Method and device for reporting channel state information in wireless communication system

Abstract

This method may comprise the steps of: triggering, by a terminal, a CSI report in a wireless communication system; determining, by the terminal, the CSI report on the basis of a priority; and performing the determined CSI report. Here, when the terminal triggers a first type CSI report, the terminal may identify whether a second type CSI report overlaps the first type CSI report in at least one symbol in which the first type CSI report is performed, and when the first type CSI report and the second type CSI report overlap each other, the terminal may perform the second type CSI report prior to the first type CSI report.

Description

Method and device for reporting channel state information in a wireless communication system

[0001] The present disclosure relates to a method and device for reporting channel state information (CSI) in a wireless communication system. Specifically, the present disclosure relates to a method and device for reporting CSI for an artificial intelligence machine learning-based system in a wireless communication system.

[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. This enables 5G communications to 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 performing channel state information (CSI) reporting in a wireless communication system.

[0008] The technical problem of the present disclosure is a method and device for performing AI (artificial intelligence) CSI reporting in a wireless communication system.

[0009] The technical problem of the present disclosure is a method and device for handling collisions between existing CSI reports and AI CSI reports in a wireless communication system.

[0010] The technical problem of the present disclosure is a method and device for determining priorities between AI CSI reports in a wireless communication system.

[0011] The technical problem of the present disclosure is a method and device for determining the priority of existing CSI reports and AI CSI reports in a wireless communication system.

[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, the method may include a step of triggering a CSI report by a terminal, a step of determining a CSI report based on a priority, and a step of performing the determined CSI report.

[0015] Additionally, according to one aspect of the present disclosure, the device includes at least one processor and a memory storing instructions for causing a wireless user device to perform a specific operation by the at least one processor, wherein the specific operation is: triggering a channel state information (CSI) report, causing the terminal to determine a CSI report based on a priority, and performing the determined CSI report.

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

[0017] According to one aspect of the present disclosure, when a terminal triggers a first type CSI report, it is checked whether a second type CSI report overlaps in at least one symbol in which the first type CSI report is performed, and if the first type CSI report and the second type CSI report overlap, the second type CSI report can be performed with priority over the first type CSI report.

[0018] Additionally, according to one aspect of the present disclosure, if the second type CSI report takes precedence, the first type CSI report may be dropped or reported after a preset delay.

[0019] Additionally, according to one aspect of the present disclosure, when the first type CSI report and the second type CSI report do not overlap, it is determined whether a plurality of first type CSI reports overlap in at least one symbol, and when the plurality of first type CSI reports overlap, the first type CSI report can be performed based on the priority of the first type CSI report.

[0020] Additionally, according to one aspect of the present disclosure, the priority of the first type CSI report may be determined based on at least one of the CSI report method, the CSI report channel, the CSI content, the serving cell index, and the report configuration ID.

[0021] In addition, according to one aspect of the present disclosure, the terminal determines a CSI report to be reported based on the priority of the CSI report, wherein the CSI report includes a first type CSI report and a second type CSI report, and the priority of the CSI report may be determined by considering the first type CSI report and the second type CSI report together.

[0022] Additionally, according to one aspect of the present disclosure, the priority of a CSI report may be determined based on at least one of a CSI report method, a CSI report channel, a CSI content, a serving cell index, and a report configuration ID.

[0023] Additionally, according to one aspect of the present disclosure, the first type CSI report may be an AI CSI report, and the second type CSI report may be a wireless channel related CSI report.

[0024]

[0025] According to the present disclosure, a method for performing CSI reporting in a wireless communication system can be provided.

[0026] According to the present disclosure, a method for performing AI CSI reporting in a wireless communication system can be provided.

[0027] According to the present disclosure, a method for handling collisions between existing CSI reports and AI CSI reports in a wireless communication system can be provided.

[0028] According to the present disclosure, a method for determining priorities between AI CSI reports in a wireless communication system can be provided.

[0029] According to the present disclosure, a method for determining priorities of existing CSI reports and AI CSI reports in a wireless communication system can be provided.

[0030] 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.

[0031]

[0032] FIG. 1 is a drawing for explaining the frame structure of a wireless communication system to which the present disclosure can be applied.

[0033] FIG. 2 is a diagram showing the resource structure of a wireless communication system to which the present disclosure can be applied.

[0034] FIG. 3 is a diagram illustrating CSI-RS resources to which the present disclosure can be applied.

[0035] FIG. 4 is a diagram illustrating a type 1 CSI applicable to the present disclosure.

[0036] FIG. 5 is a diagram illustrating a method of using multiple SRSs that can be applied to the present disclosure.

[0037] FIG. 6 is a diagram illustrating a method for performing non-codebook based precoding that can be applied to the present disclosure.

[0038] FIG. 7 is a diagram illustrating CSI omission applicable to the present disclosure.

[0039] Figure 8 is a diagram illustrating AI / ML model life cycle management applicable to the present disclosure.

[0040] FIG. 9 is a diagram illustrating a method for performing AI / ML model training based on Type 1 applicable to the present disclosure.

[0041] FIG. 10 is a diagram illustrating a method for performing AI / ML model training based on Type 2 applicable to the present disclosure.

[0042] FIG. 11 is a diagram illustrating a method for performing AI / ML model training based on Type 3 applicable to the present disclosure.

[0043] FIG. 12 is a flowchart of a method for performing CSI reporting applicable to the present disclosure.

[0044] FIG. 13 is a flowchart of a method for performing CSI reporting applicable to the present disclosure.

[0045] Figure 14 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 NR 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] 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.

[0080] 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.

[0081] 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.

[0082] 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).

[0083] 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.

[0084] 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).

[0085] [Table 2]

[0086]

[0087]

[0088] 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.

[0089] [Table 3]

[0090]

[0091]

[0092] 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).

[0093] As mentioned above, one subframe may correspond to 1ms on the time axis. Furthermore, 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 the present invention is not limited to these examples.

[0094] [Table 4]

[0095]

[0096]

[0097] A terminal can calculate downlink CSI parameters (e.g., CQI, PMI, RI, etc.) through a DL (downlink) CSI 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 indicate data scheduling information to the terminal based on the CSI parameters. Alternatively, the terminal can measure the channel state through another downlink signal (e.g., SSB (synchronization signal block)) and report the measured value to the base station.

[0098]

[0099] Select RI (rank indicator):

[0100] 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.

[0101] Select precoding matrix index (PMI):

[0102] A PMI may include a set of indices corresponding to a precoding matrix. A base station may apply the PMI to downlink transmissions. 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. Each codebook type is described below.

[0103] Codebook type

[0104] - Type 1 single-panel codebooks

[0105] - Type 1 multi-panel codebooks

[0106] - Type 2 codebooks

[0107] - Enhanced Type 2 codebooks

[0108]

[0109] For example, PMI selection may be performed based on the codebook type, the number of transmission layers, 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.

[0110] 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.

[0111] W2 codebook type

[0112] - Beam selection from W1

[0113] - Weighting coefficients for the beams in W1

[0114] - Cophasing values ​​between two polarizations

[0115]

[0116] [Equation 3]

[0117]

[0118]

[0119] 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.

[0120] 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.

[0121] 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 simply reports the measured channel status, and thus may not consider how the base station processes the reported CSI. On the other hand, implicit CSI feedback may be a method in which the terminal indicates a desired precoder matrix W within a set of candidate precoder matrices from a possible precoder codebook.

[0122] 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.

[0123] [Table 5]

[0124]

[0125]

[0126] 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.

[0127] For example, if set to "none", the base station may trigger aperiodic CSI-RS transmissions to the terminal. Aperiodic CSI-RS transmissions may be triggered by the terminal for channel measurement and receiver parameter adjustment, and may not require related CSI reporting. Aperiodic CSI-RS transmissions may be, but are not limited to, aperiodic transmissions of CSI-RS or TRS for tracking, or spatial aperiodic receive beam sweeping for the terminal to adjust its 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.

[0128] - Aperiodic transmission of the Tracking Reference Signal (TRS or CSI-RS for tracking)

[0129] - Aperiodic Rx beam sweeping (so called P3 beam sweep) - adjusts the Rx spatial received filter to the UE

[0130]

[0131] Additionally, CSI content within one CSI report may be set as shown in Table 6 below, but may not be limited thereto.

[0132] [Table 6]

[0133]

[0134]

[0135] For example, “cri-RI-i1” in Table 6 can operate based on “Hybrid beamformed / non-precoded CSI acquisition operation” as shown in FIG. 3.

[0136] 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.

[0137] For example, non-precoded CSI-RS (310) can be used to identify 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.

[0138] 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.

[0139] 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.

[0140] [Table 7]

[0141]

[0142]

[0143] 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 Equation 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.

[0144] [Equation 4]

[0145]

[0146]

[0147] 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.

[0148] 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.

[0149] [Equation 5]

[0150]

[0151]

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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, which may correspond 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 each of the two polarizations.

[0156] 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.

[0157] 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, the existing Type 2 CSI can increase the overhead because a relatively large combining coefficient value must be reported per subband, even considering the fact that the channels between adjacent subbands have significant correlation. Enhanced Type 2 CSI can utilize frequency correlation to reduce the 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.

[0158] [Equation 6]

[0159]

[0160]

[0161] 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 be composed 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.

[0162] 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.

[0163] [Equation 7]

[0164]

[0165]

[0166] 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.

[0167] 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.

[0168] [Equation 8]

[0169]

[0170]

[0171] Finally 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, The amount of CSI to be reported can be adjusted through the β value of 2LM, which allows for 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 may be considered in uplink transmission. PUSCH (physical uplink shared channel) MIMO precoding can support up to four layers. For uplink transmission, DFT-based precoding may only support single-user (SU) MIMO. For example, a UE may perform codebook-based transmission and non-codebook-based transmission using two different PUSCH precoding modes. In the case of codebook-based precoding, the uplink grant transmitted by the base station may include precoder-related information (e.g., PMI). The UE may use a precoder provided by the base station in the uplink differently from the downlink transmission. Furthermore, coherence may be assumed between the UE antennas in uplink MIMO transmission, and the phases of signals transmitted on the two antennas may 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] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] [Table 9]

[0185]

[0186]

[0187] [Table 10]

[0188]

[0189]

[0190] 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.

[0191] 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.

[0192] Additionally, CSI Part 1 and CSI Part 2 can each be set based on Table 11 and Table 12.

[0193] [Table 11]

[0194]

[0195]

[0196] [Table 12]

[0197]

[0198]

[0199] 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).

[0200] 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.

[0201] 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.

[0202] 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).

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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 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 CSI information to the base station via PUSCH through partial CSI omission.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] [Table 13]

[0213]

[0214]

[0215]

[0216] For example, deep learning, a subcategory of machine learning (ML), can be a method that focuses on parameterizing multi-layer neural networks. Deep learning can be a method for learning representativeness of data 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.

[0217] 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.

[0218] 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.

[0219] In addition, the inference operation can be performed based on the input / output, pre / post-processing, and application stages. The life cycle management (LCM) procedure of a general AI / ML framework and an AI / ML model can be performed based on the following steps. As an example, FIG. 8 is a diagram illustrating the 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, 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.

[0220] 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.

[0221] In relation to the LCM procedure, one AI / ML model may have one Model ID and related information and / or model functions for AI / ML operations, as described above.

[0222]

[0223] Actions within the LCM procedure

[0224] - Data Collection

[0225] - Model Training

[0226] - Functionality / model identification

[0227] - Model transfer

[0228] - Model Inference

[0229] - Functionality / model selection, activation, deactivation, switching and fallback operation

[0230] - Functionality / model monitoring

[0231] - Model update

[0232] - Model Storage

[0233] - UE capability

[0234]

[0235] 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.

[0236]

[0237] AI / ML-based operation of wireless communication systems

[0238] CSI (Channel State Information): compression (**), temporal prediction (*)

[0239] BM(Beam Management): spatial prediction (*), temporal prediction (*)

[0240] Positioning: direct (*), assisted (*)

[0241] (**) is a two-side model (*) is a single-side model

[0242]

[0243] As a concrete example, we can consider AI / ML model training collaborations for CSI compression using a two-sided model.

[0244] 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.

[0245] 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.

[0246] 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.

[0247] 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.

[0248] 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.

[0249] 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.

[0250] 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.

[0251] 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.

[0252] 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.

[0253] 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.

[0254] 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. As an example, Type 3 model training can be distinguished into network-first training or terminal-first training, similar to Type 1 model training.

[0255] 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.

[0256] The network (1120) side inputs the CSI generation part's input value ( ) 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).

[0257] 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.

[0258] [Table 14]

[0259]

[0260]

[0261] Additionally, as an example, the input for CSI compression may be the eigenvector of a raw channel matrix (H) or a 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 row channel matrix may include unnecessary information for precoder selection, and unnecessary feedback overhead may increase. For example, if the eigenvector is used as the input, the terminal may perform singular value decomposition (SVD) for the channel matrix.

[0262] Below, we describe a method for performing AI / ML-based CSI compression in a wireless communication system. When a terminal performs CSI reporting in a mobile wireless system (e.g., LTE, NR, 6G), collisions between CSI reports transmitted by the terminal or between CSI reports and uplink shared channel (UL-SCH) may occur.

[0263] For example, when a terminal performs an AI / ML-based operation in a wireless communication system, the terminal may report an AI / ML-based CSI feedback report (hereinafter, “AI CSI report”) in addition to the above-described CSI feedback report. Here, the AI ​​CSI report may refer to a CSI report related to CSI feedback reported by the terminal based on AI / ML in the case where the terminal performs AI / ML-based CSI compression, AI / ML-based CSI prediction, AI / ML-based training, or other AI / ML-based CSI reports by the terminal, and may not be limited to a specific form. In other words, the AI ​​CSI report may refer to a CSI feedback report and content additionally configured based on AI / ML, and may not be limited to the form or content. For the convenience of explanation, it is referred to as an AI CSI report below, but it is not limited to that term and may also be used as another term. In addition, for the convenience of explanation, the above-described CSI report is referred to as a conventional CSI report below. In other words, existing CSI reporting may refer to reporting performed in relation to wireless channels, rather than AI / ML-based CSI reporting, but may not be limited to a specific format or content. However, for convenience of explanation, it will be referred to as existing CSI reporting below.

[0264] When a terminal reports additional AI CSI reports to the base station, collision issues among possible CSI reports within the terminal may arise. Specifically, multiple CSI reports may be performed within at least one orthogonal frequency division multiplexing (OFDM) symbol, which may result in CSI collision issues where CSI reports overlap within at least one OFDM symbol.

[0265] For example, a base station and a terminal can modify fields or parameters defined in the existing CSI reporting method to share information corresponding to an AI / ML-based CSI feedback mechanism on the physical layer. Accordingly, the terminal can perform CSI reporting based on the existing CSI reporting procedure and simultaneously perform AI CSI reporting based on the AI / ML-based CSI reporting procedure for introducing new CSI compression (or other purposes). In the above-described case, a conflict issue may arise between the existing CSI report and the AI ​​CSI report. Or, a conflict issue may arise between AI CSI reports, and a solution to this problem is described below.

[0266] Below, CSI parameters for AI / ML-based CSI reporting (AI CSI reporting) are not limited to a single CSI report. That is, AI CSI reporting may have different characteristics from conventional CSI reporting. Therefore, even if the format in which CSI-related information is reported by the terminal to the base station, the reported information may differ from conventional CSI parameters. Accordingly, AI CSI reporting may be considered a different report format than conventional CSI parameters. For example, conventional CSI reporting may form a single CSI report based on a combination of multiple CSI parameters. On the other hand, feedback corresponding to AI / ML-based CSI parameters may be a different report type, referred to as an AI / ML information report.

[0267] Therefore, the proposed method is not limited to a specific case among the possible scenarios described above, but is assumed to be applicable in all possible scenarios, suggesting that the proposed method can be utilized. This is referred to as AI CSI reporting. As an example, the following describes specific information related to AI-CSI reporting for performing AI-based CSI compression / prediction.

[0268] As described above, different CSI reports within the CSI reporting framework can avoid collisions based on priority rules. These priority rules can be determined based on the CSI report transmission period and channel (e.g., aperiodic, semi-persistent, or periodic, or PUSCH or PUCCH), content, serving cell index, report configuration ID, and other factors.

[0269] Here, when AI CSI reporting (i.e., a new AI / ML-based reporting type) is applied, AI CSI reporting priority rules can be established based on the content, reporting method, and other related information of the AI ​​CSI report. Below, priority rules are described to avoid conflicts between AI CSI reports or between AI CSI reports and existing CSI reports based on the AI ​​CSI reporting method (i.e., the newly introduced AI / ML-based CSI reporting).

[0270] For example, applicable new content and transmission methods between AI CSI reports for CSI compression may be as shown in Tables 15 and 16 below. The priorities applied to reflect AI CSI reports may be determined based on Table 15 below.

[0271] [Table 15]

[0272]

[0273]

[0274] [Table 16]

[0275]

[0276]

[0277] For example, the information in Tables 15 and 16 may be configured and set based on a combination of various AI CSI reports at each step within the LCM procedure based on at least one of base station settings, channel conditions, terminal capabilities, and other information, and is not limited to a specific format. Furthermore, AI CSI reports may be configured and set considering other content or other steps other than those in Tables 15 and 16, and are not limited to a specific format.

[0278] With respect to the priority rules established based on the above, the priority rules for CSI reports corresponding to existing CSI reports may be determined to always have a higher priority than the AI ​​CSI report priority rules. In other words, existing CSI reports may always have a higher priority than AI-based CSI reports.

[0279] Specifically, while traditional CSI reports may be critical radio channel information used in real time by base stations for scheduling purposes for actual data transmission, AI CSI reports may be prioritized over traditional CSI reports because they are utilized for LCM purposes in AI models for AI-based CSI compression operations. Furthermore, for example, AI CSI reports may not have high latency requirements and their transmission frequency may not be high, allowing them to be prioritized over traditional CSI reports.

[0280] Here, a priority can be determined for determining at least one AI CSI report among AI CSI reports. That is, the terminal can apply existing CSI reports with priority over AI CSI reports, and determine which AI CSI to report based on the determined priority among AI CSI reports.

[0281] For example, if a collision occurs between an existing CSI report and another AI-CSI report in the same time slot and at least one of the same carriers (i.e., a time overlap occurs), the terminal may transmit the existing CSI report and drop the AI ​​CSI report or delay transmission to a different time slot.

[0282] As another example, if multiplexing of existing CSI reports and AI CSI reports is possible, AI CSI reports can be multiplexed with existing CSI reports. In this case, when existing CSI content and AI CSI content are multiplexed within a single CSI report, the priority can be improved by assuming AI content as existing CSI content, thereby improving the prioritization method for existing CSI reports.

[0283] For example, when existing CSI content and AI CSI content are multiplexed, the priorities for the existing CSI content and the AI ​​CSI content may be determined differently. Here, existing CSI content may be given a higher priority than AI CSI content, allowing CSI content to be transmitted with priority. In other words, existing CSI content and AI CSI content may also be multiplexed based on priority, in which case existing CSI content may be given a higher priority than AI CSI content. However, this may not be limited to this.

[0284] For example, when existing CSI content and AI CSI content are multiplexed, the CSI content included in the CSI report can be determined based on the priority between the proposed existing CSI content and the AI ​​CSI content. In other words, the CSI content included in the CSI report can be included in the CSI report based on the priority between the proposed existing CSI content and the AI ​​CSI content. Naturally, CSI content with lower priority that is not included in the CSI report can be dropped or transmitted later.

[0285] The priority between AI-CSI reports may be determined based on at least one combination of information related to AI CSI reports in Tables 15 and 16. However, the combination of at least one of the information related to AI CSI reports may not be limited to a specific form, and all possible combinations may be included based on the information in Tables 15 and 16. The priority groups described below (e.g., priority #0, #1, #N-1) may be formed by combining the information included in Tables 15 and 16, and additional detailed priorities may also be determined within each priority group. In addition, the priorities between priority groups are described below.

[0286] As a specific example, the priority value may be as shown in Equation 9 below. If multiple AI-CSI reports overlap on at least one OFDM symbol of the time axis, the terminal may perform an AI-CSI report with a smaller priority value based on Equation 9 and may not perform an AI-CSI report with a higher priority value. That is, a smaller A value may indicate a higher priority. Specifically, in Equation 9, a smaller value of y, k, c, or s may indicate a higher priority within that priority group.

[0287] [Equation 9]

[0288]

[0289]

[0290] Below, we describe a method for determining priorities for AI-CSI reporting based on Equation 9 described above. However, some variables may be omitted or added to Equation 9. Furthermore, other forms of priority value determination may also be possible within Equation 9. Furthermore, it may be possible to use other variables or define other forms of priority values, and the method may not be limited to a specific form.

[0291] As an example, the following describes how priorities are applied between priority groups. However, priorities between priority groups may be determined through different combinations and are not limited to a specific form. Furthermore, priorities within each priority group may be defined through different combinations and are not limited to the method described below. That is, based on Tables 15 and 16 described above, priority groups and priorities within each priority group are determined based on a combination of information for a new form of AI-CSI reporting related to AI CSI reporting, and the combination may not be limited to a specific form.

[0292] For example, priority group #A may be a group that corresponds to a combination of physical channels associated with the AI-CSI reporting method. Possible combinations for each priority group may be as shown in Table 17 below. However, Table 17 is merely an example and may not be limited thereto.

[0293] Referring to Table 17, priorities can be determined based on whether AI CSI reporting is aperiodic, semi-static, or periodic. Furthermore, priorities can be determined based on whether AI CSI reporting is performed over PUSCH or PUCCH. Furthermore, priorities can be determined based on whether reporting is based on MAC CE / RRC. For example, Table 17 may be an example of a combination determined based on the above-described information, with a smaller y value indicating a higher priority. However, the combinations in Table 17 may not be limited to these combinations.

[0294] For example, priority group #A1 can give the highest priority to aperiodic AI CSI reporting via PUSCH, followed by semi-static AI CSI reporting via PUSCH, and then by semi-static AI CSI reporting via PUCCH. Lastly, periodic AI CSI reporting via PUCCH can be set as the last priority. In other words, priority groups can be set based on the reporting method and reporting channel to determine if priority transmission is required. As another example, priority group #A2 can add MAC CE / RRC-based AI CSI reporting to priority group #A1 and give it the last priority. As another example, priority group #A3 can give priority to AI CSI reporting based on L1 signaling and give lower priority to AI CSI reporting based on L2 / L3 signaling.

[0295] That is, priority groups can be set in various forms, and the terminal can determine the priority in case of AI CSI reporting conflict based on at least one of the priority groups in Table 17 below.

[0296] For example, it may be possible to determine priorities based on a combination of priority groups in Table 17. Specifically, L1 signaling-based AI CSI reporting may be prioritized over L2 / L3 signaling-based AI CSI reporting by priority group #A3. Furthermore, among L1 signaling-based AI CSI reports, aperiodic AI CSI reporting by priority group #A1 may be given the highest priority, and priorities may be determined as shown in Table 17, but this embodiment is not limited thereto.

[0297] [Table 17]

[0298]

[0299]

[0300] As another example, Table 18 is an example of determining priority group #B. Referring to Table 18, it may be a priority group corresponding to AI CSI reports including AI CSI contents in Tables 15 and 16. Examples of possible combinations for each priority group may be as shown in Table 18, but are not limited thereto. In addition, the value of k is a value that determines the order of priorities defined within priority group #B, and a smaller value indicates a higher priority within the group. In addition, as an example, the number of k values ​​may be determined according to the number of priorities to be defined within the group. The k value may be set in a higher layer or may be preset and is not limited to a specific form. The priorities within a priority group may be composed by combining some of the new AI CSI contents within Tables 15 and 16, but this is only an example and may not be limited thereto. Specifically, a priority group may be determined based on a combination of each piece of information within the AI ​​CSI content.

[0301] As another example, in Table 18, each priority group may be set in multiple numbers, and among the multiple priority groups set, a specific priority group may be configured to the terminal as a higher layer setting or a basic setting, and may not be limited to a specific form.

[0302] Additionally, as an example, priority group #A may have a higher priority than priority group #B compared to Table 17. That is, priority group #B may be considered if a conflict issue for AI CSI reporting is not resolved by priority group #A, but may not be limited thereto.

[0303] Additionally, priority groups #B1 to #B10 in Table 18 may be applied singly or in combination, and may not be limited to a specific form.

[0304] [Table 18]

[0305]

[0306]

[0307]

[0308] As another example, priority group #C may be a priority group corresponding to the serving cell index where AI-CSI reporting is performed, and the possible combinations for each priority group may be as shown in Table 19, but may not be limited thereto. For example, a smaller serving cell index may have a higher priority.

[0309] Also, as an example, comparing Tables 17 and 18, priority group #A may have a higher priority than priority group #B, and priority group #B may have a higher priority than priority group #C. That is, if a conflict issue for AI CSI reporting is not resolved by priority group #A, priority group #B may be considered, and if a conflict issue for AI CSI reporting is not resolved in that case as well, priority group #C may be considered.

[0310] [Table 19]

[0311]

[0312]

[0313] As another example, priority group #D may correspond to ID information related to one or more settings and parameters associated with AI-CSI reporting, and the possible combinations for each priority group may be as shown in Table 20, but are not limited thereto.

[0314] Also, as an example, priority group #A may have a higher priority than priority group #B, priority group #B may have a higher priority than priority group #C, and priority group #B may have a higher priority than priority group #C, compared to Tables 17 to 19. That is, if a conflict issue for AI CSI reporting is not resolved by priority group #A, priority group #B may be considered, and if the conflict issue for AI CSI reporting is not resolved even in that case, priority group #C may be considered, and if the conflict issue for AI CSI reporting is not resolved even in that case, priority group #D may be considered.

[0315] [Table 20]

[0316]

[0317]

[0318] For example, priorities are determined among the priority groups (e.g., #A to #D) of Tables 17 to 20 described above, and detailed priorities within each priority can be determined based on the following mathematical expression 10, and various combinations thereof may be possible, and priority groups #0 to #3 can be as shown in Tables 21 to 24 below based on Tables 17 to 20.

[0319] [Equation 10]

[0320]

[0321]

[0322] [Table 21]

[0323]

[0324]

[0325] Referring to Table 21, priorities can be determined based on whether AI CSI reporting is aperiodic or semi-periodic. Furthermore, priorities can be determined based on whether AI CSI reporting is performed over PUSCH or PUCCH. Furthermore, priorities can be determined based on whether reporting is based on MAC CE / RRC. For example, Table 21 may be an example of a combination determined based on the above-described information, with a smaller y value indicating a higher priority. However, the combinations may not be limited to those in Table 21.

[0326] [Table 22]

[0327]

[0328]

[0329]

[0330] Table 22 is an example of determining priority group #1. Referring to Table 22, it may be a priority group corresponding to AI CSI reports including AI CSI contents in Tables 15 and 16. Examples of possible combinations for each priority group may be as shown in Table 22, but are not limited thereto. For example, in Equation 10, the value K may represent the number of k values ​​in Table 22. For example, if three priorities are determined when k = 0, 1, and 2, K in Equation 10 may be 3. As described above, y may be given priority over k.

[0331] Priorities within a priority group may be formed by combining some of the new AI CSI content in Tables 15 and 16, but this is merely an example and may not be limiting. Specifically, priority groups may be determined based on a combination of each piece of information within the AI ​​CSI content.

[0332] [Table 23]

[0333]

[0334]

[0335] As another example, priority group #2 may be a priority group corresponding to a serving cell index where AI-CSI reporting is performed, and the possible combinations for each priority group may be as shown in Table 23, but may not be limited thereto.

[0336] [Table 24]

[0337]

[0338]

[0339] As another example, priority group #3 may be a priority group corresponding to ID information related to one or more settings and parameters associated with AI-CSI reporting, and the possible combinations for each priority group may be as shown in Table 24.

[0340] The above-described priority method can determine the highest priority element based on the AI ​​CSI reporting method and channel, and then determine the priority based on whether AI CSI content is included in the AI ​​CSI report. The priority can then be determined based on the serving cell index. For example, the priorities can be determined in descending order of serving cell index, with the PCell having the highest priority. In other words, the PCell with the lowest serving cell index can be given the highest priority, and in the case of AI CSI reporting, priority can be determined in the case of serving cell index conflict.

[0341] Finally, if priorities #0 through #2 all have the same priority, the priority can determine the final AI CSI to be reported through a combination of at least one piece of ID information related to the AI-CSI report. In other words, a priority order can exist for each priority group, and based on this, priority can be determined and CSI reporting can be performed in the event of an AI CSI report conflict.

[0342] As another example, priorities between priority groups can be determined in different ways. Specifically, the AI ​​CSI reporting method and channel corresponding to the aforementioned y value can determine the priority for the final AI CSI report based solely on the remaining priority group information, without considering the priority for AI CSI reporting. This can be as shown in Equation 11 and Tables 25 and 26.

[0343] [Equation 11]

[0344]

[0345] [Table 25]

[0346]

[0347]

[0348]

[0349] Table 25 is an example of what Priority Group #1 determines. Referring to Table 25, this may be a priority group corresponding to AI CSI reporting that includes AI CSI content within Tables 15 and 16.

[0350] [Table 26]

[0351]

[0352]

[0353] As another example, priority group #2 may be a priority group corresponding to a serving cell index where AI-CSI reporting is performed, and the possible combinations for each priority group may be as in Table 26, but may not be limited thereto.

[0354] [Table 27]

[0355]

[0356]

[0357] As another example, priority group #3 may be a priority group corresponding to ID information related to one or more settings and parameters associated with AI-CSI reporting, and the possible combinations for each priority group may be as shown in Table 27.

[0358] As another example, in the above-described mathematical expression 10, the priority may be determined based on the index of the serving cell without considering y and k. That is, the priority may be determined based on the index of the serving cell (priority group #2) where the AI ​​CSI reporting is performed, without considering the AI ​​CSI reporting method and channel corresponding to the y value and the AI ​​CSI content corresponding to the k value. For example, the priority for AI CSI reporting may have the highest priority in reporting through the PCell, but may not be limited thereto.

[0359] For example, if different AI CSI reports collide in the same time slot and at least one of the same carriers (i.e., time overlap occurs), the terminal may transmit the AI ​​CSI report with a higher priority and drop or delay transmission of the AI ​​CSI report with a lower priority to another time slot.

[0360] As another example, if multiplexing between AI CSI reports is possible, AI CSI reports can be multiplexed together. Here, when AI CSI content is multiplexed within a single CSI report, the priority can be improved upon by utilizing existing CSI content prioritization methods and is not limited to a specific format.

[0361] For example, in Equation 10, c may not be considered, and the priority may be determined in the order of y, k, and s. That is, the priority may be determined based on y, k, and s corresponding to the remaining priorities, without considering the AI ​​CSI reporting method (e.g., serving cell index) corresponding to the c value. In the case where the priority is determined based on the priority levels y, k, and s, it may be as in Equation 12 below, but may not be limited thereto.

[0362] [Equation 12]

[0363]

[0364]

[0365] As another example, in Equation 10, k may not be considered, and the priority may be determined in the order of y, c, and s. That is, the priority may be determined based on y, c, and s corresponding to the remaining priorities, without considering the AI ​​CSI reporting method (e.g., AI CSI content) corresponding to the k value. In the case where the priority is determined based on the priority levels y, c, and s, it may be as in Equation 13 below, but may not be limited thereto.

[0366] [Equation 13]

[0367]

[0368]

[0369] As another example, in Equation 10, y may not be considered, and the priority may be determined in the order of k, c, and s. That is, the priority may be determined based on k, c, and s corresponding to the remaining priorities, without considering the AI ​​CSI reporting method corresponding to the y value (e.g., the combination between the AI-CSI reporting method and the associated physical channel). In the case where the priority is determined based on the priority levels k, c, and s, it may be as in Equation 14 below, but may not be limited thereto.

[0370] [Equation 14]

[0371]

[0372]

[0373] That is, in Equation 10, the priority can be determined by considering only three variables: "y, k, c", "y, k, s", "y, c, s", or "k, c, s". For variables excluded from Equation 10, the corresponding items may be excluded, but may not be limited to a specific form.

[0374] As another example, in mathematical expression 10, the priority may be determined by considering only two variables, "y, k", "y, c", "y, s", "k, c", "k, s" or "c, s", and may be as in mathematical expressions 15 to 20 below. For variables excluded from mathematical expression 10, the corresponding items may be excluded, but may not be limited to the following form.

[0375] [Equation 15]

[0376]

[0377] [Equation 16]

[0378]

[0379] [Equation 17]

[0380]

[0381] [Equation 18]

[0382]

[0383] [Equation 19]

[0384]

[0385] [Equation 20]

[0386]

[0387]

[0388] As another example, in Equation 10, priority can be determined by considering only one variable, "y", "k", "c", or "s". For variables excluded from Equation 10, the corresponding items may be excluded, but may not be limited to a specific form.

[0389] As another example, the priorities of existing CSI reports and AI CSI reports can be determined based on the prioritization method of existing CSI reports. As described above, existing CSI reports can take precedence over AI CSI reports, and both existing CSI and AI CSI reports can have their own priorities determined.

[0390] Here, for example, existing CSI reports and AI CSI reports may be prioritized based on the same priority application. As a specific example, when priorities for existing CSI reports and AI CSI reports are determined together, the priorities may be determined based on Equation 21 below. Here, existing CSI and AI CSI may be considered together in the priority group, as shown in Tables 28 to 33.

[0391] [Equation 21]

[0392]

[0393]

[0394] [Table 28]

[0395]

[0396]

[0397] For example, referring to Table 28, priority group #0 may be prioritized based on whether the CSI reporting method is aperiodic, semi-static, or periodic. Furthermore, priority may be determined based on whether reporting is via PUSCH or PUCCH in Table 28. Specifically, aperiodic CSI reporting may have the highest priority, followed by semi-static CSI reporting and periodic CSI reporting. Furthermore, among reporting via PUSCH and PUCCH, reporting via PUSCH may have priority, and lastly, between existing CSI and AI CSI, existing CSI may have priority, and the configuration may be as shown in Table 28 using the above-described method.

[0398] Here, referring to Table 29 as an example, there may be different priority schemes for priority group #0. Referring to Table 29, the priority may be given highest priority depending on whether it is a conventional CSI report or an AI-CSI report. Furthermore, aperiodic CSI reports may have the highest priority, followed by semi-static CSI reports and periodic CSI reports. Finally, among reports via PUSCH and PUCCH, PUSCH reports may have priority, and based on this, the structure may be configured as shown in Table 29 below. However, this is merely an example and may not be limited thereto.

[0399] [Table 29]

[0400]

[0401]

[0402] In addition, priority group #1 may be as shown in Table 30 below. Here, the priority value K between some pieces of information among the AI ​​CSI contents in Tables 15 and 16 described above may mean the number of k values ​​in Table 30. For example, if the priorities are determined to be 3 such that k = 0, 1, and 2, K in Equation 12 may be 3, and the same method may be applied below.

[0403] As another example, priority group #1 may be as shown in Table 31 below. Here, the priority value K between some pieces of information among the AI ​​CSI contents in Tables 15 and 16 described above may mean the number of k values ​​in Table 30. However, Table 31 may be different from Table 30 in that a CSI report including existing CSI contents may have priority over a CSI report including AI CSI contents, but may not be limited thereto.

[0404] [Table 30]

[0405]

[0406]

[0407] [Table 31]

[0408]

[0409]

[0410] Additionally, Table 32 may be a priority group corresponding to a serving cell index for which CSI reporting is performed as priority group #2, and the possible combinations for each priority group may be as in Table 32, but may not be limited thereto.

[0411] [Table 32]

[0412]

[0413]

[0414] Additionally, priority group #3 may be a priority group corresponding to ID information related to one or more configurations and parameters associated with CSI reporting, and the possible combinations for each priority group may be as shown in Table 33. Referring to Table 33, s may have the highest priority of "CSI reportConfigID", and the priorities may be determined in the following order.

[0415] [Table 33]

[0416]

[0417]

[0418] For example, regarding CSI reporting, AI CSI reporting can be added to the existing CSI prioritization method based on the AI ​​CSI parameters and content in Tables 15 and 16 related to AI CSI reporting to determine the overall CSI reporting priority. This allows existing CSI reports to be applied in the same way as the existing prioritization method, while applying a new priority to the new AI-CSI report to ensure effective CSI reporting between them.

[0419] For example, if the CSI reporting priority is determined based on the above, the terminal may perform a CSI report with a high priority and drop a CSI report with a low priority or delay transmission to another time slot.

[0420] As another example, if multiplexing between CSI reports is possible, the CSI reports can be multiplexed together. Here, when CSI content is multiplexed within a single CSI report, the priority can be assumed to be the same as the existing CSI content, and the prioritization method for existing CSI reports can be improved and utilized, but is not limited thereto.

[0421]

[0422] As another example, if there is at least one symbol or more overlap in the time axis between a PUSCH transmitting an UL-SCH (i.e., a PUSCH carrying uplink data) and a PUSCH transmitting semi-persistent CSI (SP-CSI) on the same carrier, the UE may give priority to transmitting the PUSCH transmitting the UL-SCH.

[0423] Here, when the aforementioned AI CSI report is additionally configured, the terminal may perform an operation based on the AI ​​CSI report in the case of at least one symbol or more overlap in the time domain. More specifically, three PUSCHs on the same carrier may overlap in the time domain for at least one OFDM symbol or more, and the three CSIs may be as follows.

[0424]

[0425] ● 1st PUSCH carrying SP-CSI (Semi-persistent CSI report)

[0426] ● 2nd PUSCH carrying SP-AI CSI (Semi-persistent AI CSI report)

[0427] ● 3rd PUSCH carrying UL-SCH (UL Data) scheduled by DCI

[0428]

[0429] For example, the 1st PUSCH, the 2nd PUSCH, and the 3rd PUSCH described above may overlap in at least one symbol or more symbols on the same carrier and time domain. Here, the PUSCH transmission start having the earliest OFDM symbol among the three PUSCHs described above is after the last symbol of the DCI (downlink control information) transmission (i.e., the physical downlink control channel (PDCCH)) that schedules the corresponding PUSCH. If the transmission does not start earlier than the dog symbol, the terminal may not perform PUSCH transmission including SP SCI. That is, the terminal may transmit the 2nd PUSCH and the 3rd PUSCH.

[0430] As another example, the PUSCH transmission start with the earliest OFDM symbol among the three PUSCHs described above may be preceded by a DCI transmission scheduling the corresponding PUSCH (i.e., after the last symbol of the PDCCH). If the transmission does not start earlier than the dog symbol, the terminal may not perform any PUSCH transmission including the CSI report. That is, the terminal may transmit only the 3rd PUSCH.

[0431] Here, The values ​​are associated with the 1st PUSCH, 2nd PUSCH and 3rd PUSCH. can be determined as the maximum value among them. If only DMRS is allocated to the first OFDM symbol for each PUSCH transmission, = 0. On the other hand, if only DMRS is not allocated to the first OFDM symbol for each PUSCH transmission, = can be determined as 1. Also, depending on the UE's capability to process reports associated with AI CSI operations. The value can be defined as any other value and is not limited to a specific form. For example, if the above-described time requirement is not met, the terminal may determine it as an error case.

[0432] As another example, we can consider a case where two PUSCHs on the same carrier overlap in the time domain for at least one OFDM symbol or more. Here, the two PUSCHs may be as follows.

[0433] ● 1st PUSCH carrying SP-AI CSI (Semi-persistent AI CSI report)

[0434] ● 2nd PUSCH carrying UL-SCH (UL Data) scheduled by DCI

[0435]

[0436] That is, unlike the above, PUSCH transmission including SP CSI reporting may not be performed.

[0437] For example, the 1st PUSCH and the 2nd PUSCH described above may overlap in at least one symbol or more symbols on the same carrier and time domain. Here, the PUSCH transmission start having the earliest OFDM symbol among the two PUSCHs described above is after the last symbol of the DCI transmission (i.e., PDCCH) that schedules the corresponding PUSCH. If the transmission does not start earlier than the 2nd symbol, the terminal may not perform a PUSCH transmission including the SP SCI. That is, the terminal may transmit only the 2nd PUSCH.

[0438] Here, The values ​​are associated with the 1st PUSCH and 2nd PUSCH. can be determined as the maximum value among them. If only DMRS is allocated to the first OFDM symbol for each PUSCH transmission, = 0. On the other hand, if only DMRS is not allocated to the first OFDM symbol for each PUSCH transmission, = can be determined as 1. Also, depending on the UE's capability to process reports associated with AI CSI operations. The value can be defined as any other value and is not limited to a specific form. For example, if the above-described time requirement is not met, the terminal may determine it as an error case.

[0439] As another example, we can consider a case where two PUSCHs on the same carrier overlap in the time domain for at least one OFDM symbol or more. Here, the two PUSCHs may be as follows. Here, SP AI CSI and SP CSI reports may be determined to have priority based on the method described above.

[0440] ● 1st PUSCH carrying SP-AI CSI (Semi-persistent AI CSI report)

[0441] ● 2nd PUSCH carrying SP-CSI (Semi-persistent CSI report)

[0442]

[0443] In addition, for example, if the PUSCH carrying the UL-SCH is not scheduled by DCI and is a PUSCH (SPS PUSCH or configured PUSCH) transmission based on SP scheduling (semi-persistent scheduling), the terminal may transmit the SPS PUSCH without transmitting the PUSCH including either AI CSI or CSI, and is not limited to a specific form.

[0444] FIG. 12 is a flowchart illustrating a method for performing CSI reporting applicable to the present disclosure. Referring to FIG. 12, a terminal may trigger an AI CSI report to a base station by an instruction of dynamic signaling or an upper layer setting from an AI model server on the LCM-related base station / base station side, by a request from an AI model server on the base station / terminal side, or by the terminal itself (S1210). Here, the AI ​​CSI report may refer to a report performed to the base station related to AI / ML-based CSI compression / CSI prediction or other learning / inference / monitoring / testing, and is not limited to a specific form. Here, if the AI ​​CSI report and the existing CSI report conflict (S1220), the terminal may give priority to the existing CSI report over the AI ​​CSI report (S1230). For example, the existing CSI report may be real-time channel information, and considering that the AI ​​CSI report is a learning-related report, the existing CSI report may be given priority. For example, if a traditional CSI report takes precedence, AI CSI reports may be dropped or performed after a preset delay. Furthermore, if multiplexing is possible, traditional CSI and AI CSI may be multiplexed, and may not be limited to a specific format.

[0445] If the AI ​​CSI report and the existing CSI report do not conflict (S1220), the terminal can check whether a conflict occurs between multiple AI CSI reports (S1240). Here, if a conflict does not occur between the AI ​​CSI reports, the AI ​​CSI report can be performed (S1250). On the other hand, if a conflict occurs between the AI ​​CSI reports, the terminal can perform the AI ​​CSI report operation based on the priority of the AI ​​CSI report (S1260). For example, the AI ​​CSI report may be a first type CSI report, and the existing CSI report may be a second type CSI report. The terminal can check whether the second type CSI report overlaps in at least one symbol in which the first type CSI report is performed, and if the first type CSI report and the second type CSI report overlap, the second type CSI report can be performed with priority over the first type CSI report. If the Type 1 CSI report and the Type 2 CSI report do not overlap, it is checked whether multiple Type 1 CSI reports overlap in at least one symbol, and if multiple Type 1 CSI reports overlap, the Type 1 CSI report can be performed based on the priority of the Type 1 CSI report. Here, the priority of the Type 1 CSI report can be determined based on at least one of the CSI report method, the CSI report channel, the CSI content, the serving cell index, and the report configuration ID, as described above.

[0446] Here, a collision means that the original CSI report and the AI ​​CSI report overlap at least on the same OFDM symbol, and a single CSI report cannot contain both the original CSI content and the AI ​​CSI content. Otherwise, a collision-free means that all possible CSI content is transmitted within a single CSI report, regardless of whether the original CSI report and the AI ​​CSI report overlap on the same OFDM symbol.

[0447] FIG. 13 is a flowchart illustrating a method for performing CSI reporting applicable to the present disclosure. Referring to FIG. 13, a terminal may trigger a CSI report to a base station (S1310). Here, the CSI report may include a first type CSI report and a second type CSI report. For example, the first type CSI report may be an AI CSI report, and the second type CSI report may be a conventional CSI report. The terminal may perform the CSI report based on the priority of the CSI report (S1320, S130). Here, the priority of the CSI report may be determined by considering both the first type CSI report and the second type CSI report. For example, the priority of the CSI report may be determined based on at least one of a CSI reporting method, a CSI reporting channel, CSI content, a serving cell index, and a report configuration ID. For example, a CSI report with a lower priority may be dropped or reported after a preset delay. Additionally, where multiplexing is possible, CSI reports may be multiplexed and may not be limited to a particular format.

[0448] Figure 14 is a drawing showing a base station device and a terminal device to which the present disclosure can be applied.

[0449] The base station device (1400) may include a processor (1420), an antenna unit (1412), a transceiver (1414), and a memory (1416).

[0450] The processor (1420) performs baseband-related signal processing and may include a higher layer processing unit (1430) and a physical layer processing unit (1440). The higher layer processing unit (1430) may process operations of a MAC (Medium Access Control) layer, an RRC (Radio Resource Control) layer, or higher layers. The physical layer processing unit (1440) 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 (1420) may also control the overall operation of the base station device (1400).

[0451] The antenna unit (1412) 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.

[0452] The memory (1416) can store information processed by the processor (1420), software related to the operation of the base station device (1400), an operating system, applications, etc., and may also include components such as a buffer.

[0453] The processor (1420) of the base station (1400) may be configured to implement the operations of the base station in the embodiments described in the present invention.

[0454] The terminal device (1450) may include a processor (1470), an antenna unit (1462), a transceiver (1464), and a memory (1466). For example, the terminal device (1450) in the present invention may communicate with a base station device (1400). As another example, the terminal device (1450) in the present invention may perform sidelink communication with another terminal device. That is, the terminal device (1450) in the present invention refers to a device that can communicate with at least one of the base station device (1400) and another terminal device, and is not limited to communication with a specific device.

[0455] The processor (1470) performs baseband-related signal processing and may include a higher layer processing unit (1480) and a physical layer processing unit (1490). The higher layer processing unit (1480) may process operations of the MAC layer, the RRC layer, or higher layers. The physical layer processing unit (1490) 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 (1470) may also control the overall operation of the terminal device (1450).

[0456] The antenna unit (1462) 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.

[0457] The memory (1466) can store information processed by the processor (1470), software related to the operation of the terminal device (1450), an operating system, applications, etc., and may also include components such as a buffer.

[0458] A terminal device (1450) according to an example of the present invention may be associated with a vehicle. For example, the terminal device (1450) may be integrated into, positioned in, or positioned on the vehicle. Furthermore, the terminal device (1450) according to the present invention may be the vehicle itself. Furthermore, the terminal device (1450) 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 (1450) 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.

[0459] 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 (1450) 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.

[0460] In addition, a terminal device (1450) according to an example of the present invention can trigger an AI CSI report to a base station device (1400). Here, the AI ​​CSI report may refer to a report that performs AI / ML-based CSI compression or other learning-related reporting to the base station device (1400), and is not limited to a specific form. If an AI CSI report conflicts with an existing CSI report, the terminal device (1450) may give priority to the existing CSI report over the AI ​​CSI report. For example, the existing CSI report may be given priority considering that the existing CSI report may be real-time channel information, and the AI ​​CSI report is a learning-related report. If the existing CSI report is given priority, the AI ​​CSI report may be dropped or reported after a preset delay. In addition, if multiplexing is possible, the existing CSI and AI CSI may be multiplexed, and may not be limited to a specific form.

[0461] If the AI ​​CSI report and the existing CSI report do not conflict, the terminal device (1450) can check whether a conflict occurs between multiple AI CSI reports. Here, if a conflict does not occur between the AI ​​CSI reports, the AI ​​CSI report can be performed. On the other hand, if a conflict occurs between the AI ​​CSI reports, the terminal device (1450) can perform the AI ​​CSI report operation based on the priority of the AI ​​CSI report. For example, the AI ​​CSI report can be a first type CSI report, and the existing CSI report can be a second type CSI report. The terminal can check whether the second type CSI report overlaps in at least one symbol in which the first type CSI report is performed, and if the first type CSI report and the second type CSI report overlap, the second type CSI report can be performed with priority over the first type CSI report. If the Type 1 CSI report and the Type 2 CSI report do not overlap, it is checked whether multiple Type 1 CSI reports overlap in at least one symbol, and if multiple Type 1 CSI reports overlap, the Type 1 CSI report can be performed based on the priority of the Type 1 CSI report. Here, the priority of the Type 1 CSI report can be determined based on at least one of the CSI report method, the CSI report channel, the CSI content, the serving cell index, and the report configuration ID, as described above.

[0462] As another example, the terminal device (1450) may trigger a CSI report to the base station device (1400). Here, the CSI report may include a first type CSI report and a second type CSI report. For example, the first type CSI report may be an AI CSI report, and the second type CSI report may be a conventional CSI report. The terminal device (1450) may perform the CSI report based on the priority of the CSI report. Here, the priority of the CSI report may be determined by considering both the first type CSI report and the second type CSI report. For example, the priority of the CSI report may be determined based on at least one of a CSI report method, a CSI report channel, CSI content, a serving cell index, and a report configuration ID. For example, a CSI report with a lower priority may be dropped or reported after a preset delay. In addition, if multiplexing is possible, the CSI reports may be multiplexed and may not be limited to a specific form.

[0463] 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.

[0464] 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.

[0465] 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.

[0466]

[0467]

[0468] The above may also apply to other systems.

Claims

1. In the method, A step in which a terminal triggers channel state information (CSI) reporting; A step in which the terminal determines CSI reporting based on priority; and A method comprising the step of performing the determined CSI report.

2. In paragraph 1, When the terminal triggers a first type CSI report, it is checked whether a second type CSI report overlaps at least one symbol in which the first type CSI report is performed, A method in which, when the first type CSI report and the second type CSI report overlap, the second type CSI report is performed with priority over the first type CSI report.

3. In paragraph 2, A method in which, if the above-mentioned second type CSI report takes precedence, the above-mentioned first type CSI report is dropped or reported after a preset delay.

4. In paragraph 2, If the first type CSI report and the second type CSI report do not overlap, it is checked whether a plurality of first type CSI reports overlap in at least one symbol, A method for performing a first type CSI report based on the priority of the first type CSI report when the plurality of first type CSI reports overlap.

5. In paragraph 4, A method wherein the priority of the first type CSI report is determined based on at least one of a CSI reporting method, a CSI reporting channel, a CSI content, a serving cell index, and a report configuration ID.

6. In paragraph 1, The above terminal determines the CSI report to be reported based on the priority of the CSI report, A method wherein the CSI report includes a first type CSI report and a second type CSI report, and the priority of the CSI report is determined by considering the first type CSI report and the second type CSI report together.

7. In paragraph 5, A method wherein the priority of the above CSI report is determined based on at least one of a CSI report method, a CSI report channel, a CSI content, a serving cell index, and a report configuration ID.

8. In paragraph 1, A method wherein the first type CSI report is an AI CSI report and the second type CSI report is a wireless channel related CSI report.

9. 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: Triggering channel state information (CSI) reporting, The above terminal determines CSI reporting based on priority, and A device for performing the above-determined CSI report.

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