Apparatus and method for performing scheduling of terminals in wireless communication system, and storage medium

Abstract

This apparatus of a digital unit (DU) may comprise at least one processor and a memory for storing instructions. When executed individually or collectively by the at least one processor, the instructions can cause the apparatus to: acquire channel state information (CSI) of each of user equipments (UEs) including a first UE and a second UE; acquire channel information about a sounding reference signal (SRS) of the first UE; generate compressed channel information by using a first basis vector acquired from first CSI of the first UE and the channel information; calculate correlation information between the first UE and the second UE by using the compressed channel information and a second basis vector acquired from second CSI of the second UE; select, on the basis of the calculated correlation information, UEs to be scheduled including the first UE among the UEs; and transmit downlink data to the selected UEs according to precoding information determined with respect to the selected UEs.

Description

Device, method, and storage medium for scheduling terminals in a wireless communication system

[0001] The following descriptions relate to a wireless communication system, and more specifically, to a device, method, and storage medium for scheduling user equipment in a wireless communication system.

[0002] To improve signal transmission and reception performance, multiple-input multiple-output (MIMO) technology is used. Wireless communication systems utilizing MIMO technology utilize multiple antennas at both the transmitter and receiver. The channel capacity of a wireless communication system utilizing MIMO technology can be significantly improved compared to single-antenna technology.

[0003] The above information may be provided as background art to aid in understanding the present disclosure. No claim or determination is made as to whether any of the above is applicable as prior art related to the present disclosure.

[0004] A device of a digital unit (DU) may include at least one processor including a processing circuit. The device may include a memory storing instructions and including one or more storage media. The instructions, when individually or collectively executed by the at least one processor, may cause the device to obtain channel state information (CSI) of each of UEs including a first user equipment (UE) and a second UE. The instructions, when individually or collectively executed by the at least one processor, may cause the device to obtain channel information for a sounding reference signal (SRS) of the first UE. The instructions, when individually or collectively executed by the at least one processor, may cause the device to generate compressed channel information using a first basis vector obtained from the first CSI of the first UE and the channel information. The instructions, when individually or collectively executed by the at least one processor, may cause the device to calculate correlation information between the first UE and the second UE using the compressed channel information and a second basis vector obtained from the second CSI of the second UE. The instructions, when individually or collectively executed by the at least one processor, may cause the device to select UEs to be scheduled, including the first UE, from among the UEs based on the calculated correlation information.The above instructions, when individually or collectively executed by the at least one processor, may cause the device to transmit downlink data to the selected UEs via a radio unit (RU) according to precoding information determined for the selected UEs.

[0005] A method performed by a digital unit (DU) may include an operation of acquiring channel state information (CSI) of each of UEs including a first user equipment (UE) and a second UE. The method may include an operation of acquiring channel information for a sounding reference signal (SRS) of the first UE. The method may include an operation of generating compressed channel information using a first basis vector acquired from the first CSI of the first UE and the channel information. The method may include an operation of calculating correlation information between the first UE and the second UE using the compressed channel information and a second basis vector acquired from the second CSI of the second UE. The method may include an operation of selecting UEs to be scheduled, including the first UE, from among the UEs based on the calculated correlation information. The method may include an operation of transmitting downlink data to the selected UEs through a radio unit (RU) according to precoding information determined for the selected UEs.

[0006] A non-transitory computer-readable storage medium may store one or more programs comprising instructions that, when individually or collectively executed by at least one processor of a device of a digital unit (DU), cause the device to obtain channel state information (CSI) of each of UEs including a first user equipment (UE) and a second UE. The non-transitory computer-readable storage medium may store one or more programs comprising instructions that, when individually or collectively executed by the at least one processor, cause the device to obtain channel information for a sounding reference signal (SRS) of the first UE. The non-transitory computer-readable storage medium may store one or more programs comprising instructions that, when individually or collectively executed by the at least one processor, cause the device to generate compressed channel information using a first basis vector obtained from the first CSI of the first UE and the channel information. The non-transitory computer-readable storage medium may store one or more programs including instructions that, when individually or collectively executed by the at least one processor, cause the device to calculate correlation information between the first UE and the second UE using the compressed channel information and a second basis vector obtained from the second CSI of the second UE.The non-transitory computer-readable storage medium may store one or more programs including instructions that, when individually or collectively executed by the at least one processor, cause the device to select UEs to be scheduled, including the first UE, from among the UEs based on the calculated correlation information. The non-transitory computer-readable storage medium may store one or more programs including instructions that, when individually or collectively executed by the at least one processor, cause the device to transmit downlink data to the selected UEs via a radio unit (RU) according to precoding information determined for the selected UEs.

[0007] Figure 1 illustrates an example of a wireless communication system.

[0008] Figure 2a illustrates an example of network entities according to distributed deployment.

[0009] Figure 2b illustrates an example of function splitting of network entities.

[0010] Figure 3 illustrates an example of a functional configuration of an electronic device.

[0011] Figure 4 illustrates an example of a resource structure in the time domain and frequency domain.

[0012] Figure 5a shows examples of Type 1 codebook and Type 2 codebook designs.

[0013] Figure 5b illustrates an example of a spatial beam grid associated with antenna elements.

[0014] Figure 6 illustrates an example of a signal flow for a method in which a base station calculates correlation information between terminals and performs downlink transmission.

[0015] Figure 7 illustrates an example of a method for calculating correlation information between terminals using a sounding reference signal (SRS) and a precoding matrix indicator (PMI).

[0016] Figure 8a illustrates an example of an operational flow for a method of calculating correlation information using basis vectors of terminals.

[0017] Figure 8b shows an example of a table and graph representing values ​​of correlation information.

[0018] Figures 9a and 9b illustrate an example of a method for generating compressed channel information of an SRS using basis vectors extracted according to PMI parsing.

[0019] Figure 10 illustrates an example of a method for generating compressed channel information of terminals having a unified dimension.

[0020] Figures 11a and 11b illustrate examples of MU (multi-user) MIMO (multiple input multiple output) scheduling.

[0021] Figure 12 illustrates an example of a method for sorting terminals.

[0022] Figure 13 illustrates an example of an operational flow for a method in which a DU (digital unit) calculates correlation information between terminals and performs downlink transmission.

[0023] The terms used in this disclosure are used only to describe specific embodiments and may not be intended to limit the scope of other embodiments. The singular expression may include plural expressions unless the context clearly indicates otherwise. Terms used herein, including technical or scientific terms, may have the same meaning as commonly understood by those of ordinary skill in the art described in this disclosure. Terms defined in general dictionaries among the terms used in this disclosure may be interpreted as having the same or similar meaning in the context of the relevant technology, and shall not be interpreted in an idealized or overly formal sense unless explicitly defined in this disclosure. In some cases, even if a term is defined in this disclosure, it cannot be interpreted to exclude embodiments of the present disclosure.

[0024] The various embodiments of the present disclosure described below illustrate a hardware-based approach as an example. However, since the various embodiments of the present disclosure include techniques utilizing both hardware and software, the various embodiments of the present disclosure do not exclude a software-based approach.

[0025] In the following description, terms referring to signals (e.g., packet, message, signal, information, signaling), terms referring to resources (e.g., section, symbol, slot, subframe, radio frame, subcarrier, RE (resource element), RB (resource block), BWP (bandwidth part), occasion), terms for operational states (e.g., step, operation, procedure), terms referring to data (e.g., packet, message, user stream, information, bit, symbol, codeword), terms referring to channels, terms referring to network entities (distributed unit (DU), radio unit (RU), central unit (CU), CU-CP (control plane), CU-UP (user plane), O-DU (O-RAN (open radio access network) DU), O-RU (O-RAN RU), O-CU (O-RAN Terms such as CU), O-CU-UP (O-RAN CU-CP), O-CU-CP (O-RAN CU-CP)), and components of the device are provided for convenience of explanation. Therefore, the present disclosure is not limited to the terms described below, and other terms having equivalent technical meanings may be used. In addition, terms such as '...part', '...machine', '...object', and '...body' used below may mean at least one shape structure or a unit that processes a function.

[0026] In addition, in the present disclosure, expressions such as "more than" or "less than" may be used to determine whether a specific condition is satisfied or fulfilled, but this is merely a description for expressing an example and does not exclude descriptions such as "more than" or "less than." A condition described as "more than" may be replaced with "more than," a condition described as "less than" may be replaced with "less than," and a condition described as "more than and less than" may be replaced with "more than and less than." In addition, hereinafter, "A" to "B" mean at least one of elements from A (including A) to B (including B). hereinafter, "C" and / or "D" mean at least one of "C" or "D," that is, including {"C", "D", "C" and "D"}.

[0027] Although this disclosure describes embodiments using terminology used in certain communication standards (e.g., 3rd Generation Partnership Project (3GPP)), this is merely an example for illustrative purposes. Embodiments of this disclosure can also be applied to other communication and broadcasting systems.

[0028] Figure 1 illustrates an example of a wireless communication system.

[0029] Referring to FIG. 1, FIG. 1 illustrates a base station (110) and a terminal (120) as some of the nodes utilizing a wireless channel in a wireless communication system. Although FIG. 1 illustrates only one base station, the wireless communication system may further include other base stations identical or similar to the base station (110).

[0030] The base station (110) is a network infrastructure that provides wireless access to the terminal (120). The base station (110) has coverage defined based on the distance at which a signal can be transmitted. In addition to the base station, the base station (110) may be referred to as an 'access point (AP)', 'eNodeB (eNB)', '5th generation node', 'next generation nodeB (gNB)', 'wireless point', 'transmission / reception point (TRP)', or other terms having equivalent technical meanings.

[0031] The terminal (120) is a device used by a user and communicates with the base station (110) via a wireless channel. The link from the base station (110) to the terminal (120) is referred to as a downlink (DL), and the link from the terminal (120) to the base station (110) is referred to as an uplink (UL). In addition, although not shown in FIG. 1, the terminal (120) and another terminal may communicate with each other via a wireless channel. In this case, the link between the terminal (120) and another terminal (device-to-device link, D2D) is referred to as a sidelink, and the sidelink may be used interchangeably with the PC5 interface. In some other embodiments, the terminal (120) may be operated without the involvement of a user. In one embodiment, the terminal (120) is a device that performs machine type communication (MTC) and may not be carried by the user. Additionally, according to one embodiment, the terminal (120) may be an NB (narrowband)-IoT (internet of things) device.

[0032] The terminal (120) may be referred to as a terminal, or other terms such as 'user equipment (UE),' 'customer premises equipment (CPE),' 'mobile station,' 'subscriber station,' 'remote terminal,' 'wireless terminal,' 'electronic device,' or 'user device,' or other terms having equivalent technical meanings.

[0033] The base station (110) and the terminal (120) can perform beamforming. The base station (110) and the terminal (120) can transmit and receive wireless signals in a relatively low frequency band (e.g., FR 1 (frequency range 1) of NR). In addition, the base station (110) and the terminal (120) can transmit and receive wireless signals in a relatively high frequency band (e.g., FR 2 (or, FR 2-1, FR 2-2, FR 2-3), FR 3 of NR), millimeter wave (mmWave) band (e.g., 28 GHz, 30 GHz, 38 GHz, 60 GHz)). To improve channel gain, the base station (110) and the terminal (120) can perform beamforming. Here, the beamforming can include transmission beamforming and reception beamforming. The base station (110) and the terminal (120) can impart directionality to the transmitted or received signal. To this end, the base station (110) and the terminal (120) can select serving beams through a beam search or beam management procedure. After the serving beams are selected, subsequent communication can be performed through resources that have a QCL relationship with the resource that transmitted the serving beams.

[0034] If large-scale characteristics of a channel carrying a symbol on a first antenna port can be inferred from a channel carrying a symbol on a second antenna port, the first antenna port and the second antenna port can be evaluated to have a QCL relationship. For example, the large-scale characteristics may include at least one of delay spread, Doppler spread, Doppler shift, average gain, average delay, and a spatial receiver parameter.

[0035] Although both the base station (110) and the terminal (120) are described as performing beamforming in FIG. 1, the embodiments of the present disclosure are not necessarily limited thereto. In some embodiments, the terminal may or may not perform beamforming. Furthermore, the base station may or may not perform beamforming. That is, either only one of the base station and the terminal may perform beamforming, or neither the base station nor the terminal may perform beamforming.

[0036] In the present disclosure, a beam refers to a spatial flow of a signal in a wireless channel, and is formed by one or more antennas (or antenna elements), and this forming process may be referred to as beamforming. Beamforming may include at least one of analog beamforming and digital beamforming (e.g., precoding). Reference signals transmitted based on beamforming may include, for example, a demodulation-reference signal (DM-RS), a channel state information-reference signal (CSI-RS), a synchronization signal / physical broadcast channel (SS / PBCH), and a sounding reference signal (SRS). In addition, as a configuration for each reference signal, an IE such as a CSI-RS resource or an SRS-resource may be used, and this configuration may include information associated with the beam. Information associated with a beam may mean whether the configuration (e.g., a CSI-RS resource) uses the same spatial domain filter as another configuration (e.g., another CSI-RS resource within the same CSI-RS resource set) or a different spatial domain filter, or whether it is quasi-co-located (QCL) with a reference signal, and if so, what type it is (e.g., QCL type A, B, C, D).

[0037] In communication systems with relatively large cell radius of base stations, each base station was installed to include the functions of a digital processing unit (or distributed unit (DU)) and a radio frequency (RF) processing unit (or radio unit (RU)). However, as higher frequency bands are used in 4G (4th generation) and / or subsequent communication systems (e.g., 5G) and the cell coverage of base stations becomes smaller, the number of base stations to cover a specific area has increased. The burden of installation costs on operators for installing base stations has also increased. In order to minimize the installation cost of base stations, a structure has been proposed in which the digital unit (DU) and radio unit (RU) (or massive multiple input multiple output unit (MMU)) of the base station are separated, one or more RUs are connected to one DU through a wired network, and one or more RUs are geographically distributed to cover a specific area. Below, the layout structure and expansion examples of base stations according to various embodiments of the present disclosure are described through FIG. 2a.

[0038] Figure 2a illustrates an example of network entities according to distributed deployment.

[0039] For example, the network entities may include a digital unit (DU) (210) and a radio unit (RU) (220) (or a massive multiple input multiple output (MMU) unit). For example, the network entities may be connected via a fronthaul. Unlike the backhaul between a base station and a core network, the fronthaul refers to entities (e.g., DU (210), RU (220)) between a wireless LAN and a base station. Although FIG. 2A illustrates an example of a fronthaul structure between a DU (210) and one RU (220), this is merely for convenience of explanation and the present disclosure is not limited thereto. In other words, embodiments of the present disclosure may also be applied to a fronthaul structure between one DU and multiple RUs. For example, embodiments of the present disclosure may be applied to a fronthaul structure between one DU and two RUs. Additionally, the embodiments of the present disclosure can also be applied to a fronthaul structure between one DU and three RUs.

[0040] Referring to FIG. 2A, a base station (110) may include a DU (210) and a RU (220). A fronthaul (215) between the DU (210) and the RU (220) may be operated via an Fx interface. For the operation of the fronthaul (215), an interface such as an enhanced common public radio interface (eCPRI) or radio over ethernet (ROE) may be used, for example. Depending on the implementation example, the DU (210) may be referred to as a baseband unit (BBU), a digital BBU, a baseband digital unit, a digital processing unit, a digital processing circuit, a baseband processing circuit, a baseband processing unit, and / or equivalent technical terms in addition to a DU (digital unit). According to an implementation example, the RU (220) may be referred to as a remote unit, a radio demote head (RRH), a radio processing circuit, a radio processing unit, an antenna-integrated radio, an air radio device, an air scale communication device, a radio device, a radio communication device, and / or equivalent technical terms in addition to the RU (radio unit). In addition, according to an implementation example, although a network entity connected to the DU (210) in the present disclosure is described as the RU (220), it is of course possible for an MMU (massive multiple input multiple output) unit) to be connected to and used with the DU (210) instead of the RU (220).

[0041] As communication technology advances, mobile data traffic increases, significantly increasing the bandwidth requirements for the fronthaul between the digital unit and the wireless unit. In a deployment such as a centralized / cloud radio access network (C-RAN), the DU (210) performs functions for the packet data convergence protocol (PDCP), radio link control (RLC), media access control (MAC), and physical (PHY) layer, and the RU (220) can be implemented to perform functions for the PHY layer in addition to the RF (radio frequency) function.

[0042] The DU (210) may be responsible for upper layer functions of a wireless network. For example, the DU (210) may perform functions of the MAC layer and a part of the PHY layer. Here, a part of the PHY layer refers to functions performed at a higher level among the functions of the PHY layer, and may include, for example, channel encoding (or channel decoding), scrambling (or descrambling), modulation (or demodulation), and layer mapping (or layer demapping). According to an embodiment, if the DU (210) complies with the O-RAN standard, it may be referred to as an O-DU (O-RAN DU). The DU (210) may be replaced with a first network entity for a base station (e.g., gNB) in embodiments of the present disclosure, if necessary.

[0043] The RU (220) may be responsible for lower layer functions of a wireless network. For example, the RU (220) may perform a part of the PHY layer, an RF function. Here, a part of the PHY layer refers to functions of the PHY layer that are performed at a relatively lower level than the DU (210), and may include, for example, iFFT transformation (or FFT transformation), CP insertion (CP removal), and digital beamforming. The RU (220) may be referred to as an 'access unit (AU)', an 'access point (AP)', a 'transmission / reception point (TRP)', a 'remote radio head (RRH)', a 'radio unit (RU)', or other terms having an equivalent technical meaning thereto. According to an embodiment, when the RU (220) complies with the O-RAN standard, it may be referred to as an O-RU (O-RAN RU). RU (220) may be represented as a second network entity for a base station (e.g., gNB) in embodiments of the present disclosure, as needed.

[0044] In FIG. 2A, the base station (110) is described as including a DU (210) and a RU (220), but the embodiments of the present disclosure are not limited thereto. The base station according to the embodiments may be implemented in a distributed deployment according to a centralized unit (CU) configured to perform functions of upper layers of an access network (e.g., packet data convergence protocol (PDCP), radio resource control (RRC)) and a distributed unit (DU) configured to perform functions of lower layers. For example, the digital unit (DU) (210) may be implemented by separating into a centralized unit (CU) and a distributed unit (DU). Between a core (e.g., 5G core (5GC) or next generation core (NGC)) network and a radio network (RAN), the base station may be implemented in a structure in which a centralized unit (CU), a distributed unit (DU), and a radio unit (RU) are arranged in that order. The interface between the CU (centralized unit) and the DU (distributed unit) can be referred to as the F1 interface.

[0045] A centralized unit (CU) may be connected to one or more distributed units (DUs) and may be responsible for functions at a higher layer than the distributed units (DUs). For example, the CU may be responsible for functions at the radio resource control (RRC) and packet data convergence protocol (PDCP) layers, while the DU and RU may be responsible for functions at lower layers. The DU may perform some functions (high PHY) of the radio link control (RLC), media access control (MAC), and physical (PHY) layers, while the RU may be responsible for the remaining functions (low PHY) of the PHY layer. In addition, for example, a digital unit (DU) may be included in a distributed unit (DU) depending on the implementation of a distributed deployment of the base station. Hereinafter, unless otherwise defined, the operations of DU (digital unit) and RU are described, but various embodiments of the present disclosure can be applied to both a base station arrangement including a CU and an arrangement in which a DU is directly connected to a core network (i.e., a base station in which the CU and DU are integrated into a single entity (e.g., an NG-RAN node)).

[0046] Fig. 2b illustrates an example of functional splitting of network entities. The network entities of Fig. 2b may include DU (210) and RU (220) of Fig. 2a.

[0047] As wireless communication technology develops (e.g., introduction of 5G (5th generation) communication system (or, NR (new radio) communication system), the frequency band used has increased further. As the cell radius of the base station has become very small, the number of RUs that need to be installed has also increased further. In addition, in 5G communication system, the amount of data transmitted has increased by a large amount, up to ten times, so the transmission capacity of the wired network transmitted to the fronthaul has increased significantly. Due to the factors described above, the installation cost of the wired network in the 5G communication system may increase significantly. Therefore, in order to lower the transmission capacity of the wired network and reduce the installation cost of the wired network, 'function split' can be used to lower the transmission capacity of the fronthaul by transferring some of the functions of the modem of the DU to the RU. Although described as RU below, the function split described below can be equally applied not only to the RU but also to the relationship between the MMU and the DU.

[0048] To reduce the burden on the DU, the role of the RU, which is traditionally solely responsible for RF functions, can be expanded to include some physical layer functions. As the RU performs higher-layer functions, its throughput increases, which can increase transmission bandwidth in the fronthaul while reducing latency requirements due to response processing. However, as the RU performs higher-layer functions, virtualization gains decrease, and the RU's size, weight, and cost increase. Considering the trade-offs between the advantages and disadvantages described above, implementing an optimal functional separation is required.

[0049] Referring to Figure 2b, the functional separation in the physical layer below the MAC layer is illustrated. For the downlink (DL) that transmits a signal to a terminal through a wireless network, the base station can sequentially perform channel encoding / scrambling, modulation, layer mapping, antenna mapping, RE mapping, digital beamforming (e.g., precoding), iFFT transform / CP insertion, and RF transform. For the uplink (UL) that receives a signal from a terminal through a wireless network, the base station can sequentially perform RF transform, FFT transform / CP removal, digital beamforming (pre-combining), RE demapping, channel estimation, layer demapping, demodulation, and decoding / descrambling. The separation of uplink and downlink functions can be defined in various types depending on the needs of vendors, discussions in standards, etc., according to the above-mentioned trade-offs.

[0050] In the first functional separation (255), the RU performs the RF function, and the DU performs the PHY function. The first functional separation is one in which the PHY function is not substantially implemented in the RU, and may be referred to as Option 8, for example. In the second functional separation (260), the RU performs iFFT conversion / CP insertion in the DL and FFT conversion / CP removal in the UL of the PHY function, and the DU performs the remaining PHY functions. As an example, the second functional separation (260) may be referred to as Option 7-1. In the third functional separation (270a), the RU performs iFFT conversion / CP insertion in the DL and FFT conversion / CP removal and digital beamforming in the UL of the PHY function, and the DU performs the remaining PHY functions. As an example, the third functional separation (270a) may be referred to as Option 7-2x Category A. In the fourth functional separation (270b), the RU performs up to digital beamforming in both the DL and UL, and the DU performs upper PHY functions after the digital beamforming. For example, the fourth functional separation (270b) may be referred to as Option 7-2x Category B. In the fifth functional separation (275), the RU performs up to RE mapping (or RE demapping) in both the DL and UL, and the DU performs upper PHY functions after RE mapping (or RE demapping). For example, the fifth functional separation (275) may be referred to as Option 7-2. In the sixth functional separation (280), the RU performs up to modulation (or demodulation) in both the DL and UL, and the DU performs upper PHY functions after modulation (or demodulation). For example, the sixth functional separation (280) may be referred to as Option 7-3. In the seventh functional separation (290), the RU performs encoding / scrambling (or decoding / descrambling) in both the DL and UL, and the DU performs subsequent upper PHY functions up to modulation (or demodulation). For example, the seventh functional separation (290) may be referred to as Option 6.

[0051] In one embodiment, when a large amount of signal processing is expected, such as in the FR 1 MMU, functional separation at a relatively high layer (e.g., the fourth functional separation (270b)) may be required to reduce fronthaul capacity. In addition, functional separation at too high a layer (e.g., the sixth functional separation (280)) may complicate the control interface and cause a burden on the implementation of the RU due to the inclusion of a large number of PHY processing blocks within the RU. Therefore, appropriate functional separation may be required depending on the arrangement and implementation method of the DU and the RU.

[0052] In one embodiment, if the precoding of data received from the DU cannot be processed (i.e., if the precoding capability of the RU is limited), the third functional separation (270a) or a lower functional separation (e.g., the second functional separation (260)) may be applied. Conversely, if the DU has the capability to process the precoding of data received from the DU, the fourth functional separation (270b) or a higher functional separation (e.g., the sixth functional separation (280)) may be applied.

[0053] The O-RAN standard distinguishes the types of O-RUs depending on whether the precoding function is located at the interface of the O-DU or the O-RU interface. For example, the RU may perform operations according to the functional separation of the third functional separation (270a) (which may be referred to as category A (CAT-A)) or the fourth functional separation (270b) (which may be referred to as category B (CAT-B)) for performing beamforming processing. In other words, an O-RU that does not perform precoding (i.e., has low complexity) may be referred to as a CAT-A O-RU. An O-RU that performs precoding may be referred to as a CAT-B O-RU. In addition, for example, channel estimation may be performed in the O-RU instead of the O-DU. To improve uplink performance, the O-RU may also operate according to the sixth functional separation (280) (Option 7-3).

[0054] Hereinafter, the upper-PHY refers to the physical layer processing processed in the DU of the fronthaul interface. For example, the upper-PHY may include FEC encoding / decoding, scrambling, and modulation / demodulation. The lower-PHY refers to the physical layer processing processed in the RU of the fronthaul interface. For example, the lower-PHY may include FFT / iFFT, digital beamforming, PRACH (physical random access channel) extraction and filtering. However, the above-described criteria do not exclude embodiments through other functional separations. The functional configuration, signaling, or operation of FIGS. 6 to 13 described below may be applied not only to the third functional separation (270a), the fourth functional separation (270b), but also to other functional separations (e.g., the sixth functional separation (280)). For example, channel estimation may be performed in the O-RU instead of the O-DU. To improve uplink performance, the O-RU may operate according to the sixth functional separation (280) (Option 7-3).

[0055] Figure 3 illustrates an example of a functional configuration of an electronic device.

[0056] The configuration of the electronic device (300) illustrated in FIG. 3 can be understood as a configuration of a base station (110), a terminal (120), a DU (210), or an RU (220) (or an MMU). Terms such as "...unit" and "...unit" used hereinafter mean a unit that processes at least one function or operation, and this can be implemented by hardware, software, or a combination of hardware and software.

[0057] Referring to FIG. 3, the electronic device (300) may include a transceiver (310), a memory (320), and a processor (330). However, the present disclosure is not limited thereto. For example, the electronic device (300) may not include at least some of the components illustrated in FIG. 3, or may further include components not illustrated in FIG. 3. For example, the electronic device (300) may not include the transceiver (310).

[0058] The transceiver (310) can perform functions for transmitting and receiving signals in a wired communication environment. The transceiver (310) can include a wired interface for controlling direct connections between devices via a transmission medium (e.g., copper wire, optical fiber). For example, the transceiver (310) can transmit electrical signals to other devices via copper wire, or perform conversion between electrical signals and optical signals.

[0059] The transceiver (310) may perform functions for transmitting and receiving signals in a wireless communication environment. For example, the transceiver (310) may perform a conversion function between baseband signals and bit streams according to the physical layer specifications of the system. For example, when transmitting data, the transceiver (310) encodes and modulates the transmitted bit stream to generate complex-valued symbols. Furthermore, when receiving data, the transceiver (310) demodulates and decodes the baseband signal to restore the received bit stream. Furthermore, the transceiver (310) may include multiple transmission and reception paths.

[0060] The transceiver (310) transmits and receives signals as described above. Accordingly, all or part of the transceiver (310) may be referred to as a "communication unit," a "transmitter," a "receiver," or a "transmitter-receiver unit." Furthermore, in the following description, transmission and reception performed via a wireless channel are used to mean that the transceiver (310) performs the processing described above.

[0061] Although not illustrated in FIG. 3, the transceiver (310) may further include a backhaul transceiver for connection to the core network or other base stations. The backhaul transceiver provides an interface for communicating with other nodes within the network. That is, the backhaul transceiver converts a bit stream transmitted from the base station to other nodes, such as other access nodes, other base stations, upper nodes, the core network, etc., into a physical signal, and converts a physical signal received from other nodes into a bit stream.

[0062] The memory (320) stores data such as basic programs, application programs, and setting information for the operation of the electronic device (300). The memory (320) may be referred to as a storage unit. The memory (320) may be composed of volatile memory, nonvolatile memory, or a combination of volatile memory and nonvolatile memory. In addition, the memory (320) provides stored data upon request from the processor (330).

[0063] For example, the processor (330) may include various processing circuits and / or multiple processors. For example, the term "processor" as used herein, including in the claims, may include various processing circuits including at least one processor, one or more of which may be configured to individually and / or collectively perform the various functions described below in a distributed manner. As used herein, when "processor," "at least one processor," and "one or more processors" are described as being configured to perform various functions, these terms encompass, for example, and without limitation, situations where one processor performs some of the recited functions and other processor(s) perform other parts of the recited functions, and also situations where one processor may perform all of the recited functions. Additionally, the at least one processor may include a combination of processors that perform the various functions enumerated / disclosed, for example, in a distributed manner. At least one processor may execute program instructions to achieve or perform the various functions.

[0064] The processor (330) controls the overall operations of the electronic device (300). The processor (380) may be referred to as a control unit. For example, the processor (330) transmits and receives signals via the transceiver (310) (or via a backhaul communication unit). In addition, the processor (330) records and reads data from the memory (320). In addition, the processor (330) may perform the functions of a protocol stack required by a communication standard. Although only the processor (330) is illustrated in FIG. 3, the electronic device (300) may include two or more processors according to other implementation examples.

[0065] The configuration of the electronic device (300) illustrated in FIG. 3 is only an example, and examples of the electronic device (300) performing the embodiments of the present disclosure are not limited to the configuration illustrated in FIG. 3. In some embodiments, some configurations may be added, deleted, or changed. For example, if the electronic device (300) is an RU, the electronic device (300) may further include a fronthaul transceiver. For example, the fronthaul transceiver may transmit and receive signals on a fronthaul interface. For example, the fronthaul transceiver may receive a management plane (M-plane) message. For example, the fronthaul transceiver may receive a management plane (S-plane) message. For example, the fronthaul transceiver may receive a control plane (C-plane) message. For example, the fronthaul transceiver may transmit a user plane (U-plane) message. For example, the fronthaul transceiver can receive user plane messages.

[0066] Figure 4 illustrates an example of a resource structure in the time domain and frequency domain. Figure 4 illustrates the basic structure of the time-frequency domain, which is a radio resource region where data or control channels are transmitted in the downlink or uplink.

[0067] Referring to Figure 4, the horizontal axis represents the time domain and the vertical axis represents the frequency domain. The minimum transmission unit in the time domain is an OFDM (orthogonal frequency division multiplexing) symbol, N symb OFDM symbols (402) are grouped to form one slot (406). The length of a subframe is defined as 1.0 ms, and the length of a radio frame (414) is defined as 10 ms. The minimum transmission unit in the frequency domain is a subcarrier, and the carrier bandwidth constituting the resource grid is N DL RB Dog (downlink) or N UL RB It consists of subcarriers (404) of the dog (uplink).

[0068] The basic unit of resources in the time-frequency domain is a resource element (RE) (412), which can be represented by an OFDM symbol index and a subcarrier index. A resource block may include multiple resource elements. In the LTE system, a resource block (RB) (or physical resource block (PRB)) is N in the time domain. symb N consecutive OFDM symbols and frequency domain SC RB is defined as a series of consecutive subcarriers. In an NR system, a resource block (RB) (408) is defined as N in the frequency domain. SC RBcan be defined as a series of consecutive subcarriers (410). One RB (408) is N on the frequency axis. SC RB It contains 412 REs. In general, the minimum transmission unit of data is RB and the number of subcarriers is N. SC RB =12. The frequency domain may include common resource blocks (CRBs). Physical resource blocks (PRBs) may be defined in the bandwidth part (BWP) of the frequency domain. The CRB and PRB numbers may be determined based on the subcarrier spacing. The data rate may increase in proportion to the number of RBs scheduled to the terminal.

[0069] In the NR system, in the case of a frequency division duplex (FDD) system that operates the downlink and uplink by frequency division, the downlink transmission bandwidth and the uplink transmission bandwidth may be different. The channel bandwidth represents the radio frequency (RF) bandwidth corresponding to the system transmission bandwidth. [Table 1] shows part of the correspondence between the system transmission bandwidth, subcarrier spacing (SCS), and channel bandwidth defined in the NR system in a frequency band lower than x GHz (e.g., frequency range (FR) 1 (410 MHz to 7125 MHz)). And [Table 2] shows part of the correspondence between the transmission bandwidth, subcarrier spacing, and channel bandwidth defined in the NR system in a frequency band higher than y GHz (e.g., FR2 (24250 MHz - 52600 MHz) or FR2-2 (52600 MHz to 71000 MHz)). For example, an NR system with a 100 MHz channel bandwidth and a 30 kHz subcarrier spacing has a transmission bandwidth of 273 RBs. In [Table 1] and [Table 2], N / A may be a bandwidth-subcarrier combination not supported by the NR system.

[0070] Channel bandwidth [MHz] SCS 5 10 20 50 80 100 Transmission bandwidth configuration N RB 15kHz2552106207N / AN / A30kHz11245113321727360kHzN / A112465107135

[0071] Channel bandwidth [MHz] SCS50100200400 Transmission bandwidth configuration N RB 60kHz66132264N / A120kHz3266132264

[0072] Precoding may refer to an operation applied to a signal before the signal is transmitted from the transmitter. In a wireless communication system, the signal may be transmitted on a wireless channel between the transmitter and the receiver. In order for the receiver to receive the signal well, the transmitter may perform an operation of multiplying the signal by a specific matrix. The specific matrix may be referred to as a precoder, a precoding matrix, or precoding information. For example, in a 3GPP standard communication system, it is defined that a terminal (120) reports channel state information (CSI) to a base station (110) (or DU (210)). The channel state information refers to information related to the quality of a wireless channel or wireless link formed between the terminal (120) and an antenna port (e.g., a CSI-RS port) of the base station (110). The above channel state information may include a rank indicator (RI), a precoding matrix indicator (PMI), and a channel quality indicator (CQI). The RI indicates information related to the rank of the channel and represents the number of streams that the terminal (120) can receive through the same resource. The PMI is a value reflecting the spatial characteristics of the channel and represents information (e.g., an index) on a precoding matrix preferred by the terminal among a plurality of candidate precoding matrices. The plurality of candidate precoding matrices are defined as a codebook, and the candidate precoding matrices of the codebook can be defined in the standard in various ways depending on the codebook type.

[0073] The base station (110) can support MIMO (multiple input multiple output) (hereinafter, MU-MIMO) for multiple users (MU). For example, the base station (110) can perform scheduling for MU-MIMO (hereinafter, MU-MIMO scheduling). For example, the base station (110) can operate a plurality of terminals (e.g., terminals 120 of FIG. 1) to support MU-MIMO. For example, the base station (110) can use CSI acquired (or received) from the plurality of terminals to support MU-MIMO. For example, the CSI can include PMI. For example, the base station (110) can use the result of channel estimation based on an uplink reference signal (e.g., a sounding reference signal (SRS)) received from at least some of the plurality of terminals to support MU-MIMO. In order to support MU-MIMO, the base station (110) may calculate a correlation between the plurality of terminals. For example, the correlation between the terminals may be referred to as a correlation coefficient, correlation information, correlation value, or Rho matrix. For example, the correlation may represent a correlation between two terminals. For example, the two terminals may be referred to as paired terminals or MU-paired terminals. When the base station (110) calculates the correlation using SRS acquired (or received) from the plurality of terminals, the complexity of calculating the correlation may be high.

[0074] For example, the base station (110) can support MU-MIMO for terminals within a pool of UEs for MU (hereinafter, MU pool). For example, the base station (110) can support MU-MIMO by checking channel information based on SRS received from terminals corresponding to the MU pool. At this time, the correlation between the terminals based on the channel information of the SRS can have a complexity proportional to the number of transmit antennas of each of the terminals. In addition, the base station (110) can simultaneously support MU-MIMO based on PMI (or PMI for Type 1 codebook) together with MU-MIMO based on SRS received from terminals corresponding to the MU pool. Accordingly, a diversity effect for the MU pool can be expected in practice. However, if the MU pool is doubled, the correlation between the terminals can have a complexity that increases by four times.

[0075] Hereinafter, the device, method, and storage medium according to the present disclosure may perform spatial compression on acquired CSI (e.g., PMI for Type 1 codebook), and may utilize SD (spatial domain) basis index (or SD beam index, 2D (dimension) SD beam index, DFT (discrete Fourier transform) spatial basis, spatial basis, basis vector, DFT basis vector, beam vector, 2D DFT vector, channel vector, 2D beam) (hereinafter referred to as basis vector) and low-dimensional linear coefficients. For example, the base station (110) may utilize channel information (or channel estimation information, channel estimation matrix) based on SRS and the CSI based on CSI-RS (channel state information-reference signal). The device, method, and storage medium according to the present disclosure can perform an operation of an equivalent rho matrix (or correlation) in a reduced dimension using spatial compression, and perform MU scheduling with low complexity based on basis vectors. In this case, spatial channel information of a channel has channel sparsity expressed as a sum of multiple paths within the angle domain, and thus can be related to correlation information within the antenna domain.

[0076] In one example, the device, method, and storage medium according to the present disclosure may utilize basis vectors extracted from CSI (e.g., PMI for a Type 1 codebook). For example, the device, method, and storage medium according to the present disclosure may utilize PMI index gaps between terminals obtained through PMI parsing. For example, the PMI index may represent angle indices for indicating the basis vectors. The device, method, and storage medium according to the present disclosure may utilize the PMI index gaps to calculate correlations between terminals.

[0077] In one example, the device, method, and storage medium according to the present disclosure can manage channel information by decomposing it into linear combination (LC) weights by obtaining residual basis vectors based on a dominant basis vector among basis vectors based on PMI. By converting it into a low-dimensional LC expressed by a finite orthogonal basis vector, the process of calculating correlations between terminals can be performed with low complexity.

[0078] In the above example, the basis vector indicated by the PMI can be used to indicate a basis vector capable of compressing the channel information of the SRS in the spatial domain. By using the basis vector indicated by the PMI reported by the terminal as the dominant basis vector constituting the channel formed between the base station and the terminal, complex operations on the channel information of the SRS can be avoided to directly extract the dominant basis vector from the SRS channel information. For example, the PMI can provide indicators and oversampling factors of beams orthogonal to the beam corresponding to the basis vector. For example, the channel information of the SRS of the same terminal and the SD beam of the PMI can satisfy partial reciprocity. For example, the partial reciprocity can be applied to both time division duplex (TDD) and frequency division duplex (FDD).

[0079] In the above example, when the number of transmitting antennas of the terminal is Nt, the dimension of the LC weights configured as a spatial basis by spatial compression may be L (L << Nt). In this case, by utilizing the unitary matrix property of the DFT submatrix required for the spatial compression, the pseudo-inverse operation can be simplified to a Hermitian operation.

[0080] As described above, the device, method, and storage medium according to the present disclosure can reduce complexity by using a table (e.g., a look-up table (LUT)) according to PMI index differences for correlations between terminals in a PMI MU pool. In addition, the device, method, and storage medium according to the present disclosure can reduce complexity by using PMI-based spatial domain beam information (e.g., a basis vector in a spatial domain) and compressed channel information (e.g., information compressed from channel information of SRS) for correlations between terminals in an SRS MU pool or between terminals in an SRS MU pool and terminals in a PMI MU pool. Accordingly, the device, method, and storage medium according to the present disclosure can calculate channel gain and null space vectors with relatively low complexity by using unified orthogonal basis vectors between terminals. The device, method, and storage medium according to the present disclosure can perform MU-MIMO scheduling with relatively low complexity.

[0081] The 3GPP NR standard defines Type 1 and Type 2 codebooks. Type 1 codebooks can be divided into single-panel codebooks and multi-panel codebooks. Type 2 codebooks can be divided into the general Type 2 codebook and Type 2 port selection codebook introduced in Release 15, and the enhanced Type 2 codebook and enhanced Type 2 port selection codebook introduced in Release 16. Since the Type 2 codebook in Release 15 only supports up to Rank 2, the enhanced Type 2 codebook was introduced in Release 16. The enhanced Type 2 codebook can support up to Rank 4. In an enhanced Type 2 codebook, PMI weights can be computed in an improved manner to reduce the CSI feedback overhead or CSI bit payload by additionally utilizing frequency domain compression over the Type 2 codebook, along with beam amplitude scaling and co-phasing values ​​(which may be referred to as beam combining coefficients) for all beams.

[0082] As ranks expand, feedback overhead may increase linearly with the number of subbands. Since uplink resources may be insufficient, a process called discrete Fourier transform (DFT) compression or frequency-domain compression can be applied to the enhanced Type 2 codebook by utilizing the frequency-domain correlation of the beam combining coefficients. Examples of design methods for Type 1 and Type 2 codebooks can be referenced in FIG. 5a.

[0083] Figure 5a shows examples of Type 1 codebook and Type 2 codebook designs.

[0084] Figure 5a illustrates examples (500, 505) of a method for designing a codebook to be used for mapping a precoding matrix from a reported PMI. Example (500) may represent a method for designing a Type 1 codebook for a single user (SU). Example (505) may represent a method for designing a Type 2 codebook for multiple users (MU).

[0085] Referring to example (500), beam indicators (i) of PMI of CSI received from a terminal (e.g. terminal (120) of FIG. 1) 1,1 , i 1,2 ) (or codebook index), adjacent beams (502) can be identified among a plurality of candidate beams (501). For example, adjacent beams (502) can be identified by beam selection in WB (wideband). For example, the relationship between candidate beams (501) and adjacent beams (502) can be represented by W1. Based on co-phase information by the phase indicator of PMI of the CSI, beam (503) can be identified among adjacent beams (502). For example, beam (503) can be identified by beam selection in SB (subband). For example, the relationship between adjacent beams (502) and beam (503) can be represented by W2.

[0086] Referring to example (505), beam indicators (i) of PMI of CSI received from a terminal (e.g. terminal (120) of FIG. 1) 1,1 , i 1,2) (or codebook index), beam group selection (506) may be performed. For example, according to beam selection in WB, L beams may be selected. Beam selection in WB may be represented by W1. After beam group selection (506), amplitude scaling (507) and co-phasing and linear combination (LC) (508) may be performed. For example, amplitude scaling (507) and co-phasing and linear combination (508) may be represented by W2. For example, reduced dimensions of linear coefficients per subband may be considered in W2. For example, W2 may represent a matrix of LC coefficients for amplitude and phase for SB reporting.

[0087] Figure 5b illustrates an example of a spatial beam grid associated with antenna elements.

[0088] FIG. 5B illustrates an example (520) of 32 antenna elements (or transmitting antennas) and an example (525) of a spatial beam grid associated with the antenna elements of example (520). In example (520) of FIG. 5B, the number and structure of the antenna elements are merely exemplary for convenience of explanation, and the present disclosure is not limited thereto.

[0089] Referring to example (520), 32 antenna elements can be defined as 2N1N2. For example, N1 can represent the number in a first dimension (e.g., vertical dimension) of an array of antenna elements. For example, N2 can represent the number in a second dimension (e.g., horizontal dimension) of an array of antenna elements. In this case, 2 can represent antenna elements for supporting two polarizations (or, poles).

[0090] Example (525) may represent a grid for beams in the spatial domain that may be substantially generated by the antenna elements of example (520). For example, the beams in the spatial domain may be determined by the number of antenna elements (N1, N2) and oversampling factors (O1, O2). For example, the oversampling factors may be defined for the first dimension and the second dimension. For example, the oversampling factor (O1) may be defined for the first dimension. Accordingly, with respect to the first dimension, the beams in the spatial domain may be N1O1. Also, for example, the oversampling factor (O2) may be defined for the second dimension. Accordingly, with respect to the second dimension, the beams in the spatial domain may be N2O2. In example (525), it is assumed that each of the oversampling factors is 4. However, the present disclosure is not limited thereto.

[0091] In example (525), beams (526) may represent beams that are orthogonal to each other. For example, beams (526) may have the same oversampling factors. Beams (526) may be referred to as orthogonal beams. Beams (528) may represent beams that are oversampled from each of beams (526) when the combination of oversampling factors (O1, O2) has different values. For example, beams (528) may be referred to as oversampled beams. Each of beams (526) and each of beams (528) may indicate a specific direction in the spatial domain in the form of a DFT vector.

[0092] Referring to FIGS. 5A and 5B , the base station (110) (or the DU (210) of FIG. 2A) can check the channel status of each terminal and identify a combination of terminals having a low correlation between the terminals in order to support MU-MIMO (or MU-MIMO scheduling). The base station (110) can perform downlink transmission using a plurality of beams substantially simultaneously by determining precoding information to be applied to the terminals of the combination. In order to calculate the correlation between the terminals, the base station (110) can use the CSI and / or SRS received from the terminals associated with the base station (110) (or the terminals within the cell). For example, the CSI may include a PMI for a Type 1 codebook.

[0093] Figure 6 illustrates an example of a signal flow for a method in which a base station calculates correlation information between terminals and performs downlink transmission.

[0094] Referring to FIG. 6, an example (600) of a method in which a base station (603) performs downlink transmission by calculating correlation information between terminals (601, 602) is illustrated. Each of the terminals (601, 602) of the example (600) may be an example of the terminal (120) of FIG. 1. The base station (603) of the example (600) may be an example of the base station (110) of FIG. 1. However, the present disclosure is not limited thereto. For example, the base station (603) may be an example of the DU (210) of FIG. 2A. In FIG. 6, signaling between two terminals (601, 602) and the base station (603) is illustrated for convenience of explanation, but the present disclosure is not limited thereto. For example, the present disclosure can also be applied to signaling between a larger number of terminals (e.g., 256 terminals) and a base station (603). In the embodiments below, each operation may be performed sequentially, but is not necessarily performed sequentially. For example, the order of each operation may be changed, and at least two operations may be performed in parallel.

[0095] Referring to FIG. 6, in operation (605), the base station (603) may transmit a CSI-RS. For example, the base station (603) may provide the CSI-RS to the first terminal (601) and the second terminal (602). For example, the CSI-RS may indicate a downlink reference signal for checking a channel state. However, the present disclosure is not limited thereto. For example, the CSI-RS may be changed to another downlink reference signal or a reference signal for checking a channel state.

[0096] For example, terminals (601, 602) can generate CSI using the received CSI-RS. For example, a first terminal (601) can generate a first CSI using the received CSI-RS. For example, the first CSI can include a first PMI. For example, the first PMI can be a PMI of the first terminal (601) for a Type 1 codebook. For example, a second terminal (602) can generate a second CSI using the received CSI-RS. For example, the second CSI can include a second PMI. For example, the second PMI can be a PMI of the second terminal (602) for a Type 1 codebook.

[0097] In operation (610-1), the first terminal (601) can transmit CSI to the base station (603). For example, the first terminal (601) can transmit the first CSI to the base station (603). In operation (610-2), the second terminal (602) can transmit CSI to the base station (603). For example, the second terminal (602) can transmit the second CSI to the base station (603).

[0098] In operation (605) of FIG. 6, the CSI-RS is illustrated as being transmitted from the base station (603) to the first terminal (601) and the second terminal (602) simultaneously, but the present disclosure is not limited thereto. For example, the base station (603) may transmit the CSI-RS to the second terminal (602) after transmitting the CSI-RS to the first terminal (601). In addition, in operations (610-1) and (610-2) of FIG. 6, the second terminal (602) is illustrated as transmitting the second CSI after the first terminal (601) transmits the first CSI, but the present disclosure is not limited thereto. For example, the second terminal (602) may transmit the second CSI before the first terminal (601) transmits the first CSI, or may transmit the second CSI simultaneously.

[0099] In operation (615-1), the first terminal (601) may transmit an SRS to the base station (603). For example, resources for transmitting the SRS, which is an uplink reference signal for checking a channel state, may be allocated to the first terminal (601). In operation (615-2), the second terminal (602) may transmit an SRS to the base station (603). For example, resources for transmitting the SRS, which is an uplink reference signal for checking a channel state, may be allocated to the second terminal (602). If the base station (603) does not allocate resources for transmitting the SRS to the second terminal (602) among the terminals in the cell, operation (615-2) may be omitted. In other words, some of the terminals in the cell may not support transmission of the SRS, or resources for transmitting the SRS may not be allocated to some terminals.

[0100] Referring to FIG. 6, the SRS may be acquired after the PMI (or CSI) is acquired. This may be because the PMI (or CSI) is periodically received as the RA (random access) procedure between the base station (603) and the terminals (601, 602) is completed, but the SRS is transmitted through resources scheduled by the base station (603) for at least some of the terminals (601, 602).

[0101] In operation (620), the base station (603) may calculate correlation information. For example, the base station (603) may calculate a set of correlation information between the terminals within the cell. For example, the set of correlation information may represent a set of correlation information between two of the terminals. For example, the correlation information may be referred to as a correlation degree, a correlation coefficient, a correlation value, or a Rho matrix. For example, the correlation information may be calculated using the CSI (or PMI) of one terminal and the CSI (or PMI) of another terminal, the CSI (or PMI) of one terminal and the SRS of another terminal, or the SRS of one terminal and the SRS of another terminal. Specific details related thereto are described below in FIG. 7.

[0102] For example, the correlation information between the terminals can be calculated using the first CSI of the first terminal (601) and the second CSI of the second terminal (602). For example, the correlation information can be determined using the gap between the first index of the first PMI included in the first CSI and the second index of the second PMI included in the second CSI, and a lookup table (LUT) for the gap. For example, each of the first index and the second index can be used to indicate a basis vector. Specific details related thereto are described below in FIGS. 8A and 8B.

[0103] For example, the correlation information between the terminals can be calculated using channel information for the SRS of the first terminal (601) and the second CSI of the second terminal (602). For example, the correlation information can be calculated using channel information compressed by a first basis vector indicated by the first PMI and a second basis vector indicated by the second PMI. For example, the compressed channel information can have an LC format of L selected beams (or basis vectors) and LC coefficients for the L beams by being compressed (or spatially compressed) by the first basis vector from the channel information for the SRS. For example, the L beams can include a dominant beam indicated by the first basis vector (or a beam having a dominant SD beam index). Specific details of a method for generating compressed channel information through a basis vector indicated by a PMI are described below with reference to FIGS. 9A and 9B.

[0104] For example, the correlation information between the terminals can be calculated using channel information for the SRS of the first terminal (601) and channel information for the SRS of the second terminal (602). For example, the correlation information can be calculated using channel information compressed by the first basis vector indicated by the first PMI and other channel information compressed by the first basis vector indicated by the second PMI. For example, the compressed channel information can have an LC format of L selected beams (or basis vectors) and LC coefficients for the L beams by being compressed (or spatially compressed) by the first basis vector from the channel information for the SRS of the first terminal (601). For example, the compressed other channel information may have an LC format of selected L beams (or basis vectors) and LC coefficients for the L beams by being compressed (or spatially compressed) by the second basis vector from the channel information for the SRS of the second terminal (602). For example, the compressed channel information and the compressed other channel information may have a unified dimension. In other words, the oversampling factors of the compressed channel information may correspond to the oversampling factors of the compressed other channel information. Specific details on a method for generating compressed channel information having a unified dimension are described below in FIG. 10.

[0105] In operation (625), the base station (603) may perform terminal selection. For example, the base station (603) may perform successive UE selection (SUS). For example, the base station (603) may select terminals to be scheduled from among the terminals in the MU pool using the set of correlation information. For example, the number of terminals to be scheduled may correspond to the number of candidate layers. For example, the number of candidate layers may be determined according to the capabilities of the MU scheduler. Specific details related thereto are described below in FIGS. 11A to 12 .

[0106] In operation (630), the base station (603) may perform layer selection. For example, the base station (603) may determine transmission layers for the scheduled terminals among the candidate layers. For example, the transmission layers may be determined based on a proportional fair (PF) metric. Specific details related to this are described below in FIGS. 11A and 11B .

[0107] In operation (635), the base station (603) may determine precoding information. For example, the base station (603) may determine the transmission layers and beamforming weights for the transmission layers determined for the terminals to be scheduled. For example, the base station (603) may determine the precoding information using at least one of the beamforming weights, CSI, and compressed channel information. For example, CSI and compressed channel information (or channel information of SRS) related to the terminals related to the precoding information may be utilized.

[0108] In operation (640), the base station (603) can perform downlink transmission. For example, if the terminals to be scheduled are the first terminal (601) and the second terminal (602), the base station (603) can transmit downlink data to the first terminal (601) and the second terminal (602) according to the precoding information related to the first terminal (601) and the second terminal (602).

[0109] As described above, the example (600) of FIG. 6 illustrates the operation of the base station (603), which is an example of the base station (110) of FIG. 1, but the present disclosure is not limited thereto. For example, if the base station (603) is the DU (210) of FIG. 2A, the DU (210) may transmit the downlink data to the scheduled terminals through the RU (220) of FIG. 2A according to the determined precoding information. In one example, the channel information of the SRS to be used to determine the precoding information may be estimated in the RU (220), and the estimated channel information may be transmitted to the DU (210). At this time, the DU (210) may also calculate the correlation between the terminals using the channel information received from the RU (220).

[0110] Figure 7 illustrates an example of a method for calculating correlation information between terminals using a sounding reference signal (SRS) and a precoding matrix indicator (PMI).

[0111] FIG. 7 illustrates an example (700) of a method for calculating correlation between terminals using a sounding reference signal (SRS) and a precoding matrix indicator (PMI) by a base station (110) of FIG. 1 (or a DU (210) of FIG. 2a). For example, the method for calculating correlation between terminals may represent a specific example of operation (620) of FIG. 6.

[0112] Referring to example (700), indices of candidate virtual UEs associated with SRS (or SRS UE indices), indices of candidate virtual UEs associated with PMI (or PMI UE indices), and scheduling layer indices may be defined. For example, the SRS UE index may be used to indicate a candidate UE (or SRS UE) to transmit SRS. For example, the PMI UE index may be used to indicate a candidate UE (or PMI UE) to transmit PMI. For example, the scheduling layer index may be used to indicate a layer (or transport layer) to be scheduled.

[0113] Referring to example (700), correlation information (701) between two PMI UEs can be calculated using basis vectors obtained from the PMI. For example, correlation information (703) can be calculated (or determined) using a gap between a first index used to indicate a first basis vector for a first PMI UE and a second index used to indicate a second basis vector for a second PMI UE, and an LUT for the gap.

[0114] Referring to example (700), correlation information (702) between one SRS UE and one PMI UE can be calculated using reduced dimension channel information and a basis vector obtained from the PMI. For example, correlation information (702) can be calculated using compressed channel information for the SRS UE and the basis vector for the PMI UE.

[0115] Referring to example (700), correlation information (703) between two SRS UEs can be calculated using channel information with a reduced dimension. For example, the channel information with the reduced dimension can be referred to as compressed channel information. For example, correlation information (701) can be calculated using first compressed channel information for a first SRS UE and second compressed channel information for a second SRS UE.

[0116] Figure 8a illustrates an example of the operational flow for a method of calculating correlation information using the basis vectors of terminals. Figure 8b illustrates examples of tables and graphs representing the values ​​of the correlation information.

[0117] At least some of the methods of FIG. 8A may be performed by the base station (110) of FIG. 1. However, the present disclosure is not limited thereto. At least some of the methods may be performed by the DU (210) of FIG. 2A. For example, at least some of the methods may be controlled by the processor (330) of the electronic device (or device, apparatus, node, entity) of the DU (210). In the following embodiments, each operation may be performed sequentially, but is not necessarily performed sequentially. For example, the order of each operation may be changed, and at least two operations may be performed in parallel.

[0118] In operation (800), the base station (110) may perform PMI parsing. For example, the PMI parsing may obtain codebook information constituting at least some elements of precoding information by parsing CSI obtained from each terminal. For example, the codebook information may include beam information and phase information. For example, the beam information may include beam indicators (i) for PMI. 1,1, i 1,2 ) may include. For example, the phase information may include a phase indicator (i2). In the following, for convenience of explanation, it is assumed that PMI parsing is performed on CSI obtained from each of two terminals (e.g., a first terminal and a second terminal). For example, the base station (110) may obtain first beam information and first phase information according to PMI parsing of the first CSI of the first terminal, and may obtain second beam information and second phase information according to PMI parsing of the second CSI of the second terminal.

[0119] In operation (805), the base station (110) can obtain the beam index difference. For example, the base station (110) can obtain the first beam information (i 1,1 , i 1,2 ) can identify the first beam index (or PMI index) indicated by the second beam information (i 1,1 , i 1,2 ) can identify the second beam index (or PMI index). The difference (gap) between the first beam index and the second beam index can be obtained. The difference between the first beam index and the second beam index can be referred to by the following mathematical equation.

[0120]

[0121]

[0122] In operation (810), the base station (110) can obtain a phase difference. For example, the base station (110) can obtain a gap between the first phase information and the second phase information. The difference between the first phase information and the second phase information can be referred to by the following mathematical equation.

[0123] [Correction pursuant to Rule 91, March 7, 2025]

[0124]

[0125] In operation (815), the base station (110) may calculate correlation information. For example, the base station (110) may calculate correlation information between the first terminal and the second terminal. For example, correlation information between two vectors may be calculated using the following mathematical formula.

[0126] [Correction pursuant to Rule 91, March 7, 2025]

[0127] For example, the above can represent the correlation information (or correlation, rho correlation) between vector a and vector b. For example, the vector a is a1 to a N It can be a vector containing elements. For example, the above b vector is b1 to b N It can be a vector containing elements.

[0128] Referring to the above mathematical formula, the correlation information between two vectors can be calculated as a linear sum of the vector elements of the phase shift for each element. Referring to the above mathematical formula, the correlation between the first terminal and the second terminal can be calculated by the following mathematical formula using the first beam index, the first phase information, the second beam index, and the second phase information.

[0129]

[0130]

[0131] FIG. 8b illustrates a table (820) showing correlation information (ρ) between a basis vector (v1) (821) according to the first beam index and a basis vector (v2) (822) according to the second beam index.

[0132] Referring to table (820), the value of the correlation information between the basis vector (v1) (821) and the basis vector having the same beam index may be 1. The value of the correlation information between the basis vector (v2) (822) having a different beam index from the basis vector (v1) (821) may be 0. Referring to table (820), the value of the correlation information may be lowered as the difference between the first beam index of the basis vector (v1) (821) and a specific beam index increases. In addition, the value of the correlation information may be 0 when the difference between the first beam index of the basis vector (v1) (821) and the specific beam index satisfies a specific condition (e.g., orthogonality). In the example of table (820), the basis vector (v1) (821) and the basis vector (v2) (822) may be orthogonal to each other.

[0133] Figure 8b shows a beam indicator (i) based on the basis vector (v1) (821). 1,1 ) is a graph (830) representing correlation information (ρ). The graph (830) is a beam indicator (i) used to indicate a beam index. 1,1 ), it can represent correlation information for the basis vector (v1) (821). The horizontal axis of the graph (830) is the beam indicator (i 1,1 ), the vertical axis can represent the value of correlation.

[0134] Referring to the graph (830), a beam indicator (i) for indicating the first beam index 1,1 )(e.g., 16) and a specific beam indicator, the correlation value may be lowered. In addition, if the difference between the specific beam indicator and the beam indicator for indicating the first beam index (e.g., 16) satisfies a specific condition (e.g., when the size of the difference is 4), the correlation value may be 0.

[0135] Referring back to FIG. 8A, the base station (110) can calculate correlation information (or channel correlation information) between two terminals by using the gap between the beam indices of the two terminals. If the base station (110) uses an SD beam rather than the gap between the beam indices, the complexity of calculating correlation information between the two terminals may increase. The complexity of using the SD beam can be referenced in the table below.

[0136] UE AUE BAll correlation cases b / w two UEs for all PMI weightsSD beamCo-phasingSD beamCo-phasingSame Rank b / w two UEs (Class 1 or 2)Different Rank b / w two UEs (Class 1 and 2)SD beamCo-phasingTotalSD beamCo-phasingTotalRank=1256425642^16=655362^4=162^20=10485762^15=3276 82^3=82^18=262144Rank=2256225622^16=655362^2=42^18=2621442^15=327682^3= 82^18=262144Rank=3128212822^14=163842^2=42^16=327682^15=327682^3=82^18= 262144Rank=4128212822^14=163842^2=42^16=327682^15=327682^3=82^18=262144

[0137] Referring to the above table, the bit payload of the PMI for the Type 1 codebook associated with 32 ports can be configured as 10 bits or 11 bits depending on the rank. For example, the number of the SD beams is determined by the number of beam indicators (i) in the PMI. 1,1 , i 1,2 ) can be determined by. For example, if the rank is 1, i 1,1 This is 4 bit and i1,2 can be 4 bits. Accordingly, the number of SD beams is 256 (=2 8 =2 4 *2 4 ) may be. The number of the SD beams according to the combination of two terminals is 2 16 (or 2 15 ) may be. In addition, the number of co-phasings may be determined by the phase indicator (i2) in the PMI. For example, if the rank is 1, i2 may be 2 bits. Accordingly, the number of co-phasings may be 4 (=2 2 ) can be. The number of co-phasing according to the combination of two terminals is 16 (=2 4 ) can be. Referring to the above, the complexity for calculating the correlation between terminals considering all PMI weights is 2 16 Inland 2 20 It can have levels. In the above example, an example of 32 ports is described, but the present disclosure is not limited thereto.

[0138] In other words, the base station (110) performs the calculation of the correlation between two terminals in L2 (layer 2) for scheduling. 16 Inland 2 20 Memory may be required to store LUTs having a size. In addition, the base station (110) may spend a relatively long time searching for the LUT. In contrast, the base station (110) according to the present disclosure can reduce the LUT size by utilizing the difference between the PMI indices of the terminal (or the difference between the beam indices) in the PMI parsing step. The complexity of utilizing the difference between the beam indices can be referenced in the table below.

[0139] UE AUE BAll correlation cases b / w two UEs for all PMI weightsSD beamCo-phasingSD beamCo-phasingSame Rank b / w two UEs (Class 1 or 2)Different Rank b / w two UEs (Class 1 and 2)SD beam index gapCo-phasing gapTotalSD beam index gapCo-phasing gapTotalRank=1256425642^8=2562^4=162^12=40962^8=2562^3=82^11=2048Rank=2256225622^8=2562^2=42^10=10242^8=2562^3=82^ 11=2048Rank=3128212822^7=1282^2=42^9=5122^8=2562^3=82^11=2048Rank=4128212822^7=1282^2=42^9=5122^8=2562^3=82^11=2048

[0140] Referring to the above table, the number of differences between the beam indices of the two terminals is 2 8 (or 2 7 ) can be. The number of differences between the co-phasing indices of two terminals is 2 2 (or 2 3 , 2 4 ) can be. Referring to the above, the complexity for calculating the correlation between terminals considering all PMI weights is 2 9 Inland 2 12 It can have levels.

[0141] As described above, the base station (110) can calculate the correlation between two terminals with a relatively low level of complexity by utilizing the difference between beam indices (and the index difference between the co-phasing). In addition, since the size of the look-up table (LUT) for calculating (or determining) the correlation is reduced, the memory capacity within the base station (110) (or L2 of the base station (110)) can be reduced, and the search time of the LUT can be reduced.

[0142] Although not illustrated in FIG. 8a, the base station (110) may pre-generate and store an LUT for correlation between PMI beams based on the difference between PMI beam indices. When generating the LUT based on the difference between the beam indices, the rank of the terminal according to the number of ports may be taken into consideration.

[0143] For example, when the number of ports is 16 or 32, there may be a difference between the basis vector of the PMI weight according to rank 1 or rank 2 and the basis vector of the PMI weight according to rank 3 or rank 4. In contrast, when the number of ports is 4 or 8, the basis vector of the PMI weight may not have a difference. Accordingly, the LUT for the difference between the SD beam indices can be defined in multiple categories. For example, the LUTs according to the multiple categories can be referred to in the following tables.

[0144] Category UE AUE BLook-up table (LUT) category 1strank1,2,3,4,rank1,2,3,4,Look-up table (LUT) category 2ndrank3,4rank3,4Look-up table (LUT) category 3rdrank1,2rank3,4rank3,4rank1,2

[0145] CategoryUE AUE BLook-up table (LUT) category 1strank1,2,3,4,rank1,2,3,4,

[0146] Table 5 above can show examples of LUT categories according to rank when the number of ports is 16 or 32. Table 6 above can show examples of LUT categories according to rank when the number of ports is 4 or 8.

[0147] Figures 9a and 9b illustrate an example of a method for generating compressed channel information of an SRS using basis vectors extracted according to PMI parsing.

[0148] FIG. 9a illustrates an example (900) of a method for extracting information for compressing channel information of an SRS obtained from a terminal (120) using a PMI (and RI) for a Type 1 codebook included in the CSI of the terminal (120) by a base station (110).

[0149] For example, the base station (110) can obtain a beam index of a spatial domain through parsing (or partial parsing) of the PMI. At this time, the beam index can indicate a dominant beam index. For example, the base station (110) can obtain i included in the PMI of Type 1 CSI feedback. 1,1 , i 1,2 The main components of the channel can be extracted from the base station (110). In addition, the base station (110) can obtain the best basis vector (or dominant basis vector) from the main components of the extracted channel. In one example, the PMI of Type 1 CSI feedback is i 1,1, i 1,2 , i 1,3When including , the dominant SD basis vector that can compress spatial information from two dominant beam indices is v m can be expressed as . At this time, Type 1 codebook is v, which is beam information of spatial domain. m It can be expressed as a co-phase component between the plus (+) / minus (-) antennas.

[0150] The base station (110) obtains channel information (e.g., channel matrix (H)) for SRS and covariance information (C) of the obtained channel information. s ) can be obtained. For example, the base station (110) can perform residual basis search / extraction using the main basis vector and covariance information. For example, the residual basis search / extraction can be performed based on the covariance information and SIC. In addition, the base station (110) can perform projection on the basis set for the channel information (H) to obtain compressed LC weights per RB. Accordingly, the base station (110) can obtain DFT basis indices (or basis vectors) and compressed LC weights for spatially compressing the SRS.

[0151] FIG. 9b illustrates an example (950) of a method for extracting residual spatial domain basis (e.g., residual principal basis vectors) of FIG. 9a.

[0152] For example, the base station (110) can extract residual main basis from the channel information of the SRS by performing a signal processing algorithm on the channel information. For example, the residual main basis can represent the signal component of the channel excluding the signal component (or basis vector) according to the beam index extracted through PMI parsing of the CSI.

[0153] For example, information for compressing SRS in the spatial domain is, regardless of co-phase, v m and zero vector v m and can be configured in a form that defines between plus (+) / minus (-) antennas. Alternatively, information for compressing SRS in the spatial domain can be derived from the orthogonal vector basis of the CSI report, v m It can also be configured in a form that combines co-phase.

[0154] At this time, the base station (110) can identify the dominant basis vector from the PMI by knowing i1 and i2 of the PMI before the SRS is received, and can avoid performing unnecessary signal processing to extract the dominant basis vector from the SRS. Accordingly, spatial domain compression for the SRS can be implemented with low complexity.

[0155] The remaining spatial domain basis (e.g., basis vectors) that were not extracted can be obtained through an orthogonal matching pursuit (OMP) signal processing method that identifies the best matching vector among the remaining basis vectors after removing the signal component corresponding to the main basis vector of the PMI previously extracted from the covariance information of the SRS.

[0156] Referring to the above, the base station (110) can extract residual principal basis vectors for the spatial domain from the channel information of the SRS using the principal basis vector extracted through PMI parsing. In other words, the base station (110) can obtain compressed channel information for the spatial domain. For example, the compressed channel information can include the principal basis vector (or beam) and LC coefficients for the residual principal basis vectors (or beams) and beams. The compressed channel information can have a relatively reduced dimension compared to the channel information of the SRS.

[0157] Figure 10 illustrates an example of a method for generating compressed channel information of terminals having a unified dimension.

[0158] At least some of the methods of FIG. 10 may be performed by the base station (110) of FIG. 1. However, the present disclosure is not limited thereto. At least some of the methods may be performed by the DU (210) of FIG. 2A. For example, at least some of the methods may be controlled by the processor (330) of the electronic device (or device, apparatus, node, entity) of the DU (210). In the following embodiments, each operation may be performed sequentially, but is not necessarily performed sequentially. For example, the order of each operation may be changed, and at least two operations may be performed in parallel.

[0159] In operation (1000), the base station (110) may acquire CSI. For example, the base station (110) may acquire the CSI transmitted from the terminal. For example, the CSI may include PMI. In this case, the PMI may be PMI for a Type 1 codebook (i.e., Type 1 PMI feedback).

[0160] In operation (1005), the base station (110) may obtain beam indices. For example, the base station (110) may extract L dominant beam indices according to PMI parsing. At this time, L may correspond to the number of layers. For example, the beam indices may be converted to have a unified dimension. For example, the unified dimension may represent a case where the oversampling factors (O1, O2) have a reference value. For example, the reference value may include (0, 0). However, the present disclosure is not limited thereto.

[0161] In operation (1010), the base station (110) may acquire an SRS. For example, the base station (110) may acquire the SRS transmitted from the terminal. For example, the base station (110) may acquire channel information by performing channel estimation for the SRS. For example, the channel information may represent uncompressed channel information for the spatial domain.

[0162] For example, the base station (110) can compress the channel information using L beam indices. For example, the base station (110) can obtain compressed channel information. For specific details related thereto, reference may be made to FIGS. 9A and 9B. In FIGS. 9A and 9B, an example of compressing the channel information using one beam index is described, but the present disclosure is not limited thereto. For example, the channel information can be compressed for each of the L beam indices.

[0163] In operation (1015), the base station (110) can obtain LC coefficients based on a unified dimension. For example, the compressed channel information generated through the L beam indices can have the unified dimension. For example, the oversampling factors (O1, O2) of each of the beams indicated by the compressed channel information can have the reference value. The compressed channel information represented using the LC coefficients can be referred to the following mathematical equation.

[0164]

[0165]

[0166] The above mathematical expression can be derived according to the following mathematical expression for compression in the spatial domain.

[0167]

[0168]

[0169] As described above, the base station (110) can manage compressed channel information through a simplified operation using information (SD beam index) extracted from CSI (or PMI) without directly calculating the channel estimation result (or channel information) of the SRS. In FIG. 10, an example in which the base station (110) generates compressed channel information having a unified dimension for one terminal is illustrated, but the present disclosure is not limited thereto. For example, the base station (110) can generate compressed channel information having a unified dimension for each terminal. By the base station (110) using the compressed channel information having the unified dimension, the complexity for calculating the correlation between terminals can be reduced. In one example, the base station (110) can use compressed channel information having the same oversampling factors when calculating the correlation between terminals transmitting the SRS (i.e., SRS UEs). However, the present disclosure is not limited thereto. For example, the base station (110) may use compressed channel information having the same oversampling factors when calculating the correlation between the PMI UE and the SRS UE.

[0170] Figures 11a and 11b illustrate examples of MU (multi-user) MIMO (multiple input multiple output) scheduling.

[0171] FIG. 11A illustrates an example (1100) of MU-MIMO scheduling between a base station (1101) and terminals (1102-1, 1102-k). The base station (1101) of FIG. 11A may represent an example of the base station (110) of FIG. 1 (or the base station (603) of FIG. 6). Each of the terminals (1102-1, 1102-k) of FIG. 11A may be an example of the terminal (120) of FIG. 1. For example, the terminals (1102-1, 1102-k) may include the first terminal (601) and the second terminal (602) of FIG. 6.

[0172] Referring to example (1100), the base station (1101) can transmit downlink data to terminals (1102-1, 1102-k) according to MU-MIMO scheduling. For example, the entire received signal vector (y) of the terminals (1102-1, 1102-k) s ) can be referenced as the following mathematical formula.

[0173]

[0174]

[0175]

[0176] At this time, the SINR (signal to interference plus noise ratio) of each layer for each terminal can be referenced by the following mathematical formula.

[0177]

[0178]

[0179] [Correction pursuant to Rule 91, March 7, 2025]

[0180]

[0181] [Correction pursuant to Rule 91, March 7, 2025]

[0182] [Correction under Rule 91 07.03.2025] The above sumPF is the sum of MU PF, is the average value of the above MU sum capacity, can represent the instantaneous value of the MU sum capacity. The base station (1101) can perform MU scheduling for terminals (1102-1, 1102-k) based on the MU sum PF. For example, after performing the MU scheduling, the precoder to be applied can refer to the following mathematical equation.

[0183]

[0184]

[0185] Referring to the above, the base station (1101) can perform MU scheduling by considering the MU sum capacity and the MU sum PF. For specific details on the method for performing the MU scheduling, reference may be made to FIG. 11b below.

[0186] FIG. 11b illustrates an example (1150) of how the base station (1101) of FIG. 11a performs MU scheduling.

[0187] For example, assume that a terminal is RRC connected to a base station (1101). For example, the base station (1101) can set SRS resource settings. In addition, the base station (1101) can set RRC reconfiguration.

[0188] For example, in operation (1161), the base station (1101) can check the PMI SU (single user) pool for the RRC connected terminal. For example, the base station (1101) can set PMI (or CSI) to the terminal in the PMI SU pool. For example, in operation (1162), the base station (1101) can check the MU pool. In operation (1163), the base station (1101) can perform an MU condition check through the SRS MU flag and the PMI MU flag. If the condition for MU-MIMO is satisfied, the base station (1101) can perform operation (1165) to perform MU-MIMO. Conversely, if the condition for MU-MIMO is not satisfied, the base station (1101) can perform SU scheduling to perform SU-MIMO.

[0189] Before performing operation (1165), the base station (1101) may calculate correlation information. For example, for specific details on the operation of calculating the correlation information, reference may be made to operation (620) of FIG. 6.

[0190] For example, in operation (1165), the base station (1101) may perform successive UE selection (SUS). For example, the SUS may be referred to as UE selection or UE sorting. For example, the base station (1101) may generate a candidate layer set for the UEs in the MU pool. For example, the candidate layer set may include up to 8 or 16 layers by considering the PF metric and channel orthogonality. However, the present disclosure is not limited thereto. For example, the Gram-Schmidt process may be performed to consider the channel orthogonality. For example, CSI for WB may be utilized to alleviate complexity.

[0191] For example, for the SUS, for the first layer among the candidate layer set, the terminal with the largest SU (single user) PF may be selected. For example, the base station (1101) may calculate the SU PF of each terminal in the MU pool and select the terminal with the largest SU PF among them. Thereafter, for a specific layer after the first layer among the candidate layer set, the terminal having the highest orthogonality channel with the previous layer (e.g., the first layer) may be selected. For example, without considering the MU sum PF, the terminal may be selected for the specific layer so that the MU sum capacity has the maximum value. Alternatively, for example, the terminal may be selected for the specific layer so that the MU sum PF has the maximum value. For specific details on terminal selection based on the sum of PF metrics (or MU sum PF), reference may be made to FIG. 12 below.

[0192] Figure 12 illustrates an example of a method for sorting terminals. Figure 12 illustrates an example of a null space (1200) for sorting terminals.

[0193] For example, the base station (1101) can calculate a matrix (Q) defining a null space (1200) through a span operation of a set (φ) of pre-selected channel vectors (or basis vectors). For example, the span operation can represent an LC operation of the channel vectors. The channel vectors can utilize L LC coefficients obtained in the SD DFT compressed basis domain based on SRS channel information and PMI channel information. For example, the LC coefficients can represent coefficients of a reduced dimension.

[0194] For example, the base station (1101) can perform a null operation on the matrix (Q). For example, the null operation can be used to identify an orthogonal vector (1240) on the null space (1200). For example, the vector (1240) can be identified by an operation on the first channel vector (1210) and the second channel vector (1220).

[0195]

[0196] For example, the base station (1101) can calculate a PF metric based on the channel gain loss. At this time, the effective channel gain is assumed to be the length (b).

[0197] For example, the base station (1101) can repeatedly perform the operations described above for all channel vectors in the set (φ). Thereafter, the base station (1101) can add the PMI vector having the maximum PF metric to the set (φ). The base station (1101) can repeatedly perform the operations described above for each layer. In other words, the base station (1101) can select terminals for candidate layers so that the sum of the PF metrics has the maximum value by calculating the effective channel gain and null space vector according to the layer and the UE.

[0198] In one example, the base station (1101) can maximize (or improve) cell throughput, user perceived throughput, or cell IP throughput by using the operations of FIG. 12 when calculating MU sum capacity or MU sum PF.

[0199] According to one embodiment, the base station (1101) can calculate the correlation between terminals with relatively low complexity by using reduced dimension coefficients, and can also reduce the complexity of calculating channel gain loss and null space vectors. For example, the base station (1101) can reduce the complexity of calculating the null space between terminals by considering only the portions where the beam indices of the terminals match (i.e., considering the correlation information between the terminals). In addition, for example, the base station (1101) can reduce the complexity of calculating the null space between terminals by using unified beam indices between the terminals. Calculating the null space can be understood as selecting a terminal with high orthogonality by considering the correlation between the terminals.

[0200] Referring to the above, the base station (1101) can select terminals to be scheduled from among the terminals within the MU pool. For example, the combination of terminals to be scheduled can be determined based on correlation information between the terminals within the combination. For example, the correlation information between terminals can be used in calculating the null space vector and the effective channel gain.

[0201] For example, in operation (1166), the base station (1101) may perform MU layer decision. For example, the base station (1101) may determine transmission layers from the candidate layer set used to determine terminals in the MU pool. For example, the base station (1101) may compare the MU sum PF of the upper n-th layers in the candidate layer set. The MU sum PF for the n-th layers may be calculated as in Equation 11. When calculating the MU sum PF, if the number of retransmission layers is m, n may have a value greater than or equal to m.

[0202] For example, the number of transmission layers selected based on the above MU sum PF can be determined according to the following mathematical formula.

[0203] [Correction pursuant to Rule 91, March 7, 2025]

[0204]

[0205]

[0206] Although not illustrated in FIG. 11b, the base station (1101) can refine beamforming weights. For example, the beamforming weights can be calculated for each PRG (precoding resource block group) using corresponding channel information along with the scheduled results. For example, the beamforming weights can be referenced by the following mathematical equations.

[0207] [Correction pursuant to Rule 91, March 7, 2025]

[0208] [Correction pursuant to Rule 91, March 7, 2025]

[0209]

[0210] In FIG. 11b, methods performed by the base station (1101) are illustrated, but the present disclosure is not limited thereto. For example, terminal selection and layer determination may be performed by the DU (210) of FIG. 2a. Additionally, for example, beamforming weighting may be performed by the RU (220) (or MMU) of FIG. 2a.

[0211] Although not illustrated in FIG. 11b, the base station (1101) may generate precoding information according to the terminals selected by MU scheduling and the determined transmission layers, and transmit downlink data to the selected terminals using the generated precoding information. For example, the precoding information may be determined using the CSI (or PMI), channel information (or compressed channel information) of the SRS, and beamforming weights of the selected terminals.

[0212] Figure 13 illustrates an example of an operational flow for a method in which a DU (digital unit) calculates correlation information between terminals and performs downlink transmission.

[0213] At least some of the above methods of FIG. 13 may be performed by the DU (210) of FIG. 2A. For example, at least some of the above methods may be controlled by the processor (330) of the electronic device (or device, apparatus, node, entity) of the DU (210). In the following embodiments, each operation may be performed sequentially, but is not necessarily performed sequentially. For example, the order of each operation may be changed, and at least two operations may be performed in parallel.

[0214] In operation (1310), DU (210) can obtain CSI of each of UEs including first UE and second UE. For example, DU (210) can obtain first CSI of the first UE. For example, DU (210) can obtain second CSI of the second UE. For example, the first CSI and the second CSI can be obtained through an RU (e.g., RU (220) of FIG. 2A) connected to DU (210). In the above example, an example in which CSIs of the first UE and the second UE are obtained is described, but the present disclosure is not limited thereto. For example, DU (210) can obtain CSI from each of the UEs.

[0215] For example, the first CSI may include a first PMI. For example, the first PMI may be used to indicate a first basis vector identified by the first UE. For example, the second CSI may include a second PMI. For example, the second PMI may be used to indicate a second basis vector identified by the second UE.

[0216] In operation (1320), the DU (210) may obtain channel information for the SRS of the first UE. For example, the DU (210) may obtain the channel information for the SRS transmitted from the first UE. For example, the SRS may be received through the RU (220) connected to the DU (210). For example, the channel information may be generated by estimating from the SRS by the RU (220) or the DU (210). For example, the channel information may be referred to as a channel matrix or channel estimation information.

[0217] In operation (1330), the DU (210) may generate compressed channel information using the first basis vector and the channel information obtained from the first CSI of the first UE. For example, the DU (210) may generate the compressed channel information including basis vectors representing a plurality of beams including a beam determined based on the first basis vector and LC coefficients for the basis vectors. For example, the basis vectors included in the compressed channel information may include the first basis vector, which is a main basis vector, and at least one residual basis vector.

[0218] In operation (1340), DU (210) may calculate correlation information between the first UE and the second UE using the compressed channel information and the second basis vector obtained from the second CSI of the second UE. For example, DU (210) may calculate correlation information through an LC operation between each of the basis vectors in the compressed channel information and the second basis vector.

[0219] In operation (1350), DU (210) may select UEs to be scheduled, including the first UE, among the UEs based on the calculated correlation information. For example, DU (210) may generate a candidate layer set for the UEs within the MU pool. For example, the candidate layer set may include up to 8 or 16 layers by considering the PF metric and channel orthogonality. However, the present disclosure is not limited thereto.

[0220] For example, for the SUS, for the first layer among the candidate layer set, the UE with the largest single user (SU) PF may be selected. For example, the DU (210) may calculate the SU PF of each UE in the MU pool and select the UE with the largest SU PF among them. Thereafter, for the layers after the first layer among the candidate layer set, the UE with the highest orthogonality channel with the previous layer (e.g., the first layer) may be selected.

[0221] For example, the combination of UEs to be scheduled may be determined based on correlation information between UEs within the combination. For example, DU (210) may calculate an effective channel gain and a null region vector according to a layer and a UE using a set of correlation information between UEs including the correlation information between the first UE and the second UE. For example, DU (210) may calculate a PF metric according to the effective channel gain and the null region vector. For example, the UEs to be scheduled may be determined based on the PF metric.

[0222] For example, DU (210) can perform MU layer decision. For example, DU (210) can determine the transport layers from the candidate layer set used to determine the UEs in the MU pool. For example, DU (210) can compare the MU sum PF (MU sum PF metric) of the upper nth layers in the candidate layer set. The MU sum PF for the nth layers can be calculated as in the above mathematical expression (11).

[0223] For example, DU (210) can determine the transmission layers for the selected UEs (or the UEs to be scheduled) based on the sum of the PF metrics (or MU sum PF). For example, DU (210) can determine beamforming weights for the transmission layers based on the sum of the PF metrics (or MU sum PF). For example, the number of the transmission layers can be determined according to the MU sum PF with the maximum value.

[0224] In operation (1360), DU (210) may transmit downlink data to the selected UEs via RU (220) according to the precoding information determined for the selected UEs. For example, the precoding information may be determined using the first CSI and the compressed channel information. For example, the precoding information may be determined based on the compressed channel information generated by the first PMI. For example, the precoding information may be determined further using the beamforming weight.

[0225] Although not shown in FIG. 13, DU (210) can calculate correlation information between PMI UE and PMI UE. For example, DU (210) can further obtain third CSI of a third UE included in the UEs. For example, DU (210) can calculate correlation information between the first UE and the third UE using a third basis vector indicated by the third PMI of the third CSI. For example, the correlation information between the first UE and the third UE can be determined using a difference between a first index indicating the first basis vector and a second index indicating the third basis vector and an LUT for the difference.

[0226] In addition, the DU (210) can calculate correlation information between the SRS UE and the SRS UE. For example, the DU (210) can obtain other channel information for the SRS of the fourth UE included in the UEs. For example, the DU (210) can obtain the other channel information for the SRS transmitted from the fourth UE. For example, the DU (210) can generate compressed other channel information using the fourth basis vector of the fourth CSI (or fourth PMI) of the fourth UE and the other channel information. For example, the DU (210) can calculate correlation information between the first UE and the fourth UE using the compressed channel information and the compressed other channel information. For example, in order to reduce computational complexity, the oversampling factors for the compressed channel information can correspond to the oversampling factors for the compressed other channel information. In other words, the compressed channel information and the compressed other channel information can have a unified dimension.

[0227] Referring to FIGS. 1 to 13, the device, method, and storage medium according to the present disclosure can perform spatial compression on acquired CSI (e.g., PMI for Type 1 codebook) and utilize basis vectors and low-dimensional linear coefficients. The device, method, and storage medium according to the present disclosure can perform an operation of an equivalent rho matrix (or correlation) in a reduced dimension using spatial compression, and perform MU scheduling with low complexity based on basis vectors.

[0228] In one example, the device, method, and storage medium according to the present disclosure can utilize the PMI index gap between terminals obtained through PMI parsing. The device, method, and storage medium according to the present disclosure can calculate the correlation between terminals using the PMI index gap.

[0229] In one example, the device, method, and storage medium according to the present disclosure can manage channel information by decomposing it into LC (linear combination) weights by obtaining residual basis vectors based on a dominant basis vector among basis vectors based on PMI. By converting it into a low-dimensional LC expressed by a finite number of orthogonal basis vectors, the complexity for calculating correlations between terminals can be reduced.

[0230] The device, method, and storage medium according to the present disclosure can reduce complexity by using a table (e.g., a look-up table (LUT)) according to PMI index differences for correlations between terminals in a PMI MU pool. In addition, the device, method, and storage medium according to the present disclosure can reduce complexity by using PMI-based spatial domain beam information (e.g., basis vectors in a spatial domain) and compressed channel information (e.g., information compressed from channel information of SRS) for correlations between terminals in an SRS MU pool or between terminals in an SRS MU pool and terminals in a PMI MU pool. Accordingly, the device, method, and storage medium according to the present disclosure can calculate channel gain and null space vectors with relatively low complexity by using unified orthogonal basis vectors between terminals. The device, method, and storage medium according to the present disclosure can perform MU-MIMO scheduling with relatively low complexity.

[0231] 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 can be clearly understood by a person having ordinary skill in the art to which the present disclosure belongs from the description below.

[0232] As described above, a device of a digital unit (DU) may include at least one processor including a processing circuit. The device may include a memory storing instructions and including one or more storage media. The instructions, when individually or collectively executed by the at least one processor, may cause the device to obtain channel state information (CSI) of each of UEs including a first user equipment (UE) and a second UE. The instructions, when individually or collectively executed by the at least one processor, may cause the device to obtain channel information for a sounding reference signal (SRS) of the first UE. The instructions, when individually or collectively executed by the at least one processor, may cause the device to generate compressed channel information using a first basis vector obtained from the first CSI of the first UE and the channel information. The instructions, when individually or collectively executed by the at least one processor, may cause the device to calculate correlation information between the first UE and the second UE using the compressed channel information and a second basis vector obtained from the second CSI of the second UE. The instructions, when individually or collectively executed by the at least one processor, may cause the device to select UEs to be scheduled, including the first UE, from among the UEs based on the calculated correlation information.The above instructions, when individually or collectively executed by the at least one processor, may cause the device to transmit downlink data to the selected UEs via a radio unit (RU) according to precoding information determined for the selected UEs.

[0233] According to one embodiment, the first basis vector may be indicated by a precoding matrix indicator (PMI) of the first CSI for a Type 1 codebook. The first basis vector may be determined using the number of transmit antennas in the first dimension, the number of transmit antennas in the second dimension, a first oversampling factor in the first dimension, and a second oversampling factor in the second dimension.

[0234] According to one embodiment, the compressed channel information may include basis vectors representing a plurality of beams including a beam determined based on the first basis vector, and linear combination (LC) coefficients for the basis vectors. The precoding information may be determined using the first CSI and the compressed channel information.

[0235] According to one embodiment, the instructions, when individually or collectively executed by the at least one processor, may cause the device to calculate correlation information between the first UE and the third UE using the first basis vector of the first UE and the third basis vector of the third CSI of the third UE among the UEs. The selected UEs, including the first UE, may be selected based on the correlation information between the first UE and the third UE.

[0236] According to one embodiment, the correlation information between the first UE and the third UE may be determined using a gap between a first index indicating the first basis vector and a second index indicating the third basis vector and a look up table (LUT) for the gap.

[0237] According to one embodiment, the instructions, when individually or collectively executed by the at least one processor, may cause the device to obtain other channel information for an SRS of a third UE among the UEs. The instructions, when individually or collectively executed by the at least one processor, may cause the device to generate compressed other channel information using a third basis vector of a third CSI of the third UE and the other channel information. The instructions, when individually or collectively executed by the at least one processor, may cause the device to calculate correlation information between the first UE and the third UE using the compressed channel information and the compressed other channel information. The selected UEs, including the first UE, may be selected based on the correlation information between the first UE and the third UE.

[0238] According to one embodiment, the oversampling factors for the compressed channel information may correspond to the oversampling factors for the other compressed channel information.

[0239] In one embodiment, the instructions, when individually or collectively executed by the at least one processor, may cause the device to calculate an effective channel gain and a null space vector according to a layer and a UE using the set of correlation information between the UEs including the correlation information between the first UE and the second UE. The instructions, when individually or collectively executed by the at least one processor, may cause the device to calculate a proportional fair (PF) metric according to the effective channel gain and the null space vector. The selected UEs may be determined based on the PF metric.

[0240] According to one embodiment, the instructions, when individually or collectively executed by the at least one processor, may cause the device to determine transmission layers for the selected UEs and beamforming weights for the transmission layers. The precoding information may be determined using the beamforming weights.

[0241] According to one embodiment, the number of the transmission layers may be determined based on the sum of the PF metrics of the transmission layers having the maximum value.

[0242] As described above, a method performed by a digital unit (DU) may include an operation of acquiring channel state information (CSI) of each of UEs including a first user equipment (UE) and a second UE. The method may include an operation of acquiring channel information for a sounding reference signal (SRS) of the first UE. The method may include an operation of generating compressed channel information using a first basis vector acquired from the first CSI of the first UE and the channel information. The method may include an operation of calculating correlation information between the first UE and the second UE using the compressed channel information and a second basis vector acquired from the second CSI of the second UE. The method may include an operation of selecting UEs to be scheduled, including the first UE, from among the UEs based on the calculated correlation information. The method may include an operation of transmitting downlink data to the selected UEs through a radio unit (RU) according to precoding information determined for the selected UEs.

[0243] According to one embodiment, the first basis vector may be indicated by a precoding matrix indicator (PMI) of the first CSI for a Type 1 codebook. The first basis vector may be determined using the number of transmit antennas in the first dimension, the number of transmit antennas in the second dimension, a first oversampling factor in the first dimension, and a second oversampling factor in the second dimension.

[0244] According to one embodiment, the compressed channel information may include basis vectors representing a plurality of beams including a beam determined based on the first basis vector, and linear combination (LC) coefficients for the basis vectors. The precoding information may be determined using the first CSI and the compressed channel information.

[0245] According to one embodiment, the method may include calculating correlation information between the first UE and the third UE using the first basis vector of the first UE and the third basis vector of the third CSI of the third UE among the UEs. The selected UEs, including the first UE, may be selected based on the correlation information between the first UE and the third UE.

[0246] According to one embodiment, the correlation information between the first UE and the third UE may be determined using a gap between a first index indicating the first basis vector and a second index indicating the third basis vector and a look up table (LUT) for the gap.

[0247] According to one embodiment, the method may include an operation of obtaining other channel information for an SRS of a third UE among the UEs. The method may include an operation of generating compressed other channel information using a third basis vector of a third CSI of the third UE and the other channel information. The method may include an operation of calculating correlation information between the first UE and the third UE using the compressed channel information and the compressed other channel information. The selected UEs, including the first UE, may be selected based on the correlation information between the first UE and the third UE.

[0248] According to one embodiment, the oversampling factors for the compressed channel information may correspond to the oversampling factors for the other compressed channel information.

[0249] According to one embodiment, the method may include calculating an effective channel gain and a null space vector according to a layer and a UE using a set of correlation information between the UEs, the set including the correlation information between the first UE and the second UE. The method may include calculating a proportional fair (PF) metric according to the effective channel gain and the null space vector. The selected UEs may be determined based on the PF metric.

[0250] According to one embodiment, the method may include determining transmission layers for the selected UEs and beamforming weights for the transmission layers. The precoding information may be determined using the beamforming weights. The number of transmission layers may be determined based on the sum of the PF metrics of the transmission layers having the maximum value.

[0251] The non-transitory computer-readable storage medium as described above may store one or more programs including instructions that, when individually or collectively executed by at least one processor of a device of a digital unit (DU), cause the device to obtain channel state information (CSI) of each of UEs including a first user equipment (UE) and a second UE. The non-transitory computer-readable storage medium may store one or more programs including instructions that, when individually or collectively executed by the at least one processor, cause the device to obtain channel information for a sounding reference signal (SRS) of the first UE. The non-transitory computer-readable storage medium may store one or more programs including instructions that, when individually or collectively executed by the at least one processor, cause the device to generate and cause compressed channel information using a first basis vector obtained from the first CSI of the first UE and the channel information. The non-transitory computer-readable storage medium may store one or more programs including instructions that, when individually or collectively executed by the at least one processor, cause the device to calculate correlation information between the first UE and the second UE using the compressed channel information and a second basis vector obtained from the second CSI of the second UE.The non-transitory computer-readable storage medium may store one or more programs including instructions that, when individually or collectively executed by the at least one processor, cause the device to select UEs to be scheduled, including the first UE, from among the UEs based on the calculated correlation information. The non-transitory computer-readable storage medium may store one or more programs including instructions that, when individually or collectively executed by the at least one processor, cause the device to transmit downlink data to the selected UEs via a radio unit (RU) according to precoding information determined for the selected UEs.

[0252] The methods according to the embodiments described in the claims or specification of the present disclosure may be implemented in the form of hardware, software, or a combination of hardware and software.

[0253] When implemented in software, a computer-readable storage medium storing one or more programs (software modules) may be provided. The one or more programs stored in the computer-readable storage medium are configured to be executed by one or more processors in an electronic device. The one or more programs include instructions that cause the electronic device to execute methods according to embodiments described in the claims or specification of the present disclosure. The one or more programs may be provided as a computer program product. The computer program product may be traded between a seller and a buyer as a commodity. The computer program product may be distributed in the form of a machine-readable storage medium (e.g., compact disc read only memory (CD-ROM)), or may be distributed online (e.g., downloaded or uploaded) via an application store (e.g., Play Store™) or directly between two user devices (e.g., smart phones). In the case of online distribution, at least a portion of the computer program product may be temporarily stored or temporarily created in a device-readable storage medium, such as the memory of a manufacturer's server, an application store's server, or an intermediary server.

[0254] These programs (software modules, software) may be stored in random access memory, non-volatile memory including flash memory, read only memory (ROM), electrically erasable programmable read only memory (EEPROM), magnetic disc storage devices, compact disc-ROM (CD-ROM), digital versatile discs (DVDs) or other forms of optical storage devices, magnetic cassettes, or may be stored in memories formed by a combination of some or all of these. In addition, each configuration memory may include multiple copies.

[0255] Additionally, the program may be stored on an attachable storage device that is accessible via a communication network, such as the Internet, an intranet, a local area network (LAN), a wide area network (WAN), a storage area network (SAN), or a combination thereof. Such a storage device may be connected to a device implementing an embodiment of the present disclosure via an external port. Additionally, a separate storage device on the communication network may be connected to a device implementing an embodiment of the present disclosure.

[0256] In the specific embodiments of the present disclosure described above, components included in the disclosure are expressed singularly or plurally, depending on the specific embodiment presented. However, the singular or plural expressions are selected to suit the presented situation for convenience of explanation, and the present disclosure is not limited to singular or plural components. Components expressed in plural may be composed of singular elements, or components expressed in singular may be composed of plural elements.

[0257] According to embodiments, one or more of the components or operations of the aforementioned components may be omitted, or one or more other components or operations may be added. Alternatively or additionally, a plurality of components (e.g., modules or programs) may be integrated into a single component. In such a case, the integrated component may perform one or more functions of each of the plurality of components identically or similarly to those performed by the corresponding component among the plurality of components prior to the integration. According to embodiments, the operations performed by a module, program, or other component may be executed sequentially, in parallel, iteratively, or heuristically, or one or more of the operations may be executed in a different order, omitted, or one or more other operations may be added.

[0258] Meanwhile, although the detailed description of the present disclosure has described specific embodiments, it is obvious that various modifications are possible within the scope of the present disclosure.

Claims

1. In the DU (digital unit) device, At least one processor comprising a processing circuit; and A memory storing instructions and including one or more storage media, The above instructions, when individually or collectively executed by the at least one processor, cause the device to: Acquire CSI (channel state information) of each of the UEs including the first UE (user equipment) and the second UE, Obtain channel information for the SRS (sounding reference signal) of the first UE, Generating compressed channel information using the first basis vector and the channel information obtained from the first CSI of the first UE, Using the compressed channel information and the second basis vector obtained from the second CSI of the second UE, correlation information between the first UE and the second UE is calculated, Selecting UEs to be scheduled, including the first UE, among the UEs based on the calculated correlation information, and Causing downlink data to be transmitted to the selected UEs through a RU (radio unit) according to the precoding information determined for the selected UEs. device.

2. In claim 1, The first basis vector is indicated by the PMI (precoding matrix indicator) of the first CSI for the Type 1 codebook, and The first basis vector is determined using the number of transmission antennas in the first dimension, the number of transmission antennas in the second dimension, the first oversampling factor in the first dimension, and the second oversampling factor in the second dimension. device.

3. In claim 1, The compressed channel information includes basis vectors representing a plurality of beams including a beam determined based on the first basis vector and LC (linear combination) coefficients for the basis vectors, and The above precoding information is determined using the first CSI and the compressed channel information. device.

4. In claim 1, The above instructions, when individually or collectively executed by the at least one processor, cause the device to: By using the first basis vector of the first UE and the third basis vector of the third CSI of the third UE among the UEs, correlation information between the first UE and the third UE is calculated, The selected UEs including the first UE are selected based on the correlation information between the first UE and the third UE. device.

5. In claim 4, The correlation information between the first UE and the third UE is determined using a difference (gap) between a first index indicating the first basis vector and a second index indicating the third basis vector and a look up table (LUT) for the difference. device.

6. In claim 1, The above instructions, when individually or collectively executed by the at least one processor, cause the device to: Obtain other channel information for the SRS of the third UE among the above UEs, Generating compressed other channel information using the third basis vector of the third CSI of the third UE and the other channel information, and By using the compressed channel information and the other compressed channel information, cause correlation information between the first UE and the third UE to be calculated, The selected UEs including the first UE are selected based on the correlation information between the first UE and the third UE. device.

7. In claim 6, The oversampling factors for the above compressed channel information correspond to the oversampling factors for the other compressed channel information. device.

8. In claim 1, The above instructions, when individually or collectively executed by the at least one processor, cause the device to: Using the correlation information set between the UEs including the correlation information between the first UE and the second UE, an effective channel gain and a null space vector according to a layer and a UE are calculated, and To cause the calculation of the PF (proportional fair) metric according to the above effective channel gain and the above null region vector, The above selected UEs are determined based on the PF metric. device.

9. In claim 8, The above instructions, when individually or collectively executed by the at least one processor, cause the device to: Causing to determine transmission layers for the selected UEs and beamforming weights for the transmission layers, The above precoding information is determined using the beamforming weight. device.

10. In claim 9, The number of the above transmission layers is determined according to the sum of the PF metrics of the above transmission layers having the maximum value. device. In a method performed by a device of 11.DU (digital unit), the method comprises: An operation of acquiring channel state information (CSI) of each of UEs including a first UE (user equipment) and a second UE; An operation of obtaining channel information for an SRS (sounding reference signal) of the first UE; An operation of generating compressed channel information using a first basis vector obtained from the first CSI of the first UE and the channel information; An operation of calculating correlation information between the first UE and the second UE using the compressed channel information and the second basis vector obtained from the second CSI of the second UE; An operation of selecting UEs to be scheduled, including the first UE, among the UEs based on the calculated correlation information; and An operation of transmitting downlink data to the selected UEs through a RU (radio unit) according to precoding information determined for the selected UEs, method.

12. In claim 11, The first basis vector is indicated by the PMI (precoding matrix indicator) of the first CSI for the Type 1 codebook, and The first basis vector is determined using the number of transmission antennas in the first dimension, the number of transmission antennas in the second dimension, the first oversampling factor in the first dimension, and the second oversampling factor in the second dimension. method.

13. In claim 11, The compressed channel information includes basis vectors representing a plurality of beams including a beam determined based on the first basis vector and LC (linear combination) coefficients for the basis vectors, and The above precoding information is determined using the first CSI and the compressed channel information. method.

14. In claim 11, The above method is: An operation of calculating correlation information between the first UE and the third UE by using the first basis vector of the first UE and the third basis vector of the third CSI of the third UE among the UEs, The selected UEs including the first UE are selected based on the correlation information between the first UE and the third UE. method.

15. A non-transitory computer-readable storage medium, when individually or collectively executed by at least one processor of a device of a digital unit (DU), causes the device to: Acquire CSI (channel state information) of each of the UEs including the first UE (user equipment) and the second UE, Obtain channel information for the SRS (sounding reference signal) of the first UE, Generating compressed channel information using the first basis vector and the channel information obtained from the first CSI of the first UE, Using the compressed channel information and the second basis vector obtained from the second CSI of the second UE, correlation information between the first UE and the second UE is calculated, Selecting UEs to be scheduled, including the first UE, among the UEs based on the calculated correlation information, and Storing one or more programs including instructions that cause downlink data to be transmitted to the selected UEs via a radio unit (RU) according to precoding information determined for the selected UEs. Non-transitory computer-readable storage medium.

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