be construed as limiting the application of the present invention to the described example.

[0347] In addition, the terminal device (1850) according to an example of the present invention can receive at least one wide beam from the base station (1800) based on approximate wide beam sweeping. Here, an AI / ML model may exist in at least one of the base station (1800) or the terminal device (1850). If the AI / ML (artificial intelligence) model exists in the base station (1800), the base station (1800) can transmit the entire beam to the terminal device (1850) based on beam sweeping, and receive a data set acquired from the terminal device (1850) to perform learning for the AI / ML model. As another example, if the AI / ML model exists in the terminal device (1850), the terminal device (1850) can receive the entire beam from the base station (1800) based on beam sweeping, and acquire a data set based on the received entire beam to perform learning for the AI / ML model, as described above.

[0348] Here, at least one wide beam may be a sparse / wide SSB beam, but may not be limited thereto. For example, when the AI / ML model is located in the base station (1800), the base station (1800) may obtain a measurement value for at least one wide beam from the terminal device (1850), provide it as an input to the learned AI / ML model, and generate at least one narrow beam as a result value through inference of the AI / ML model and transmit it to the terminal device (1850). On the other hand, when the AI / ML model is located in the terminal device (1850), the terminal device (1850) may perform a measurement for at least one wide beam, provide it as an input to the learned AI / ML model, generate at least one narrow beam as a result value through inference of the AI / ML model, and transmit information about the at least one narrow beam to the base station (1800).

[0349] The terminal device (1850) can receive at least one determined narrow beam, determine an optimal narrow beam, and transmit information about the optimal narrow beam to the base station (1800). (S1720) Here, the at least one narrow beam may be an optimal K CSI-RS beam (Top-k CSI-RS beam), but may not be limited thereto. Thereafter, the terminal device (1850) can receive data transmitted based on the optimal narrow beam. The optimal narrow beam may be an optimal CSI-RS beam, but may not be limited thereto.

[0350] Additionally, various embodiments of the present disclosure may be implemented by hardware, firmware, software, or a combination thereof. In the case of hardware implementation, the embodiments may be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), general processors, controllers, microcontrollers, microprocessors, etc.

[0351] The scope of the present disclosure includes software or machine-executable instructions (e.g., operating systems, applications, firmware, programs, etc.) that cause operations according to the methods of various embodiments to be executed on a device or a computer, and a non-transitory computer-readable medium having such software or instructions stored thereon and executable on the device or computer.

[0352] The various embodiments of the present disclosure are not intended to list all possible combinations but rather to illustrate representative aspects of the present disclosure, and the matters described in the various embodiments may be applied independently or in combination of two or more.

[0353]

[0354] The above may also apply to other systems.

Claims

1. In terms of method, A step of a wireless user device receiving at least one wide beam from a base station based on a rough wide beam sweep; A step of receiving at least one narrow beam determined based on measurements for at least one wide beam, determining an optimal narrow beam, and transmitting information about the optimal narrow beam to the base station; and A method comprising the step of receiving data transmitted based on the optimal narrow beam.

2. In paragraph 1, If an AI / ML (artificial intelligence) model exists in the base station, the base station transmits the entire beam to the wireless user device based on beam sweeping, acquires a data set obtained from the wireless user device, and performs learning for the AI / ML model. A method for obtaining measurement values ​​for at least one wide beam from the wireless user device and providing them as inputs to the learned AI / ML model, and generating at least one narrow beam as a result value through inference of the AI / ML model and transmitting the result to the wireless user device.

3. In paragraph 1, If an AI / ML model exists in the wireless user device, the wireless user device receives the entire beam from the base station based on beam sweeping, obtains a data set based on the received entire beam, and performs learning for the AI / ML model. A method for performing measurements on at least one wide beam and providing the same as input to the learned AI / ML model, generating at least one narrow beam as a result value through inference of the AI / ML model, and transmitting information on the at least one narrow beam to the base station.

4. For wireless devices, at least one processor; and A memory storing instructions that cause the wireless user device to perform a specific operation by the at least one processor, The above specific actions are: Receive at least one wide beam based on a rough wide beam sweep from a base station, Receive at least one narrow beam determined based on measurements for at least one wide beam, determine an optimal narrow beam, and transmit information about the optimal narrow beam to the base station, and A wireless user device receiving data transmitted based on the optimal narrow beam.

Images