Electronic device and method for transmitting and receiving signal in multiple input multiple output system

Abstract

Provided is a radio unit (RU) device in embodiments. The device may comprise: a radio frequency (RF) transceiver; a fronthaul transceiver; a memory for storing instructions; and a processor. The instructions, when executed by the processor, cause the device to: acquire reference signals via the RF transceiver; acquire a transformed covariance matrix of the reference signals with respect to noise and interference through random embedding for dimensionality reduction; acquire an extraction matrix through the subspace decomposition of the transformed covariance matrix; use the extraction matrix to pre-combine uplink signals acquired via the RF transceiver; and transmit data of the pre-combined uplink signals to a digital unit (DU) via the fronthaul transceiver. The random embedding may be used to transform a first dimension of the number of reception layers of the RU into a second dimension of a number smaller than the number of reception layers.

Description

Electronic device and method for transmitting and receiving signals in a multiple input / output system

[0001] The present disclosure relates to a multiple input multiple output (MIMO) system. For example, the present disclosure relates to an electronic device and method for transmitting and receiving signals in a MIMO system.

[0002] To improve signal transmission and reception performance, multiple-input multiple-output (MIMO) technology is used. Both transmitters and receivers utilizing MIMO technology can utilize multiple antennas. 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-described matters constitute prior art related to the present disclosure.

[0004] In embodiments, a device of a radio unit (RU) is provided. The device may include a radio frequency (RF) transceiver, a fronthaul transceiver, a memory storing instructions, and a processor. The instructions, when executed by the processor, may cause the device to obtain reference signals through the RF transceiver, obtain a transform covariance matrix for noise and interference of the reference signals through random embedding for dimensionality reduction, obtain an extraction matrix through subspace decomposition of the transform covariance matrix, perform pre-combining using the extraction matrix on uplink signals obtained through the RF transceiver, and transmit data of the uplink signals on which the pre-combining has been performed to a digital unit (DU) through the fronthaul transceiver. The above random embedding can be used to transform a first dimension having the number of receiving layers of the RU into a second dimension having a number smaller than the number of receiving layers.

[0005] In embodiments, a device of a digital unit (DU) is provided. The device may include a transceiver, a memory storing instructions, and a processor. The instructions, when executed by the processor, may cause the device to obtain reference signals from a radio unit (RU) via the transceiver, obtain a transform covariance matrix for noise and interference of the reference signals through random embedding for dimensionality reduction, obtain an extraction matrix through subspace decomposition of the transform covariance matrix, and perform equalization on uplink signals from the RU using the extraction matrix, thereby obtaining transmission signals. The random embedding may be used to transform a first dimension having a number of reception layers of the RU into a second dimension having a number smaller than the number of reception layers.

[0006] In embodiments, a device of a radio unit (RU) is provided. The device may include a radio frequency (RF) transceiver, a fronthaul transceiver, a memory storing instructions, and a processor. The instructions, when executed by the processor, may cause the device to obtain a covariance matrix for noise and interference through reference signals received through the RF transceiver, obtain a noise vector matrix by performing eigen decomposition of the covariance matrix, obtain a channel matrix for the reference signals and a filter matrix using the noise vector matrix, obtain transformed signals by applying the filter matrix to uplink signals obtained through the RF transceiver, and transmit data of the transformed signals to a digital unit (DU) through the fronthaul transceiver. The number of columns of the noise vector matrix may be smaller than the number of reception layers of the RU.

[0007] In embodiments, a device of a radio unit (RU) may include a radio frequency (RF) transceiver, a fronthaul transceiver, a memory storing instructions, and a processor. The instructions, when executed by the processor, may cause the device to obtain reference signals through the RF transceiver, obtain a transformed covariance matrix for noise and interference through the reference signals, obtain an extraction matrix having column vectors corresponding to a specified number of column vectors of an orthogonal matrix of the QR decomposition through a QR solution of the covariance matrix, obtain a filter matrix using a projection matrix obtained from the extraction matrix and a channel matrix for the reference signals, obtain transformed signals by applying the filter matrix to uplink signals obtained through the RF transceiver, and transmit data of the transformed signals to a digital unit (DU) through the fronthaul transceiver.

[0008] Figure 1 shows a wireless communication system.

[0009] Figure 2a illustrates a fronthaul interface.

[0010] Figure 2b shows the fronthaul interface of an O(open)-RAN(radio access network).

[0011] Figure 3a shows the components of a distributed unit (DU).

[0012] Figure 3b shows the components of a RU (radio unit).

[0013] Figure 4 shows an example of function split between DU and RU.

[0014] Figure 5 shows an example of a transmitter and a receiver.

[0015] Figure 6 shows an example of subspace decomposition using random embedding.

[0016] Figure 7 shows an example of subspace decomposition using random embedding.

[0017] Figure 8 shows an example of precoding or pre-combining using subspace decomposition.

[0018] Figure 9 shows an example of a receiving operation using eigen decomposition of the covariance matrix for noise and interference.

[0019] Figure 10 shows an example of a receiving operation using the null space of the covariance matrix for noise and interference.

[0020] Figure 11 shows an example of a receiving operation using the null space of the covariance matrix for noise and interference.

[0021] Figure 12 shows an example of a receiving operation using weight interpolation.

[0022] Figures 13a and 13b show examples of the performance of a receiving operation using random embedding and null space.

[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., signal, information, message, signaling), terms referring to data types (e.g., list, set, subset), terms for operational states (e.g., step, operation, procedure), terms referring to data (e.g., packet, user stream, information, bit, symbol, codeword), terms referring to resources (e.g., symbol, slot, subframe, radio frame, subcarrier, resource element (RE), resource block (RB), bandwidth part (BWP), occasion), terms referring to channels, terms referring to network entities, terms referring to components of devices, etc. are examples for convenience of description. Therefore, the present disclosure is not limited to the terms described below, and other terms having equivalent technical meanings may be used.

[0026] In the following description, terms referring to signals (e.g., signal, information, message, signaling), terms referring to resources (e.g., symbol, slot, subframe, radio frame, subcarrier, resource element (RE), resource block (RB), bandwidth part (BWP), occasion), terms for operational states (e.g., step, operation, procedure), terms referring to data (e.g., packet, user stream, information, bit, symbol, codeword), terms referring to channels, terms referring to network entities, terms referring to components of devices, etc. are examples for convenience of description. Therefore, the present disclosure is not limited to the terms described below, and other terms having equivalent technical meanings may be used.

[0027] 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"}.

[0028] Although the present disclosure describes various embodiments using terms used in some communication standards (e.g., 3rd Generation Partnership Project (3GPP), European Telecommunications Standards Institute (ETSI), extensible radio access network (xRAN), open-radio access network (O-RAN), etc.), these are merely examples for explanation. The various embodiments of the present disclosure can be easily modified and applied to other communication systems.

[0029] Figure 1 shows a wireless communication system.

[0030] 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).

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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).

[0038] In the past, 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 DU (distributed unit)) and an RF (radio frequency) processing unit (or RU (radio unit)). However, in 4G (4 th As higher frequency bands are used in the 5G 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 installation cost burden on operators to install base stations has also increased. In order to minimize the installation cost of base stations, a structure has been proposed in which the DU and RU 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. Hereinafter, the deployment structure and expansion examples of base stations according to various embodiments of the present disclosure are described through FIGS. 2A and 2B.

[0039] Fig. 2a illustrates a fronthaul interface. Unlike the backhaul between the base station and the core network, fronthaul refers to the entity between the wireless LAN and the 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 can also be applied to a fronthaul structure between one DU and multiple RUs. For example, embodiments of the present disclosure can be applied to a fronthaul structure between one DU and two RUs. Furthermore, embodiments of the present disclosure can also be applied to a fronthaul structure between one DU and three RUs.

[0040] Referring to FIG. 2A, the base station (110) may include a DU (210) and a RU (220). The fronthaul (215) between the DU (210) and the RU (220) is F x It can be operated through an interface. For operation of the fronthaul (215), interfaces such as eCPRI (enhanced common public radio interface) and ROE (radio over ethernet) can be used, for example.

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

[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. An example of such specific functional separation is described in detail in FIG. 4. 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. In one embodiment, if RU (220) complies with the O-RAN standard, it may be referred to as O-RU (O-RAN RU). RU (220) may be replaced with a second network entity for a base station (e.g., gNB) in embodiments of the present disclosure, as needed.

[0044] Although FIG. 2A illustrates that the base station (110) includes a DU (210) and a RU (220), 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. In this case, the distributed unit (DU) may correspond to the digital unit (DU) or include at least a part of the functional components of the digital unit (DU). Between the core (e.g., 5GC (5G core) or NGC (next generation core)) network and the radio network (RAN), the base station may be implemented in a structure in which the CU, DU, and RU are arranged in that order. The interface between CU and DU (distributed unit) can be referred to as the F1 interface.

[0045] A centralized unit (CU) can be connected to one or more DUs and can be responsible for functions at a higher layer than the DU. For example, the CU can be responsible for functions at the RRC (radio resource control) and PDCP (packet data convergence protocol) layers, while the DU and RU can be responsible for functions at lower layers. The DU can perform some functions (high PHY) of the RLC (radio link control), MAC (media access control), and PHY (physical) layers, while the RU can be responsible for the remaining functions (low PHY) of the PHY layer. In addition, for example, a digital unit (DU) can 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 the DU is directly connected to the core network (i.e., a base station in which the CU and DU are integrated into one entity (e.g., an NG-RAN node)).

[0046] Figure 2b illustrates a fronthaul interface of an open RAN (radio access network). A base station (110) according to a distributed deployment is exemplified by an eNB or gNB.

[0047] Referring to FIG. 2b, the base station (110) may include an O-DU (251) and O-RUs (253-1, ..., 253-n). Hereinafter, for convenience of explanation, the operation and function of the O-RU (253-1) may be understood as a description of each of the other O-RUs (e.g., O-RU (253-n)).

[0048] The O-DU (251) is a logical node that includes functions, excluding functions exclusively assigned to the O-RU (253-1), among the functions of a base station (e.g., eNB, gNB) according to FIG. 4 described below. The O-DU (251) can control the operation of the O-RUs (253-1, ..., 253-n). The O-DU (251) may be referred to as an LLS (lower layer split) CU (central unit). The O-RU (253-1) is a logical node that includes a subset of the functions of a base station (e.g., eNB, gNB) according to FIG. 4 described below. Real-time aspects of control plane (C-plane) communication and user plane (U-plane) communication with the O-RU (253-1) can be controlled by the O-DU (251).

[0049] The O-DU (251) can communicate with the O-RU (253-1) through an LLS interface. The LLS interface corresponds to a fronthaul interface. The LLS interface refers to a logical interface between the O-DU (251) and the O-RU (253-1) that utilizes lower layer functional split (i.e., intra-PHY based functional split). The LLS-C between the O-DU (251) and the O-RU (253-1) provides the C-plane through the LLS interface. The LLS-U between the O-DU (251) and the O-RU (253-1) provides the U-plane through the LLS interface.

[0050] In order to explain the O-RAN in FIG. 2B, the entities of the base station (110) are described as O-DU and O-RU. However, these names are not to be construed as limiting the embodiments of the present disclosure. In the embodiments described below, it goes without saying that the operations of the DU (210) can be performed by the O-DU (251). The description of the DU (210) can be applied to the O-DU (251). Similarly, in the embodiments described through FIGS. 3A to 12B, it goes without saying that the operations of the RU (220) can be performed by the O-RU (253-1). The description of the RU (220) can be applied to the O-RU (253-1).

[0051] Fig. 3a illustrates components of a DU (distributed unit). The configuration illustrated in Fig. 3a can be understood as a configuration of the DU (210) of Fig. 2a (or the O-DU (250) of Fig. 2b) as part of a base station. 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.

[0052] Referring to FIG. 3a, DU (210) includes a transceiver (310), memory (320), and processor (330).

[0053] 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. The DU (210) can communicate with a radio unit (RU) via the transceiver (310). The DU (210) can be connected to a core network or a CU in a distributed arrangement via the transceiver (310).

[0054] 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 a baseband signal and a bit stream according to the physical layer specifications of the system. For example, when transmitting data, the transceiver (310) generates complex symbols by encoding and modulating the transmitted bit stream. In addition, when receiving data, the transceiver (310) restores the received bit stream by demodulating and decoding the baseband signal. In addition, the transceiver (310) may include multiple transmission and reception paths. In addition, according to one embodiment, the transceiver (310) may be connected to the core network or other nodes (e.g., an integrated access backhaul (IAB).

[0055] The transceiver (310) can transmit and receive signals. For example, the transceiver (310) can transmit a management plane (M-plane) message. For example, the transceiver (310) can transmit a management plane (S-plane) message. For example, the transceiver (310) can transmit a control plane (C-plane) message. For example, the transceiver (310) can transmit a user plane (U-plane) message. For example, the transceiver (310) can receive a user plane message. Although only the transceiver (310) is illustrated in FIG. 3A, in other implementations, the DU (210) may include two or more transceivers.

[0056] 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.

[0057] Although not illustrated in FIG. 3A, 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.

[0058] The memory (320) stores data such as basic programs, application programs, and setting information for the operation of the DU (210). The memory (320) may be referred to as a storage unit. The memory (320) may be configured as volatile memory, non-volatile memory, or a combination of volatile memory and non-volatile memory. In addition, the memory (320) provides stored data according to a request from the processor (330). The memory (320) is a functional component and represents a storage space. For example, the memory (320) may be understood to represent not only a memory (e.g., a hard disk, flash memory, RAM) arranged as a component within the DU (210), but also a space for storing instructions and / or programs.

[0059] The processor (330) controls the overall operations of the DU (210). The processor (380) may be referred to as a control unit. For example, the processor (330) transmits and receives signals through the transceiver (310) (or through the 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 the protocol stack required by the communication standard. Although only the processor (330) is illustrated in FIG. 3A, the DU (210) may include two or more processors according to other implementation examples.

[0060] The configuration of DU (210) illustrated in FIG. 3A is merely an example, and examples of DUs performing embodiments of the present disclosure are not limited to the configuration illustrated in FIG. 3A. In some embodiments, some configurations may be added, deleted, or changed.

[0061] Fig. 3b illustrates components of a RU (radio unit). The configuration illustrated in Fig. 3b can be understood as a configuration of the RU (220) of Fig. 2b or the O-RU (253-1) of Fig. 2b as part of a base station. 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.

[0062] Referring to FIG. 3b, the RU (220) includes an RF transceiver (360), a fronthaul transceiver (365), a memory (370), and a processor (380).

[0063] The RF transceiver (360) performs functions for transmitting and receiving signals via a wireless channel. For example, the RF transceiver (360) upconverts a baseband signal into an RF band signal and transmits it via an antenna, and downconverts an RF band signal received via the antenna into a baseband signal. For example, the RF transceiver (360) may include a transmit filter, a receive filter, an amplifier, a mixer, an oscillator, a DAC, an ADC, and the like.

[0064] The RF transceiver (360) may include multiple transmission and reception paths. Furthermore, the RF transceiver (360) may include an antenna unit. The RF transceiver (360) may include at least one antenna array composed of multiple antenna elements. In terms of hardware, the RF transceiver (360) may be composed of digital circuits and analog circuits (e.g., a radio frequency integrated circuit (RFIC)). Here, the digital circuits and analog circuits may be implemented in a single package. In addition, the RF transceiver (360) may include multiple RF chains. The RF transceiver (360) may perform beamforming. The RF transceiver (360) may apply beamforming weights to a signal to be transmitted and received in order to impart directionality according to the settings of the processor (380). According to one embodiment, the RF transceiver (360) may include an RF (radio frequency) block (or RF unit).

[0065] According to one embodiment, the RF transceiver (360) can transmit and receive signals on a radio access network. For example, the RF transceiver (360) can transmit a downlink signal. The downlink signal can include a synchronization signal (SS), a reference signal (RS) (e.g., a cell-specific reference signal (CRS), a demodulation (DM)-RS), system information (e.g., a MIB, a SIB, remaining system information (RMSI), other system information (OSI)), a configuration message, control information, or downlink data. In addition, for example, the RF transceiver (360) can receive an uplink signal. The uplink signal may include a random access related signal (e.g., a random access preamble (RAP) (or Msg1 (message 1)), Msg3 (message 3)), a reference signal (e.g., a sounding reference signal (SRS), DM-RS), or a power headroom report (PHR). Although only the RF transceiver (360) is illustrated in FIG. 3b, in other implementation examples, the RU (220) may include two or more RF transceivers.

[0066] According to embodiments, the RF transceiver (460) may transmit a RIM-RS. The RF transceiver (460) may transmit a first type of RIM-RS (e.g., RIM-RS type 1 of 3GPP) to indicate the detection of far-field interference. The RF transceiver (460) may transmit a second type of RIM-RS (e.g., RIM-RS type 2 of 3GPP) to indicate the presence or absence of far-field interference.

[0067] The fronthaul transceiver (365) can transmit and receive signals. According to one embodiment, the fronthaul transceiver (365) can transmit and receive signals on the fronthaul interface. For example, the fronthaul transceiver (365) can receive a management plane (M-plane) message. For example, the fronthaul transceiver (365) can receive a management plane (S-plane) message. For example, the fronthaul transceiver (365) can receive a control plane (C-plane) message. For example, the fronthaul transceiver (365) can transmit a user plane (U-plane) message. For example, the fronthaul transceiver (365) can receive a user plane message. Although only the fronthaul transceiver (365) is shown in FIG. 3b, according to other implementation examples, the RU (220) may include two or more fronthaul transceivers.

[0068] The RF transceiver (360) and the fronthaul transceiver (365) transmit and receive signals as described above. Accordingly, all or part of the RF transceiver (360) and the fronthaul transceiver (365) may be referred to as a 'communication unit', a 'transmitter unit', a 'receiver unit', or a 'transmitter-receiver unit'. In addition, in the following description, transmission and reception performed through a wireless channel are used to mean that the processing as described above is performed by the RF transceiver (360). In the following description, transmission and reception performed through a wireless channel are used to mean that the processing as described above is performed by the RF transceiver (360).

[0069] The memory (370) stores data such as basic programs, application programs, and setting information for the operation of the RU (220). The memory (370) may be referred to as a storage unit. The memory (370) may be configured as volatile memory, non-volatile memory, or a combination of volatile memory and non-volatile memory. In addition, the memory (370) provides stored data according to a request of the processor (380). According to one embodiment, the memory (370) may include memory for conditions, commands, or setting values ​​related to the SRS transmission method. The memory (370) is a functional component and represents a storage space. For example, the memory (370) may be understood to represent not only a memory (e.g., a hard disk, a flash memory, a RAM) arranged as a component within the RU (220), but also a space for storing instructions and / or programs.

[0070] The processor (380) controls the overall operations of the RU (220). The processor (380) may be referred to as a control unit. For example, the processor (380) transmits and receives signals through the RF transceiver (360) or the fronthaul transceiver (365). In addition, the processor (380) records and reads data in the memory (370). In addition, the processor (380) may perform functions of a protocol stack required by a communication standard. Although only the processor (380) is illustrated in FIG. 3B, the RU (220) may include two or more processors according to other implementation examples. The processor (380) may be a set of instructions or codes stored in the memory (370), or may be a storage space that stores instructions / codes or instructions / codes that are at least temporarily residing in the processor (380), or may be a part of the circuitry that constitutes the processor (380). Additionally, the processor (380) may include various modules for performing communication. The processor (380) may control the RU (220) to perform operations according to the embodiments described below.

[0071] The configuration of RU (220) illustrated in FIG. 3b is merely an example, and examples of RUs performing embodiments of the present disclosure are not limited to the configuration illustrated in FIG. 3b. In some embodiments, some configurations may be added, deleted, or changed.

[0072] Figure 4 shows an example of function split between DU and RU. As wireless communication technology advances (e.g., 5G (5 thWith the introduction of 5G communication systems (or NR (new radio) communication systems), the frequency bands used have increased further. As the cell radius of the base station has become significantly smaller, the number of RUs required for installation has also increased further. Furthermore, in the 5G communication system, the amount of data transmitted has increased by a factor of up to ten, significantly increasing the transmission capacity of the wired network transmitted to the fronthaul. 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 utilized, which transfers some of the functions of the modem of the DU to the RU, thereby lowering the transmission capacity of the fronthaul.

[0073] 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.

[0074] Referring to Figure 4, 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.

[0075] In the first functional separation (405), 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 (410), 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 (410) may be referred to as Option 7-1. In the third functional separation (420a), 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 (420a) may be referred to as Option 7-2x Category A. In the fourth functional separation (420b), 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 (420b) may be referred to as Option 7-2x Category B. In the fifth functional separation (425), 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 (425) may be referred to as Option 7-2. In the sixth functional separation (430), 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 (430) may be referred to as Option 7-3. In the seventh functional separation (440), 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 (440) may be referred to as Option 6.Hereinafter, the embodiments of the present disclosure are described based on the sixth functional separation (430), but the described examples do not indicate that the application of other functional separations is excluded.

[0076] Fig. 5 illustrates an example of a transmitter (500) and a receiver (550). In Fig. 5, a transmitting node and a receiving node using a channel in a communication system (e.g., a wired communication system or a wireless communication system) or a broadcasting system are described.

[0077] Referring to FIG. 5, a transmitter (500) may provide information (510) to a receiver (550). The transmitter (500) may perform conversion between a baseband signal and a bit stream according to a physical layer standard. In order to transmit information (510) through a channel (530), the transmitter (500) may perform various operations. The transmitter (500) may include a channel encoder (511) and a modulator (513). The channel encoder (511) may encode (e.g., polar coding, LDPC (low density parity check)) information (510). The encoded information may be referred to as a codeword. The modulator (513) can modulate a codeword (e.g., Quadrature Phase Shift Keying (QPSK), 16 Quadrature Amplitude Modulation (QAM), 64 QAM). The codeword can be converted into complex symbols (which can be referred to as data symbols to distinguish it from the reference signal (515) described below) through modulation. The transmitter (500) can input the complex symbols into a resource mapper and multiplexing block (517). The resource mapping and multiplexing block (517) can map the complex symbols into resource elements (REs) within a resource grid. In the case of multi-antenna transmission, the resource mapping and multiplexing block (517) can map the complex symbols per antenna, in units of REs.The above mapping may be determined according to a multiplexing method (e.g., orthogonal frequency division multiplexing (OFDM), discrete frequency transform-spreading (DFT-S) OFDM, code division multiplexing access (CDMA)) determined by a radio access technology (RAT) between a transmitter (500) and a receiver (550). The transmitter (500) may transmit an RF signal on a channel (530) through a transmission front end (519). The transmitter (500) may generate the RF signal through signal processing (e.g., digital-to-analog converting (DAC), up-conversion) on the symbols. The transmitter (500) may transmit the RF signal on mapped REs.

[0078] The transmitter (500) may transmit not only information (510), but also a reference signal (515) for estimating and demodulating a channel (e.g., coherent demodulation). For example, the reference signal (515) may be a downlink reference signal. According to the 3GPP NR standard, the reference signal (515) may be an SS / PBCH block, a CSI-RS, a demodulation-reference signal (DM-RS), or a phase tracking (PT)-RS (reference signal). For example, the reference signal (515) may be an uplink reference signal. According to the 3GPP NR standard, the reference signal (515) may be an SRS, a DM-RS, or a PT-RS. The transmitter (500) may provide the reference signal (515) to a resource mapping and multiplexing block (517). The resource mapping and multiplexing block (517) can map symbols corresponding to the reference signal (515) (which may be referred to as reference symbols to distinguish them from the information (510) described above) into REs within the resource grid according to the type of the reference signal (515) and the predefined communication protocol. In the case of multi-antenna transmission, the resource mapping and multiplexing block (517) can map the symbols per antenna, per RE. The transmitter (500) can transmit an RF signal on a channel (530) through the transmission front end (519). The transmitter (500) can generate the RF signal through signal processing (e.g., DAC, upconversion) on the symbols. The transmitter (500) can transmit the RF signal on the mapped REs.

[0079] RF signals transmitted on the channel (530) may be affected by damage or gain reduction due to background noise, interference, fading, etc. After passing through the channel (530), the RF signals may be received through the antenna(s) of the receiver (550). The receiver (550) may receive the RF signals through the reception front end (551). The receiver (550) may obtain complex symbols of the RF signals through signal processing (e.g., down-conversion, analog-to-digital converting (ADC)) of the reception front end (551). The resource demapping and demultiplexing block (553) may transmit information about the complex symbols to the channel estimator (555) and the channel equalizer (557). The channel estimator (555) may perform channel estimation. For example, the channel estimator (555) can perform channel estimation using the received reference symbols. The channel equalizer (557) can perform equalization on the channel (530) using the channel estimation result of the channel estimator (555). The channel equalizer (557) can obtain data symbols using the equalized channel. The data symbols can be input to the demodulator (559) and the channel decoder (561). The receiver (550) can estimate the bit sequence of the information (510) transmitted from the transmitter (500) through demodulation by the demodulator (559) and decoding by the channel decoder (561). The demodulation can be performed according to the modulation scheme used in the modulator (513) of the transmitter (500). The decoding can be performed according to the channel coding scheme used in the channel encoder (511) of the transmitter (500).

[0080] The following embodiments of the present disclosure relate to techniques for processing signals in a receiver (550). References described in the present disclosure are as follows.

[0081] [1] N. Halko, PG Martinsson, JA Tropp "FINDING STRUCTURE WITH RANDOMNESS: PROBABILISTIC ALGORITHMS FOR CONSTRUCTING APPROXIMATE MATRIX DECOMPOSITIONS," SIAM Review, Vol 53, Issue 2, pp217-288, May 2011,

[0082] [2] Yezi Huang, Wanlu Lei, Chenguang Lu, and Miguel Berg, “Fronthaul Functional Split of IRC-based Beamforming for Massive MIMO Systems,” IEEE VTC2019-Fall.

[0083] In addition, before describing the embodiments of the present disclosure, the following mathematical symbols can represent the following matters.

[0084]

[0085] ● Unless otherwise stated throughout this disclosure, it is assumed that the index of the first element of a set, sequence, or vector starts from 0 (zero-based numbering).

[0086]

[0087]

[0088]

[0089]

[0090] In a massive MIMO system, various types of receivers (e.g., receiver (550)) may be utilized. For example, linear receivers such as minimum mean square error (MMSE) receivers may be utilized. For example, successive interference cancellation (SIC) receivers or nonlinear receivers that expect maximum likelihood (ML) performance using iterative equalization and decoding methods may be utilized. For example, receiver (550) may include an MMSE receiver that utilizes interference rejection combining (IRC) for interference from other cells.

[0091] A resource grid divided into a time axis and a frequency axis may include REs composed of symbols on the time axis and subcarriers on the frequency axis. In each RE, at least one receiving antenna The received signal can be expressed as follows.

[0092]

[0093]

[0094] The weight vector of the full-dimensional MMSE-IRC receiver can be represented as follows.

[0095]

[0096]

[0097]

[0098]

[0099]

[0100]

[0101]

[0102] N represents the total number of subcarriers.

[0103] In a large-scale MIMO system, throughput may increase as the number of antennas associated with a channel (530) increases. Conversely, as the number of antennas increases, the channel estimation complexity of the channel (530) may increase. In particular, the complexity may increase in the process of estimating the inverse matrix of the channel matrix of the channel (530). Various methods have been proposed to reduce this complexity. In the present disclosure, a receiver technology for reducing this complexity is described.

[0104] random embedding

[0105] Figure 6 illustrates an example of subspace decomposition using random embedding. The operations of Figure 6 may be performed by a receiver (550). For example, the receiver (550) may receive an uplink signal. At least some of the above operations may be performed by a base station (110), a DU (210), or an RU (220).

[0106] Referring to FIG. 6, in operation (601), the receiver (550) can estimate a covariance matrix. The covariance matrix can represent channel characteristics experienced by signals received through the receiver (550). The covariance matrix can be determined based on the channel matrix. For example, the covariance matrix can be determined based on the following mathematical equation.

[0107]

[0108]

[0109]

[0110]

[0111] In operation (603), the receiver (550) can perform random embedding. The random embedding can be used to reduce reception complexity when performing subspace decomposition of the covariance matrix. Let us assume that the actual rank of the channel (530) between the transmitter (500) and the receiver (550) is K. The rank represents the number of linearly independent vectors in the matrix of the channel (530). In a massive MIMO system, the number of assumed antennas (e.g., 64) is large, but the number of ranks available in the channel (530) between the transmitter (500) and the receiver (550) is limited. For example, let us assume that the total number of antennas is 64. When 8 layers are used in a communication protocol (e.g., when 8 terminals with one transmit antenna are supported in the same resource area, a maximum rank of 8 is used, which can be up to 8 when supporting DMRS-config 1 of terminals before NR Release 18, and up to 24 can be supported from NR Release 18), the channel estimation results of other receive antennas (e.g., 56 layers) may be overhead. Therefore, the random embedding can be used to provide the same reception performance as before while reducing the computational complexity of channel estimation. The dimension of a column in the random embedding matrix can be S. The S can be greater than or equal to K. The size of the covariance matrix can be changed due to the random embedding matrix. The receiver (550) can apply the random embedding matrix to the covariance matrix. For example, the receiver (550) can obtain a transformed covariance matrix based on the following mathematical equation.

[0112]

[0113]

[0114] The receiver (550) may determine a random embedding matrix. The power of the random embedding matrix may be 1. By applying the random embedding matrix, the power magnitude of the covariance matrix may not change. In addition, the column vectors of the random embedding matrix may be linearly independent of each other. The random embedding matrix may be understood as rotating the second dimension among the first and second dimensions of the covariance matrix. The complexity may be reduced through the rotation of the second dimension without deforming the first dimension used for channel estimation of antennas among the covariance matrix. As a non-limiting example, the column vectors may be orthogonal to each other. For example, the random embedding matrix may be an iid (independent and isotropically distributed) Gaussian random matrix. For example, the random embedding matrix may be an isotropic random matrix (e.g., a Haar matrix). For example, the random embedding matrix may be a tensor matrix. For example, the random embedding matrix may be an isotropic random matrix with a specific structure (e.g., a matrix including an FFT with trigonometric functions as elements). For the random embedding matrix, reference [1] may be referred to.

[0115] In operation (605), the receiver (550) may perform subspace decomposition. The subspace decomposition may refer to an operation for decomposing a transformation covariance matrix obtained through the random embedding matrix into a plurality of subspaces that are orthogonal to each other. According to one embodiment, the receiver (550) may perform QR decomposition. QR decomposition refers to an operation for dividing a matrix into an orthogonal matrix (hereinafter, Q matrix) and an upper triangular matrix (hereinafter, R matrix). The QR decomposition refers to a technique for orthogonalizing basis vectors through the Q matrix and the R matrix. Hereinafter, in the present disclosure, QR decomposition is exemplified as a technique for finding orthogonal basis vectors; however, any decomposition technique for finding orthogonal basis vectors other than the QR decomposition may be understood as an embodiment of the present disclosure. For example, the Q matrix may be an orthogonal normal matrix. The row vectors of the Q matrix may be orthogonal to each other and may have a length of 1. For example, the QR decomposition may be expressed by the following mathematical equation.

[0116]

[0117]

[0118]

[0119] To more accurately select ranks, eigenvalue decomposition can be utilized. As eigenvalue decomposition progresses after the dimensionality is reduced from M to S, complexity can be reduced. In one embodiment, the receiver (550) can perform eigenvalue decomposition. For example, eigenvalue decomposition can be performed based on the following mathematical equation.

[0120]

[0121]

[0122]

[0123]

[0124]

[0125]

[0126]

[0127] As another example, eigenvalue decomposition can be performed based on the following mathematical formula, unlike the method used in Equations 10, 11, and 12.

[0128]

[0129]

[0130]

[0131]

[0132]

[0133]

[0134]

[0135] Figure 7 illustrates an example of subspace decomposition using random embedding. The operations of Figure 7 may be performed by a receiver (550). For example, the receiver (550) may receive an uplink signal. At least some of the above operations may be performed by a base station (110), a DU (210), or an RU (220).

[0136] Referring to FIG. 7, in operation (701), the receiver (550) may perform random embedding. The random embedding may be used to reduce reception complexity when performing subspace decomposition of the covariance matrix. Assume that the actual rank of the channel (530) between the transmitter (500) and the receiver (550) is K. The rank represents the number of linearly independent vectors in the matrix of the channel (530). In a massive MIMO system, the number of assumed antennas (e.g., 64) is large, but the number of ranks available in the channel (530) between the transmitter (500) and the receiver (550) is limited. For example, assume that the total number of antennas is 64. Since the communication protocol defines a maximum of 8 ranks, the channel estimation results of other receiving antennas (e.g., 56 layers) may be overhead. Therefore, the random embedding can be used to provide the same reception performance as before while reducing the computational complexity of channel estimation. The dimension of a column in the random embedding matrix can be S. S can be greater than or equal to K. For example, the receiver (550) can perform random embedding based on the following mathematical equation.

[0137]

[0138]

[0139] The power of the random embedding matrix may be 1. By applying the random embedding matrix, the power magnitude of the covariance matrix may not change. In addition, the column vectors of the random embedding matrix may be linearly independent of each other. The random embedding matrix may be understood as rotating the second dimension among the first and second dimensions of the covariance matrix. The complexity may be reduced through the rotation of the second dimension without deforming the first dimension used for channel estimation of antennas among the covariance matrix. As a non-limiting example, the column vectors may be orthogonal to each other. For example, the random embedding matrix may be an iid (independent and isotropically distributed) Gaussian random matrix. For example, the random embedding matrix may be an isotropic random matrix (e.g., a Haar matrix). For example, the random embedding matrix may be a tensor matrix. For example, the random embedding matrix may be an isotropic random matrix having a specific structure (e.g., a matrix including an FFT having trigonometric functions as elements). For the random embedding matrix, reference [1] may be referred to.

[0140] In operation (703), the receiver (550) can estimate a randomly embedded covariance matrix. For example, the receiver (550) can estimate the randomly embedded covariance matrix based on the following mathematical equation.

[0141]

[0142]

[0143] In operation (705), the receiver (550) may perform subspace decomposition. The subspace decomposition may represent an operation for decomposing the randomly embedded covariance matrix into a plurality of mutually orthogonal subspaces. In one embodiment, the receiver (550) may perform QR decomposition. For example, the QR decomposition may be expressed by the following mathematical equation.

[0144]

[0145]

[0146]

[0147] To more accurately select ranks, eigenvalue decomposition can be utilized. As eigenvalue decomposition progresses after the dimensionality is reduced from M to S, complexity can be reduced. In one embodiment, the receiver (550) can perform eigenvalue decomposition. For example, eigenvalue decomposition can be performed based on the following mathematical equation.

[0148]

[0149]

[0150]

[0151]

[0152]

[0153]

[0154]

[0155] In Fig. 6, a receiving operation that performs random embedding on a covariance matrix is ​​proposed, whereas in Fig. 7, a receiving operation that performs random embedding directly on a channel matrix and then obtains a covariance matrix is ​​proposed. Since subspace decomposition is performed per covariance matrix, the more operations on the covariance matrix are performed, the more it is obtained. Therefore, the more accumulations there are for the covariance matrix, the more advantageous the receiving operation illustrated in Fig. 7 can be in terms of reducing complexity.

[0156] In the present disclosure, subspace decomposition techniques such as QR decomposition and / or eigenvalue decomposition can be performed together with random embedding, thereby reducing complexity and improving stability of subspace decomposition. In particular, in addition to embedding the covariance matrix, the computational complexity of the covariance matrix can be reduced by embedding it in instantaneous vectors. The subspace obtained according to the above-described method can have a reduced dimension based on the rank according to the number of support layers and the number of strong interferences. The subspace with the reduced dimension can be utilized for projection into the space of the signal or the space of noise and interference / downlink MIMO precoding / uplink MIMO port reduction combiner / covariance matrix rank approximation, etc., and can reduce the computational complexity in each operation. The complexity reduction technique utilizing random embedding can support various dimensions compared to the existing maximal ratio combining (MRC) and maximal ratio transmission (MRT) that use instantaneous channels. Furthermore, the above technique can fully utilize the advantages of subspace-based transceivers, which maximize the average signal-to-noise ratio (SNR) or signal-to-interference noise ratio (SINR) within the supported dimensions. For example, when an isotropic matrix with a specific structure is used for random embedding, the complexity reduction can be represented as shown in the table below.

[0157]

[0158]

[0159] Figure 8 shows an example of precoding or pre-combining using subspace decomposition.

[0160] Referring to FIG. 8, the RU (220) can perform channel estimation (810). The RU (220) can receive a reference signal (801). For example, the reference signal (801) can be a sounding reference signal (SRS). For example, the reference signal (801) can be a DMRS (e.g., front-loaded DMRS). The RU (220) can perform channel estimation (810) through the reference signal (801). For example, the RU (220) can obtain a channel matrix H. For example, the size of the channel matrix H can be MxN (e.g., M is the number of reception layers, and N is the number of transmission layers).

[0161]

[0162] The RU (220) can perform RS port reduction (830). The RU (220) can receive a reference signal (803). For example, the reference signal (803) can be a sounding reference signal (SRS). For example, the reference signal (803) can be a DMRS (e.g., front-loaded DMRS). The RU (220) can perform pre-combining for port reduction on the reference signal (803). The RU (220) can multiply the vector of the reference signal (803) by a pre-combining matrix for the pre-combining.

[0163]

[0164]

[0165] DU (210) can perform channel estimation (850). DU (210) can perform channel estimation (850) using the reference signal on which the pre-combining has been performed. Assuming the number of ports to be L, DU (210) can estimate a channel matrix having a size of KxL.

[0166] The RU (220) can perform data port reduction (840). The RU (220) can receive a data signal (805). For example, the data signal (805) can include a PUSCH. The RU (220) can perform pre-combining for port reduction on the data signal (805). The RU (220) can multiply the vector of the data signal (805) by a pre-combining matrix for the pre-combining.

[0167]

[0168]

[0169] DU (210) can perform equalization (860). DU (210) can perform equalization (860) of the data signal on which the pre-combining has been performed based on the result of channel estimation (850). DU (210) can obtain signals of independent paths of the channel through equalization (860).

[0170] DU (210) can also maximize channel power based on the estimated channel. Although not illustrated in FIG. 8, if interference estimation is possible, a receiver that maximizes the signal-to-interference plus noise ratio (SINR) can be configured. For example, if maximal ratio combining (MRC) is performed, the number of ports can be reduced to L. Therefore, the extra ports can be utilized for interference estimation. Port reduction techniques using subspace decomposition (820) can facilitate selecting a number of ports greater than the number of supported layers (e.g., ranks).

[0171]

[0172] eigen decomposition

[0173] Figure 9 shows an example of a receiving operation using eigen decomposition of the covariance matrix for noise and interference.

[0174] Referring to FIG. 9, the RU (220) can perform noise and interference estimation (910). The RU (220) can receive a reference signal (901). For example, the reference signal (901) can be an SRS. For example, the reference signal (901) can be a DMRS. The RU (220) can perform noise and interference estimation (910) through the reference signal (901). For example, the RU (220) can receive a covariance matrix R for noise and interference. nn can be obtained. Covariance matrix R for noise and interference nn The size of can be MxM (e.g., M is the number of transmission layers and M is the number of reception layers). Covariance matrix R for noise and interference nn The sample vectors of can be used for channel and interference estimation. In embodiments of the present disclosure, the covariance matrix R for noise and interference nn Instead of using the sample vectors as they are, eigenvalue decomposition can be performed. Through the eigenvalue decomposition, the number of samples transmitted from the RU (220) to the DU (210) can be reduced. According to one embodiment, the covariance matrix R for noise and interference nn The sample vectors of can be replaced with noise vectors using eigenvectors and eigenvalues. As the number of samples transmitted to the DU (210) decreases, the number of ports considered for channel estimation and equalization in the DU (210) in a massive MIMO system can be reduced.

[0175] RU (220) can perform eigenvalue decomposition (930). For example, RU (220) can generate a covariance matrix (e.g., a covariance matrix R for noise and interference). nn ) can perform eigenvalue decomposition (930) (e.g., operation (605)) on the random embedding result. For example, RU (220) can perform eigenvalue decomposition (930) (e.g., operation (705)) on the randomly embedded covariance matrix. For example, covariance matrix R for noise and interference nn Eigenvalue decomposition (930) for can be performed according to mathematical expressions 10 to 15.

[0176]

[0177]

[0178]

[0179] RU (220) can perform channel estimation (920). RU (220) can receive a reference signal (901). RU (220) can perform channel estimation (920) through the reference signal (901). For example, RU (220) can obtain a channel matrix H through the channel estimation (920). For example, the size of the channel matrix H can be MxL (e.g., M is the number of receiving layers, and L is the number of transmitting layers).

[0180] RU (220) can perform channel projection (940). RU (220) can perform channel projection (940) based on the results of channel estimation (920) and eigenvalue decomposition (930). For example, RU (220) can perform channel projection (940) based on the following mathematical equation.

[0181]

[0182]

[0183] The RU (220) can convert the data signal (903) through the match filter (950). The converted result may be referred to as a converted signal. The converted result may be provided to the DU (210). For example, the RU (220) can convert the data signal (903) based on the following mathematical equation.

[0184]

[0185]

[0186] DU (210) can perform channel estimation (960). For example, DU (210) can perform channel estimation (960) based on the following mathematical equation.

[0187]

[0188]

[0189] DU (210) can perform channel inversion (970). For example, DU (210) can perform an operation according to the following mathematical formula.

[0190]

[0191]

[0192]

[0193]

[0194]

[0195] In Fig. 9, an example of compressing samples of a covariance matrix for noise and interference according to eigenvalue decomposition is described. Through eigenvalue decomposition, received signals can be separated into a signal space and a noise and interference space. By separately projecting the column vectors obtained through eigenvalue decomposition onto the channel, the computational complexity in the DU (210) can be reduced. For example, compared to the method of extracting samples from the column vectors of the covariance matrix for noise and interference in the reference literature [2], the number of ports of data provided from the RU (220) to the DU (210) can be reduced. In performing the eigenvalue decomposition, the computational complexity can be further reduced through the eigenvalue decomposition method using random embedding described in Figs. 6 and 7 (e.g., the method described in Equations 10 to 15). Meanwhile, if signal processing due to noise and interference is performed in the RU (220), a more simplified operation can be performed in the DU (210). For the simplified operation, the null space of noise and interference can be utilized. The channel is projected onto the null space of noise and interference, and the projected channel can be used to reduce the number of receiving ports in the DU or for equalization. Hereinafter, channel projection using the null space is described with reference to FIGS. 10 to 12.

[0196] Channel projection using null space

[0197] Figure 10 shows an example of a receiving operation using the null space of the covariance matrix for noise and interference.

[0198] Referring to FIG. 10, the RU (220) can perform noise and interference estimation (910). The RU (220) can receive a reference signal (901). For example, the reference signal (901) can be an SRS. For example, the reference signal (901) can be a DMRS. The RU (220) can perform noise and interference estimation (910) through the reference signal (901). For example, the RU (220) can receive a covariance matrix R for noise and interference. nn can be obtained. Covariance matrix R for noise and interference nn The size of can be MxM (e.g., M is the number of transmitting layers and M is the number of receiving layers).

[0199] RU (220) can perform channel estimation (920). RU (220) can receive a reference signal (901). RU (220) can perform channel estimation (920) through the reference signal (901). For example, RU (220) can obtain a channel matrix H through the channel estimation (920). For example, the size of the channel matrix H can be MxL (e.g., M is the number of receiving layers, and L is the number of transmitting layers).

[0200] RU (220) can obtain a null space (1030). The null space represents the null space of noise and interference. The null space of noise and interference can be obtained through subspace decomposition of the covariance matrix (or transformed covariance matrix) for noise and interference. For example, the noise and interference vectors can be defined based on the mathematical equations below.

[0201]

[0202]

[0203] The randomly embedded noise and interference vectors described in FIGS. 6 and 7 can be expressed as follows.

[0204]

[0205]

[0206] The covariance matrix for randomly embedded noise and interference can be expressed as follows.

[0207]

[0208]

[0209] To obtain the null space of a randomly embedded covariance matrix, QR decomposition can be performed. For example, the null space can be obtained based on the following mathematical formula.

[0210]

[0211]

[0212]

[0213]

[0214]

[0215] The RU (220) can convert the data signal (903) through the match filter (1050). The converted result may be referred to as a converted signal. The converted result may be provided to the DU (210). For example, the RU (220) can convert the data signal (903) based on the following mathematical equation.

[0216]

[0217]

[0218] DU (210) can perform channel estimation (1060). DU (210) can perform channel estimation (1060). For example, DU (210) can perform channel estimation (1060) based on the following mathematical equation.

[0219]

[0220]

[0221] DU (210) can perform channel inversion (1070). For example, DU (210) can perform an operation according to the following mathematical formula.

[0222]

[0223]

[0224]

[0225]

[0226]

[0227] Figure 11 illustrates an example of a receiving operation utilizing the null space of a covariance matrix for noise and interference. Figure 11 illustrates an example of processing all received signals in a receiver (550) without separating the DU (210) and the RU (220). Identical reference numerals may indicate identical descriptions.

[0228] Referring to FIG. 11, the receiver (550) can perform noise and interference estimation (910). The receiver (550) can perform channel estimation (920). The receiver (550) can obtain a null space (1030). The receiver (550) can perform channel projection (1040). The receiver (550) can transform the data signal (903) through a match filter (1050). Unlike FIG. 10, the receiver (550) does not require an operation of transmitting to another node or channel estimation in the other node.

[0229] The receiver (550) can perform channel inversion (1170). For example, the receiver (550) can perform an operation according to the following mathematical formula.

[0230]

[0231]

[0232]

[0233]

[0234]

[0235]

[0236]

[0237] Fig. 12 illustrates an example of a receiving operation using weighted interpolation. In Figs. 10 and 11, examples of applying a matched filter (e.g., matched filter 950, matched filter 1050) to a received data signal (903) are described. However, projecting a null space onto an estimated channel for each of all received symbols may increase the computational burden. In Fig. 12, operations of applying a weight (or filter) to a received signal without performing a null space operation on the received signal through weighted interpolation are described. At least some of the operations of the receiver (550) may be performed by the DU (210) or the RU (220). The same reference numbers may indicate the same description.

[0238]

[0239]

[0240] The receiver (550) can perform weight calculation (1230). The receiver (550) can calculate the weight based on the inversion matrix and projection channel matrix. For example, the receiver (550) can calculate the weight based on the following mathematical equation.

[0241]

[0242]

[0243]

[0244] The receiver (550) can perform equalization (1250).

[0245]

[0246]

[0247] In Fig. 12, an example of using channel or weight interpolation is described when the filter projecting the channel into the null space of noise and interference is known only for some symbols or some subcarriers, or when only the reception weight filter for some symbol subcarriers is known. For example, the channel may change relatively slowly. The reception signal may vary for each symbol (time-domain unit) and each subcarrier (frequency-domain unit). Therefore, if filtering is performed on all reception signals, the complexity may increase because multiple filters are performed even if the channel variation is minimal. Therefore, the computational complexity can be improved through weight interpolation (1240). In addition, the receiver (550) can interpolate only the channel filter projected into the null space of noise and interference without separately interpolating the channel for obtaining the weight. The characteristics of the projection channel matrix can satisfy the following mathematical equation.

[0248]

[0249]

[0250]

[0251] Figures 13a and 13b show examples of the performance of a receiving operation using random embedding and null space.

[0252] Referring to FIGS. 13A and 13B, a graph (1300) represents a CDF (Cumulative distribution function) according to pSINR. The horizontal axis of the graph (1300) represents pSINR (unit: dB (decibel)), and the vertical axis of the graph (1300) represents CDF. A graph (1350) represents a CDF according to pSINR. The horizontal axis of the graph (1350) represents pSINR (unit: dB), and the vertical axis of the graph (1350) represents CDF. The graph (1350) may correspond to a region (1310) of the graph (1300). A first line (1301) represents the reception performance of an MMSE-IRC receiver for the entire dimension based on an ideal noise and interference matrix. A second line (1302) represents the reception performance of an MMSE-IRC receiver for the entire dimension based on an estimated noise and interference matrix. The third line (1303) represents the reception performance according to the method of reference [2] when the number of noise and interference samples is 8 (e.g., in reference [2], the receiver (RU and / or DU) extracts samples from the column vectors of the covariance matrix of noise and interference to obtain noise and interference samples, changes the channel through the noise and interference samples, and performs an MMSE reception operation according to the changed channel). The fourth line (1304) represents the reception performance according to the method of reference [2] when the number of noise and interference samples is 5. The fifth line (1305) represents the reception performance according to the method of reference [2] when the number of noise and interference samples is 2. The sixth line (1306) represents the reception performance according to the method illustrated in FIG. 9 when the number of noise vectors is 2. The seventh line (1307) represents the reception performance according to the method illustrated in FIG. 10 when the number of noise vectors is 2.

[0253] The technique of the reference [2] (e.g., the method of extracting samples from the column vectors of the covariance matrix of noise and interference) requires a sufficiently large number of samples relative to the number of interference streams. For example, the technique (e.g., the third line (1303)) that uses 8 samples for two interference streams shows about 1 dB performance degradation compared to the proposed technique (e.g., the sixth line (1306), the seventh line (1307)). According to the proposed technique, only two paths can be selected for two strong interference streams. Therefore, since only the shape of the null space or two large eigenvectors is considered, the computational complexity in the DU (210) can be reduced. By improving the estimation accuracy of the noise and interference covariance through eigenvalue decomposition of the covariance matrix for noise and interference, the proposed technique can show improved performance compared to the existing MMSE IRC (e.g., the second line (1302)). Additionally, the receiving technique utilizing random embedding and null space (e.g., line 7 (1307)) can further reduce complexity because it does not select the paths of interference streams. It can be confirmed that the receiving technique exhibits performance equivalent to that of the eigenvalue decomposition-based MMSE technique (e.g., line 6 (1301)).

[0254] Random embedding can be performed on the covariance matrix itself, or on the channel or noise vectors for obtaining the covariance matrix. A filter that separates the space of interference and noise from the space of the signal using a spatial decomposition technique can be used, or a filter that projects the estimated channel onto a null space orthogonal to the space of interference and noise can be used. Through the above filter, the complexity of the equalizer can be reduced by reducing the number of ports of the DU (210) by the number of transmission layers while reducing the influence of interference.

[0255] 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.

[0256] In embodiments, a device of a radio unit (RU) is provided. The device may include a radio frequency (RF) transceiver, a fronthaul transceiver, a memory storing instructions, and a processor. The instructions, when executed by the processor, may cause the device to obtain reference signals through the RF transceiver, obtain a transform covariance matrix for noise and interference of the reference signals through random embedding for dimensionality reduction, obtain an extraction matrix through subspace decomposition of the transform covariance matrix, perform pre-combining using the extraction matrix on uplink signals obtained through the RF transceiver, and transmit data of the uplink signals on which the pre-combining has been performed to a digital unit (DU) through the fronthaul transceiver. The above random embedding can be used to transform a first dimension having the number of receiving layers of the RU into a second dimension having a number smaller than the number of receiving layers.

[0257] In one embodiment, the instructions, when executed by the processor, may cause the device to obtain a noise and interference covariance matrix for a channel of the reference signals, and to obtain the transform covariance matrix by multiplying the noise and interference covariance matrix by a random embedding matrix for the random embedding. Each column vector of the random embedding matrix may be linearly independent.

[0258] In one embodiment, the instructions, when executed by the processor, may cause the device to obtain a channel matrix for the reference signals, obtain a randomly embedded channel matrix by multiplying the channel matrix by a random embedding matrix for the random embedding, and obtain a noise and interference covariance matrix for the randomly embedded channel matrix as the transform covariance matrix, to obtain the transform covariance matrix. Each column vector of the random embedding matrix may be linearly independent.

[0259] In one embodiment, the instructions, when executed by the processor, may cause the device to perform a QR decomposition on the transformed covariance matrix and to extract at least one column vector, a specified number K, from among the column vectors of an orthogonal matrix resulting from the QR decomposition, to obtain the extraction matrix. The extraction matrix may correspond to the at least one extracted column vector.

[0260] In one embodiment, the instructions, when executed by the processor, may cause the device to perform an eigen decomposition on the transformed covariance matrix and to extract at least one column vector, a specified number K, from among the column vectors of eigen vector matrices resulting from the eigen decomposition, to obtain the extraction matrix. The extraction matrix may correspond to the at least one extracted column vector.

[0261] In one embodiment, the random embedding may include a multiplication of a random embedding matrix. The random embedding matrix may be an iid (independent and isotropic distributed) Gaussian random matrix, an isotropic random matrix, or a tensor matrix.

[0262] According to one embodiment, the uplink signals may include at least one of physical uplink shared channel (PUSCH) signals, sounding reference signals (SRS), or uplink demodulation reference signals (DMRS).

[0263] In one embodiment, the instructions, when executed by the processor, may cause the device to determine a precoding matrix for a downlink signal based on the extraction matrix, apply the precoding matrix to the downlink signal, thereby generating a transmission signal, and transmit the transmission signal via the RF transceiver.

[0264] In one embodiment, the instructions, when executed by the processor, may cause the device to obtain a projection matrix for a null-space based on the extraction matrix, obtain a filter matrix by applying the projection matrix to a channel matrix for the reference signals, and perform the pre-combining by multiplying the uplink signals by the filter matrix.

[0265] In embodiments, a device of a digital unit (DU) is provided. The device may include a transceiver, a memory storing instructions, and a processor. The instructions, when executed by the processor, may cause the device to obtain reference signals from a radio unit (RU) via the transceiver, obtain a transform covariance matrix for noise and interference of the reference signals through random embedding for dimensionality reduction, obtain an extraction matrix through subspace decomposition of the transform covariance matrix, and perform equalization on uplink signals from the RU using the extraction matrix, thereby obtaining transmission signals. The random embedding may be used to transform a first dimension having a number of reception layers of the RU into a second dimension having a number smaller than the number of reception layers.

[0266] In embodiments, a device of a radio unit (RU) is provided. The device may include a radio frequency (RF) transceiver, a fronthaul transceiver, a memory storing instructions, and a processor. The instructions, when executed by the processor, may cause the device to obtain a covariance matrix for noise and interference through reference signals received through the RF transceiver, obtain a noise vector matrix by performing eigen decomposition of the covariance matrix, obtain a channel matrix for the reference signals and a filter matrix using the noise vector matrix, obtain transformed signals by applying the filter matrix to uplink signals obtained through the RF transceiver, and transmit data of the transformed signals to a digital unit (DU) through the fronthaul transceiver. The number of columns of the noise vector matrix may be smaller than the number of reception layers of the RU.

[0267] In one embodiment, the instructions, when executed by the processor, may cause the device to obtain an eigenvector matrix and eigenvalues ​​by performing the eigenvalue decomposition of the covariance matrix, and to obtain the noise vector matrix through extracted column vectors corresponding to a specified number of column vectors of the eigenvector matrix and eigenvalues ​​corresponding to the extracted column vectors.

[0268] According to one embodiment, the noise vector matrix may be determined based on the following mathematical formula.

[0269]

[0270]

[0271] In one embodiment, the number of rows of the filter matrix may correspond to the number of receiving layers of the RU. The number of columns of the filter matrix may correspond to the sum of the number of transmitting layers and the number of columns of the noise vector matrix.

[0272] In one embodiment, the reference signals may include uplink demodulation reference signals (DMRS). The uplink signals may include physical uplink shared channel (PUSCH) signals.

[0273] In embodiments, a device of a radio unit (RU) may include a radio frequency (RF) transceiver, a fronthaul transceiver, a memory storing instructions, and a processor. The instructions, when executed by the processor, may cause the device to obtain reference signals through the RF transceiver, obtain a transformed covariance matrix for noise and interference through the reference signals, obtain an extraction matrix having column vectors corresponding to a specified number of column vectors among the orthogonal matrix of the QR decomposition through QR decomposition of the covariance matrix, obtain a filter matrix using a projection matrix obtained from the extraction matrix and a channel matrix for the reference signals, obtain transformed signals by applying the filter matrix to uplink signals obtained through the RF transceiver, and transmit data of the transformed signals to a digital unit (DU) through the fronthaul transceiver.

[0274] According to one embodiment, the projection matrix may be determined based on the following mathematical formula.

[0275]

[0276]

[0277] According to one embodiment, the filter matrix may be determined based on the following mathematical formula.

[0278]

[0279]

[0280] In one embodiment, the number of rows of the filter matrix may correspond to the number of receiving layers of the RU. The number of columns of the filter matrix may correspond to the number of transmitting layers.

[0281] In one embodiment, the reference signals may include uplink demodulation reference signals (DMRS). The uplink signals may include physical uplink shared channel (PUSCH) signals.

[0282] For one or more embodiments, at least one of the components described in one or more of the preceding drawings may be configured to perform one or more operations, techniques, processes, and / or methods as described herein. For example, a processor (e.g., a baseband processor) described herein with respect to one or more of the preceding drawings may be configured to operate according to one or more examples described herein. For another example, circuitry associated with a user equipment (UE), a base station, a network element, and the like, as described above with respect to one or more of the preceding drawings, may be configured to operate according to one or more examples described herein.

[0283] Any of the embodiments described above may be combined with any other embodiment (or combination of embodiments) unless explicitly stated otherwise. The foregoing description of one or more implementations provides examples and descriptions, but is not intended to be exhaustive or limit the scope of the embodiments to the precise forms disclosed. Modifications and variations are possible in light of the above teachings or may be learned from practicing various embodiments.

[0284] 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.

[0285] 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.

[0286] 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.

[0287] 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.

[0288] 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.

[0289] 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.

[0290] 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 RU (radio unit) device, RF(radio frequency) transceiver; Fronthaul transceiver; Memory for storing instructions; and Contains a processor, The above instructions, when executed by the processor, cause the device to: Obtain reference signals through the above RF transceiver, Through random embedding for dimensionality reduction, a transformation covariance matrix for noise and interference of the reference signals is obtained, By subspace decomposition of the above transformation covariance matrix, the extraction matrix is ​​obtained, Pre-combining is performed on uplink signals obtained through the RF transceiver using the extraction matrix, Causing data of the uplink signals on which the above pre-combining has been performed to be transmitted to the DU (digital unit) through the fronthaul transceiver, The above random embedding is used to transform a first dimension having the number of receiving layers of the RU into a second dimension having a number smaller than the number of receiving layers. device.

2. In claim 1, the instructions, when executed by the processor, cause the device to: Obtain the noise and interference covariance matrix for the channel of the above reference signals, By multiplying the random embedding matrix for the random embedding to the above noise and interference covariance matrix, the transformation covariance matrix is ​​obtained, Each column vector of the above random embedding matrix is ​​linearly independent, device.

3. In claim 1, the instructions, when executed by the processor, cause the device to: Obtain a channel matrix for the above reference signals, By multiplying the random embedding matrix for the random embedding to the above channel matrix, a randomly embedded channel matrix is ​​obtained, Causing the noise and interference covariance matrix for the randomly embedded channel matrix to be obtained as the transform covariance matrix, Each column vector of the above random embedding matrix is ​​linearly independent, device.

4. In claim 1, the instructions, when executed by the processor, cause the device to: Perform QR decomposition on the above transformation covariance matrix, Causes to extract at least one column vector, as many as the specified number K, from among the column vectors of the orthogonal matrix according to the above QR decomposition, The above extraction matrix corresponds to at least one extracted column vector, device.

5. In claim 1, the instructions, when executed by the processor, cause the device to: Perform eigen decomposition on the above transformed covariance matrix, Causes to extract at least one column vector, a specified number K, from among the column vectors of the eigenvector matrices according to the above eigenvalue decomposition, The above extraction matrix corresponds to at least one extracted column vector, device.

6. In claim 1, the random embedding includes multiplication of random embedding matrices, The above random embedding matrix is ​​an iid (independent and isotropic distributed) Gaussian random matrix, an isotropic random matrix, or a tensor matrix. device.

7. In claim 1, The above uplink signals include at least one of PUSCH (physical uplink shared channel) signals, SRS (sounding reference signals), or DMRS (demodulation reference signals). device.

8. In claim 1, the instructions, when executed by the processor, cause the device to: Based on the above extraction matrix, a precoding matrix for a downlink signal is determined, By applying the precoding matrix to the above downlink signal, a transmission signal is generated, causing said transmission signal to be transmitted through said RF transceiver, device.

9. In claim 1, the instructions, when executed by the processor, cause the device to: Based on the above extraction matrix, a projection matrix for the null space is obtained, By applying the projection matrix to the channel matrix for the above reference signals, a filter matrix is ​​obtained, By multiplying the above uplink signals by the above filter matrix, the pre-combining is performed, device. In the device of 10.RU(radio unit), RF(radio frequency) transceiver; Fronthaul transceiver; Memory for storing instructions; and Contains a processor, The above instructions, when executed by the processor, cause the device to: Through the reference signals received through the above RF transceiver, a covariance matrix for noise and interference is obtained, By performing eigen decomposition of the above covariance matrix, a noise vector matrix is ​​obtained, Obtain a filter matrix using the channel matrix and the noise vector matrix for the above reference signals, By applying the filter matrix to the uplink signals obtained through the RF transceiver, converted signals are obtained, Causing the data of the above conversion signals to be transmitted to the DU (digital unit) through the front hall transceiver, The number of columns of the above noise vector matrix is ​​smaller than the number of receiving layers of the RU. device.

11. In claim 10, the instructions, when executed by the processor, cause the device to: By performing the eigenvalue decomposition of the above covariance matrix, the eigenvector matrix and eigenvalues ​​are obtained, Causing to obtain the noise vector matrix through extracting column vectors corresponding to a specified number of column vectors of the above eigenvector matrix and eigenvalues ​​corresponding to the extracted column vectors, device.

12. In claim 10, The number of rows of the above filter matrix corresponds to the number of receiving layers of the above RU, The number of columns of the above filter matrix corresponds to the sum of the number of transmission layers and the number of columns of the above noise vector matrix, The above reference signals include uplink DMRS (demodulation reference signals), The above uplink signals include PUSCH (physical uplink shared channel) signals. device. In the device of 13.RU(radio unit), RF(radio frequency) transceiver; Fronthaul transceiver; Memory for storing instructions; and Contains a processor, The above instructions, when executed by the processor, cause the device to: Obtain reference signals through the above RF transceiver, Through the above reference signals, a transformation covariance matrix for noise and interference is obtained, Through QR decomposition of the above covariance matrix, an extraction matrix having column vectors corresponding to a specified number of column vectors among the orthogonal matrix of the QR decomposition is obtained, Obtain a filter matrix using a projection matrix obtained from the above extraction matrix and a channel matrix for the above reference signals, By applying the filter matrix to the uplink signals obtained through the RF transceiver, converted signals are obtained, Causing the data of the above conversion signals to be transmitted to the DU (digital unit) through the front hall transceiver, device.

14. In claim 13, the projection matrix is ​​determined based on the following mathematical formula:

15. In claim 13, The number of rows of the above filter matrix corresponds to the number of receiving layers of the above RU, The number of columns of the above filter matrix corresponds to the number of transmission layers, The above reference signals include uplink DMRS (demodulation reference signals), The above uplink signals include PUSCH (physical uplink shared channel) signals. device.

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