Apparatus, method, and storage medium for adjusting split architecture in network

Abstract

This communication apparatus configured to perform a function of an upper network node may comprise: a memory for storing instructions; and at least one processor. The instructions, when executed individually or collectively by the at least one processor, may cause the communication apparatus to: obtain first resource information used in each of first functions of the function; provide the first resource information to a trained model stored in the upper network node, so as to generate second resource information to be used in each of the first functions; identify quality of service provided through the first functions configured on the basis of the second resource information; identify second functions changed from the first functions as the function of the upper network node among functions of a lower network node and the upper network node on the basis of the quality of service lower than a reference quality; and transmit, to the lower network node, configuration information indicating a change in the function of the lower network node.

Description

Device, method, and storage medium for coordinating partitioned architecture in a network

[0001] The following descriptions relate to a device, method, and storage medium for coordinating a partitioned architecture in a network.

[0002] In wireless communication systems, function splitting is applied to functionally separate base stations. Depending on the function split, base stations can be separated into a central unit (CU) (or control unit), a distributed unit (DU), or a radio unit (RU).

[0003] The information described above may be provided as related art for the purpose of aiding understanding of the present disclosure. No claim or determination is made as to whether any of the foregoing may be applied as prior art related to the present disclosure.

[0004] A communication device configured to perform the functions of an upper network node may include a memory for storing instructions. The communication device may include at least one processor. When the instructions are executed individually or collectively by the at least one processor, the communication device may cause to acquire first resource information used in each of the first functions of the functions of the upper network node. When the instructions are executed individually or collectively by the at least one processor, the communication device may cause to generate second resource information to be used in each of the first functions by providing the first resource information to a trained model stored within the upper network node. When the instructions are executed individually or collectively by the at least one processor, the communication device may cause to identify the quality of service provided through the first functions configured based on the second resource information. When the above instructions are executed individually or collectively by the at least one processor, the communication device may cause the device to identify second functions that have been changed from the first functions to the functions of the upper network node among the functions of the upper network node and the lower network node connected to the upper network node, based on the service quality below the reference quality, and to transmit configuration information to the lower network node that instructs the change of the functions of the lower network node.

[0005] A method performed in a communication device configured to perform the function of an upper network node may include an operation of acquiring first resource information used in each of the first functions of the function of the upper network node. The method may include an operation of generating second resource information to be used in each of the first functions by providing the first resource information to a trained model stored in the upper network node. The method may include an operation of identifying the quality of service provided through the first functions configured based on the second resource information. The method may include an operation of identifying second functions modified from the first functions by the function of the upper network node among the functions of the lower network node and the lower network node connected to the upper network node based on the quality of service that is less than a reference quality, and an operation of transmitting to the lower network node configuration information instructing the modification of the function of the lower network node.

[0006] A non-transient computer-readable storage medium may store one or more programs including instructions that cause the communication device to acquire first resource information used in each of the first functions of said function of said function when executed individually or collectively by at least one processor of a communication device configured to perform the function of said upper network node. The non-transient computer-readable storage medium may store one or more programs including instructions that cause the communication device to generate second resource information to be used in each of said first functions by providing said first resource information to a trained model stored in said upper network node when executed individually or collectively by said at least one processor. The non-transient computer-readable storage medium may store one or more programs including instructions that cause the communication device to identify the quality of service provided through said first functions configured based on said second resource information when executed individually or collectively by said at least one processor. The above non-transient computer-readable storage medium may store one or more programs including instructions that, when executed individually or collectively by the at least one processor, cause the communication device to identify second functions that have been changed from the first functions to the functions of the upper network node among the functions of the upper network node and the lower network node connected to the upper network node based on the service quality below the reference quality, and to transmit configuration information to the lower network node that directs the change of the functions of the lower network node.

[0007] A base station may include a memory for storing instructions. The base station may include at least one processor. When the instructions are executed individually or collectively by the at least one processor, the base station may be caused to identify first functions and second functions distinguished from the first functions among the functions of the base station. When the instructions are executed individually or collectively by the at least one processor, the base station may be caused to acquire first resource information used in each of the first functions and second resource information used in each of the second functions. When the instructions are executed individually or collectively by the at least one processor, the base station may be caused to generate third resource information to be used in each of the first functions by providing the first resource information to a first trained model stored within the base station. When the above instructions are executed individually or collectively by the at least one processor, the base station may generate fourth resource information to be used in each of the second functions by providing the second resource information to a second trained model stored within the base station. When the above instructions are executed individually or collectively by the at least one processor, the base station may identify the quality of service provided through the first functions configured based on the third resource information and the second functions configured based on the fourth resource information.When the above instructions are executed individually or collectively by the at least one processor, the base station may cause the base station to perform a reconfiguration of the base station’s functions by changing the first functions to third functions among the base station’s functions and changing the second functions to fourth functions among the base station’s functions that are distinct from the third functions, based on the service quality being below a reference quality.

[0008] Figure 1 illustrates an example of a wireless communication system.

[0009] Figure 2 illustrates an example of the logical architecture of a network.

[0010] Figure 3 illustrates an example of an interface between an upper network node and a lower network node.

[0011] Figure 4 shows an example of the functional configuration of an electronic device.

[0012] Figure 5 illustrates an example of the connection status of SMO (service management and orchestration), CU (central unit), and DU.

[0013] Figure 6 illustrates examples of options for functional separation.

[0014] Figure 7 illustrates an example of how to define each of the layers using a containerized network function (CNF).

[0015] Figure 8 illustrates an example of a signal flow for a method of adjusting the functional split between the CU and the DU.

[0016] Figure 9 illustrates an example of a method for generating resource information using a trained model.

[0017] Figure 10a illustrates an example of federated learning.

[0018] Figure 10b illustrates an example of federated learning between SMOs, CUs, and DUs.

[0019] FIG. 11 illustrates an example of an operation flow for how an upper network node coordinates the functions of an upper network node and a lower network node.

[0020] Throughout the drawings, the same reference numerals will be understood to refer to the same parts, components, and structures.

[0021] The terms used in this disclosure are used merely to describe specific embodiments and are not intended to limit the scope of other embodiments. A singular expression may include a plural expression unless the context clearly indicates otherwise. Terms used herein, including technical or scientific terms, may have the same meaning as generally understood by those skilled in the art described in this disclosure. Terms used in this disclosure that are defined in a general dictionary may be interpreted as having the same or similar meaning as they have in the context of the relevant technology, and are not to be interpreted in an ideal or overly formal sense unless explicitly defined in this disclosure. In some cases, even terms defined in this disclosure are not to be interpreted to exclude the embodiments of this disclosure.

[0022] In the various embodiments of the present disclosure described below, a hardware-based approach is described as an example. However, since the various embodiments of the present disclosure include techniques using both hardware and software, the various embodiments of the present disclosure do not exclude a software-based approach.

[0023] Terms used in the following description to refer to signals (e.g., packet, message, signal, information, signaling), terms for operation states (e.g., step, operation, procedure), terms for data (e.g., packet, message, information, user stream, information, bit, symbol, codeword), terms for channels, terms for network entities (DU (distributed unit), RU (radio unit), CU (central unit), CU-CP (control plane), CU-UP (user plane), O-DU (O-RAN (open radio access network) DU), O-RU (O-RAN RU), O-CU (O-RAN CU), O-CU-UP (O-RAN CU-CP), O-CU-CP (O-RAN CU-CP)), terms for device components, etc. are examples provided for the convenience of explanation. Accordingly, the present disclosure is not limited to the terms described below, and other terms having equivalent technical meanings may be used. Additionally, terms such as '...part', '...device', '...object', '...body' used below may refer to at least one shape structure or a unit that processes a function.

[0024] Additionally, in this disclosure, expressions of "greater than" or "less than" may be used to determine whether a specific condition is satisfied or fulfilled; however, this is merely for the purpose of expressing an example and does not exclude descriptions of "greater than" or "less than." Conditions described as "greater than" may be replaced with "greater than," conditions described as "less than" may be replaced with "less than," and conditions described as "greater than and less than" may be replaced with "greater than and less than." Furthermore, "A" to "B" below refer to at least one of elements from A (including A) to B (including B). Below, "C" and / or "D" refers to including at least one of "C" or "D," i.e., {"C", "D", "C" and "D"}.

[0025] The present disclosure describes embodiments using terms used in some communication standards (e.g., 3GPP (3rd Generation Partnership Project)), but this is merely illustrative. The embodiments of the present disclosure may also be applied to other communication and broadcasting systems.

[0026] Figure 1 illustrates an example of a wireless communication system.

[0027] Referring to FIG. 1, FIG. 1 illustrates a base station (110) and a terminal (120) as part of nodes using a wireless channel in a wireless communication system. FIG. 1 illustrates only one base station, but the wireless communication system may include other base stations identical or similar to the base station (110).

[0028] A base station (110) is a network infrastructure that provides wireless access to a terminal (120). The base station (110) has coverage defined based on the distance over which it can transmit signals. In addition to being a base station, the base station (110) may be referred to as an 'access point (AP)', 'eNodeB (eNB)', '5G node (5th generation node)', 'next generation nodeB (gNB)', 'wireless point', 'transmission / reception point (TRP)', or other terms having an equivalent technical meaning.

[0029] A terminal (120) is a device used by a user and communicates with a 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). Additionally, although not shown in FIG. 1, the terminal (120) and another terminal can 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 user involvement. According to 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 a narrowband (NB)-Internet of Things (IoT) device.

[0030] The terminal (120) may be referred to 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 an equivalent technical meaning.

[0031] The base station (110) can perform beamforming with the terminal (120). 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). Additionally, 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) and a 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, beamforming may include transmit beamforming and receive beamforming. The base station (110) and the terminal (120) can impart directivity to the transmitted signal or the 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 a resource that has a QCL relationship with the resource that transmitted the serving beams.

[0032] If large-scale characteristics of the channel that transmitted the symbol on the first antenna port can be inferred from the channel that transmitted the symbol on the second antenna port, the first antenna port and the second antenna port can be evaluated as being in a QCL relationship. For example, the large-scale characteristics may include at least one of a delay spread, a Doppler spread, a Doppler shift, an average gain, an average delay, and a spatial receiver parameter.

[0033] In FIG. 1, it is described that both the base station (110) and the terminal (120) perform beamforming, but the embodiments of the present disclosure are not necessarily limited thereto. In some embodiments, the terminal may or may not perform beamforming. Also, the base station may or may not perform beamforming. That is, either the base station or the terminal may perform beamforming, or neither the base station nor the terminal may perform beamforming.

[0034] In the present disclosure, a beam refers to a spatial flow of a signal in a wireless channel, formed by one or more antennas (or antenna elements), and this formation process may be referred to as beamforming. Beamforming may include at least one of analog beamforming or digital beamforming (e.g., precoding). A reference signal 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), or a sounding reference signal (SRS). Additionally, an IE such as a CSI-RS resource or an SRS-resource may be used as a configuration for each reference signal, and such a configuration may include information associated with the beam. Information associated with a beam may refer to whether the configuration (e.g., CSI-RS resource) uses the same spatial domain filter as other configurations (e.g., other CSI-RS resources within the same CSI-RS resource set) or a different spatial domain filter, or which reference signal it is quasi-colocated with, and if so, what type (e.g., QCL type A, B, C, D).

[0035] Conventionally, in communication systems with a 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 a radio frequency (RF) processing unit (or RU (radio unit)). However, as high frequency bands are used in 4G (4th generation) and / or subsequent communication systems (e.g., 5G) and the cell coverage of base stations decreases, the number of base stations required to cover a specific area has increased. Consequently, the burden of installation costs for operators to install base stations has also increased. To minimize base station installation costs and geographical constraints, a structure has been proposed in which the central unit (CU) (or control unit), DU, and RU of the base station are separated and arranged. Examples of network nodes resulting from the functional separation of base stations may be referenced in FIGS. 2 and FIGS. 3 below.

[0036] In FIG. 1, the connection state between the base station (110) and the terminal (120) is illustrated, but the present disclosure is not limited thereto. For an example of nodes within a network, refer to FIG. 2 below.

[0037] Figure 2 illustrates an example of the logical architecture of a network.

[0038] Referring to FIG. 2, the system (200) may include a central unit (CU) (210), a DU (220), a RU (230), an eNB (240), a service management and orchestration (SMO) (250), a non-real-time radio access network (RAN) intelligent controller (260), and a near-real-time radio access network (RAN) intelligent controller (270).

[0039] The CU (210) in FIG. 2 may be referred to as O-CU (open-RAN CU). The DU (220) in FIG. 2 may be referred to as O-DU. The RU (230) in FIG. 2 may be referred to as O-RU. The eNB (240) in FIG. 2 may be referred to as O-eNB, gNB (gNodeB), or O-gNB.

[0040] For example, the CU (210) may be connected to the DU (220) via the F1 interface. Although not shown in FIG. 2, the CU (210) may include (or be implemented, configured) a CU-UP (CU-user plane) and a CU-CP (CU-control plane). For example, the CU-UP may be referred to as O-CU-UP, and the CU-CP may be referred to as O-CU-CP. For example, the CU-CP may be connected to the CU-UP via the E1 interface. For example, the CU (210) may be connected to the Near-RT RIC (270) via the E2 interface. For example, the CU (210) may be connected to the SMO (250) (or Non-RT RIC (260)) via the O1 interface.

[0041] For example, the DU (220) can be connected to the CU (210). For example, the DU (220) can be connected to the CU (210) via the CU-CP and F1-c interfaces of the CU (210), and via the CU-UP and F1-u interfaces of the CU (210). For example, the DU (220) can be connected to the Near-RT RIC (270) via the E2 interface.

[0042] For example, the RU (230) may be connected to the DU (220) via an open fronthall interface. For example, the open fronthall interface (or fronthall interface) may be used to provide messages (or information) to the control plane, user plane, synchronization plane, and management plane. However, embodiments of the present disclosure are not limited thereto. For example, the RU (230) may be connected to the SMO (250) via an open fronthall interface (e.g., management plane) or an O1 interface. For example, the RU (230) may be referred to as a massive MIMO unit (MMU).

[0043] For example, the eNB (240) can be connected to the SMO (250) via an O1 interface. In FIG. 2, the eNB (240) is illustrated, but the present disclosure is not limited thereto. For example, the eNB (240) may represent a gNB or a base station capable of providing 6G (e.g., base station (110) of FIG. 1).

[0044] For example, the Near-RT RIC (270) can be connected to the eNB (240) via the E2 interface. The Near-RT RIC (270) can be connected to the Non-RT RIC (260) in the SMO (250) via the A1 interface.

[0045] Although not illustrated in FIG. 2, the system (200) may further include a Cloud (or O-Cloud). For example, the Cloud may be connected to the SMO (250) via an O2 interface. Additionally, FIG. 2 illustrates a system (200) representing the architecture of an O-RAN, but the present disclosure is not limited thereto.

[0046] Figure 3 illustrates an example of an interface between an upper network node and a lower network node.

[0047] FIG. 3 illustrates an interface (315) between an upper network node (310) and a lower network node (320). For example, the upper network node (310) may be an example of a CU (210) of FIG. 2. For example, if the upper network node (310) is an example of a CU (210) of FIG. 2, the lower network node (320) may be an example of a DU (220) of FIG. 2. However, the present disclosure is not limited thereto. For example, if the upper network node (310) is a DU (220), the lower network node (320) may be a RU (230).

[0048] Referring to FIG. 3, the interface (315) between the upper network node (310) and the lower network node (320) may include a midhaul interface. For example, the midhaul may refer to the interface between the CU (210) and the DU (220), unlike the backhaul between the base station (110) (or CU (210)) and the core network (e.g., UPF (user plane function)) and the fronthaul between the RU (230) and the DU (220).

[0049] FIG. 3 illustrates an example of a layout structure in which an upper network node (310) is connected to one lower network node (320), but this is merely for convenience of explanation and the present disclosure is not limited thereto. In other words, the embodiments of the present disclosure may also be applied to a layout structure between one upper network node and a plurality of lower network nodes. For example, the embodiments of the present disclosure may be applied to a layout structure between one upper network node and two lower network nodes. Additionally, the embodiments of the present disclosure may also be applied to a fronthole structure between one upper network node and three lower network nodes.

[0050] Referring to FIG. 3, the base station (110) may include an upper network node (310) and a lower network node (320). An interface (315) between the upper network node (310) and the lower network node (320) may be operated through an F1 interface.

[0051] As communication technology develops, mobile data traffic increases, and consequently, the bandwidth requirements at the interface (315) between the upper network node (310) and the lower network node (320) have increased significantly. In a deployment such as a C-RAN (centralized / cloud radio access network), the upper network node (310) is implemented to perform functions for RRC (radio resource control) and PDCP (packet data convergence protocol), and the lower network node (320) can be implemented to perform functions for RLC (radio link control), MAC (media access control), and PHY (physical). Although not shown in FIG. 3, a network node different from the lower network node (320) (e.g., RU (230)) can be implemented to perform functions for the PHY layer in addition to RF (radio frequency) functions. Specific details regarding this may be referenced below in FIG. 6.

[0052] The upper network node (310) can be responsible for the upper layer functions of the wireless network. As an example without limitation, the upper network node (310) can perform the functions of the RRC layer and the PDCP layer.

[0053] The lower network node (320) may be responsible for the relatively lower layer functions of the wireless network. As an example without limitation, the lower network node (320) may perform the functions of the MAC layer and part of the PHY layer. Here, part of the PHY layer refers to functions of the PHY layer that are performed at a higher level, and may include, for example, channel encoding (or channel decoding), scrambling (or descramming), modulation (or demodulation), and layer mapping (or layer demapping).

[0054] In the above example, the functions (or layers) of the upper network node (310) and the functions (or layers) of the lower network node (320) are merely for convenience of explanation and the present disclosure is not limited thereto. A specific example of functional separation between the upper network node (310) and the lower network node (320) may be referenced below in FIG. 6.

[0055] A base station (110) according to the embodiments may be implemented in a distributed deployment according to a CU (210) configured to perform the functions of the upper layers of an access network (e.g., PDCP (packet data convergence protocol), RRC (radio resource control)) and a DU (220) configured to perform the functions of the lower layers. In this case, the DU (220) may include a DU (digital unit) and a RU (radio unit). Between a core network (e.g., 5GC (5G core) or NGC (next generation core)) and a radio network (RAN), the base station (110) may be implemented in a structure in which the CU, DU, and RU are arranged in that order.

[0056] For example, a centralized unit (CU) can be connected to one or more DUs and perform functions at a higher layer than the DUs. For instance, the CU can perform functions at the radio resource control (RRC) and packet data convergence protocol (PDCP) layers, while the DU and RU can perform functions at lower layers. The DU can perform radio link control (RLC), media access control (MAC), and some functions of the physical (PHY) layer (high PHY), while the RU can perform the remaining functions of the PHY layer (low PHY). Additionally, as an example, a digital unit (DU) can be included in a distributed unit (DU) depending on the distributed deployment implementation of the base station. The following description describes the operations of DU and RU unless otherwise defined, but various embodiments of the present disclosure may be applied to both base station deployments including CU and deployments where DU is directly connected to the core network (i.e., implemented by integrating CU and DU into a single entity base station (e.g., NG-RAN node)).

[0057] Figure 4 shows an example of the functional configuration of an electronic device.

[0058] The configuration of the electronic device (400) exemplified in FIG. 4 can be understood as a configuration of a base station (110), a terminal (120), a CU (210), a DU (220), an SMO (250) (or a Non-RT RIC (320)), an upper network node (310), a lower network node (320), or a server. Terms such as '... unit' and '... device' used below refer to a unit that processes at least one function or operation, which may be implemented as hardware or software, or a combination of hardware and software. For example, the electronic device (400) may be referred to as a communication device or communication equipment.

[0059] Referring to FIG. 4, the electronic device (400) may include a transceiver (410), a memory (420), and a processor (430). However, the present disclosure is not limited thereto. For example, the electronic device (400) may not include at least some of the components shown in FIG. 4, or may further include components not shown in FIG. 4. For example, the electronic device (400) may not include a transceiver (410).

[0060] The transceiver (410) can perform functions for transmitting and receiving signals in a wired communication environment. The transceiver (410) may include a wired interface for controlling a direct connection between devices through a transmission medium (e.g., copper wire, optical fiber). For example, the transceiver (410) can transmit an electrical signal to another device through a copper wire or perform conversion between an electrical signal and an optical signal.

[0061] The transceiver (410) may perform functions for transmitting and receiving signals in a wireless communication environment. For example, the transceiver (410) may perform a conversion function between a baseband signal and a bit sequence according to the physical layer specifications of the system. For example, when transmitting data, the transceiver (410) generates complex-valued symbols by encoding and modulating the transmitted bit sequence. Also, when receiving data, the transceiver (410) restores the received bit sequence by demodulating and decoding the baseband signal. Additionally, the transceiver (410) may include multiple transmission and reception paths.

[0062] The transceiver (410) transmits and receives signals as described above. Accordingly, all or part of the transceiver (410) may be referred to as a 'communication unit', 'transmitter unit', 'receiver unit', or 'transmitter / receiver unit'. Furthermore, in the following description, transmission and reception performed via a wireless channel are used to mean that processing as described above is performed by the transceiver (410).

[0063] Although not illustrated in FIG. 4, the transceiver (410) may further include a backhaul transceiver for connecting to a core network or another base station. The backhaul transceiver provides an interface for communicating with other nodes within the network. That is, the backhaul transceiver converts a sequence of bits transmitted from a base station to another node, e.g., another access node, another base station, an upper node, a core network, etc., into a physical signal, and converts a physical signal received from another node into a sequence of bits.

[0064] The memory (420) stores data such as basic programs, application programs, and setting information for the operation of the electronic device (400). The memory (420) may be referred to as a storage unit. The memory (420) may be composed of volatile memory, non-volatile memory, or a combination of volatile memory and non-volatile memory. Additionally, the memory (420) provides the stored data upon the request of the processor (430).

[0065] For example, the processor (430) may include various processing circuits and / or multiple processors. For example, the term “processor” as used herein, including in the claims, may include various processing circuits including at least one processor, and one or more of said at least one processor may be configured to perform the various functions described below in a distributed manner, individually and / or collectively. As used below, where “processor,” “at least one processor,” and “one or more processors” are described as being configured to perform various functions, these terms encompass, for example, but not limited to, situations where one processor performs some of the cited functions and other processor(s) perform other parts of the cited functions, and also situations where one processor can perform all of the cited functions. Additionally, said at least one processor may include a combination of processors that perform the enumerated / disclosed various functions, for example, in a distributed manner. At least one processor may execute program instructions to achieve or perform the various functions.

[0066] The processor (430) controls the overall operations of the electronic device (400). The processor (430) may be referred to as a control unit. For example, the processor (430) transmits and receives signals through the transceiver (410) (or through the backhaul communication unit). Additionally, the processor (430) writes and reads data to and from memory (420). Furthermore, the processor (430) can perform the functions of a protocol stack required by the communication standard. Although only the processor (430) is shown in FIG. 4, according to other implementation examples, the electronic device (400) may include two or more processors.

[0067] The configuration of the electronic device (400) illustrated in FIG. 4 is merely an example, and the examples of the electronic device (400) for performing embodiments of the present disclosure are not limited to the configuration illustrated in FIG. 4. In some embodiments, some configurations may be added, deleted, or changed. For example, if the electronic device (400) is a RU (230), the electronic device (400) may further include a fronthall transceiver. For example, the fronthall transceiver may transmit and receive signals on a fronthall interface. For example, the fronthall transceiver may receive a management plane (M-plane) message. For example, the fronthall transceiver may receive a synchronization plane (S-plane) message. For example, the fronthall transceiver may receive a control plane (C-plane) message. For example, the fronthall transceiver may transmit a user plane (U-plane) message. For example, the above fronthole transceiver can receive user plane messages.

[0068] Figure 5 illustrates an example of the connection status of SMO (service management and orchestration), CU (central unit), and DU.

[0069] FIG. 5 illustrates an example of a connection state between an SMO (500), a first CU (510), a second CU (520), a first DU (531), a second DU (532), and a third DU (540). The SMO (500) of FIG. 5 may be an example of the SMO (250) of FIG. 2. Each of the first CU (510) and the second CU (520) of FIG. 5 may be an example of the CU (210) of FIG. 2. Each of the first DU (531), the second DU (532), and the third DU (540) of FIG. 5 may be an example of the DU (220) of FIG. 2. The number of CUs and DUs shown in FIG. 5 is merely illustrative for convenience of explanation and is not limited thereto.

[0070] Referring to FIG. 5, the SMO (500) may be connected to each of the first CU (510) and the second CU (520). By example, without limitation, the first CU (510) may be configured to provide services for a first area in physical space, and the second CU (520) may be configured to provide services for a second area different from the first area. However, the present disclosure is not limited thereto.

[0071] For example, the first CU (510) may be connected to the first DU (531) and the second DU (532), respectively. For example, the first DU (531) may be configured to provide services for a first sub-region within the first area, and the second DU (532) may be configured to provide services for a second sub-region within the first area. By example, without limitation, the second sub-region may be different from the first sub-region. For example, the second CU (520) may be connected to the third DU (540). For example, the third DU (540) may be configured to provide services for the second area. Although not shown in FIG. 5, the first DU (531), the second DU (532), and the third DU (540) may each be connected to at least one RU. As a non-limiting example, the first CU (510) and the first DU (531) may be referred to as the first pair, the first CU (510) and the second DU (532) may be referred to as the second pair, and the second CU (520) and the third DU (540) may be referred to as the third pair.

[0072] Referring to FIG. 5, the base station (110) may be implemented by being separated into a CU, a DU, and an RU. As a non-limiting example, in a C-RAN (cloud / centralized / containerized-RAN) system, the base station (110) may be implemented by being separated into the CU, a DU, and an RU. For example, the DU and the CU may be deployed at the edge and center of the cloud. For example, the amount of traffic transmitted and received through the bandwidth between the CU and the DU (or the bandwidth of the midhall interface) may be limited. In one example, the amount of traffic may be determined by the functions (or layers) processed in the CU and the functions (or layers) processed in the DU. The functions processed in the CU and the functions processed in the DU may be determined by the functional separation between the CU and the DU. For example, the functional separation may be referred to as a split architecture.

[0073] The functional separation between the CU and the DU can be determined regardless of the current load (or hourly load) of the CU and the current load of the DU. Additionally, the functional separation between the CU and the DU can be determined regardless of the region where each of the CU and the DU is deployed. Furthermore, the functional separation between the CU and the DU can be fixed according to a determined option when the CU and the DU are deployed (or distributed). In other words, when the CU and the DU are deployed (or distributed), a functional separation fixed according to a determined option can be used, and resources can be allocated regardless of the amount of load in the region. As a more specific example, when the base station (110) is implemented through a V-RAN (virtual-RAN) system, each of the CU and the DU can be implemented through a VM (virtual machine). For example, the VM for the above CU may be configured (or implemented) to perform the functions of the CU (e.g., functions of the RRC layer). Also, for example, the VM for the above DU may be configured (or implemented) to perform the functions of the DU (e.g., functions of the MAC layer). However, even when using a VM, the VM may be configured to perform the functions of the CU (or functions of the DU) according to a fixed functional separation based on the options determined as described above. Also, even when using a VM, the hardware components for performing the functions by the VM (e.g., cores of the CPU (central processing unit), memory) may be fixedly configured according to the functional separation.

[0074] In the example of FIG. 5, the first CU (510) and the first DU (531) may be configured as the first option (e.g., Option 4) of the functional separation. For example, the functions of the first CU (510) and the functions of the first DU (531) may be defined according to the first option. Alternatively, in the example of FIG. 5, the first CU (510) and the second DU (532) may be configured as the first option of the functional separation. For example, the first CU (510) may be configured as the first option to provide services to the first DU (531) and the second DU (532), respectively. Alternatively, in the example of FIG. 5, the second CU (520) and the third DU (540) may be configured as the second option (e.g., Option 6) of the functional separation.

[0075] Referring to the example described above, the CUs (510, 520) may utilize different functional separations depending on the region where the CU is deployed and the DU to which it is connected. However, when the CU and DU are deployed (or distributed), the determined functional separation may be used in a fixed manner regardless of changes in load (or changes in connection status).

[0076] Hereinafter, the present disclosure can dynamically adjust the functional separation between the CU (210) and the DU (220) by implementing the functions of the CU (210) and the functions of the DU (220) through a containerized network function (CNF). For example, the present disclosure can adjust the resources of the functions (or CNFs) of a network node considering the network state by using a model trained at a network node (e.g., CU (210) or DU (220)). For example, the present disclosure can adjust the functions of a network node according to the quality of service provided by the functions (or CNFs) of the network node configured through the adjusted resources. When adjusting the functions of a network node, the functions of other network nodes may be adjusted together. In other words, the functions of a network node and other network nodes may be distributed (or redistributed, reconfigured, partitioned, or repartitioned). The present disclosure enables the flexible (or adaptive) utilization of network node resources according to the network environment (e.g., nodes, performance) by dynamically distributing functions among network nodes. Additionally, accordingly, the present disclosure can reduce the design and management costs of network nodes by optimizing the utilization of network node resources.

[0077] Hereinafter, FIG. 6 illustrates examples of options available for functional separation between CU (210) and DU (220).

[0078] Figure 6 illustrates examples of options for functional separation.

[0079] FIG. 6 illustrates examples of options for functional separation between CU (210) and DU (220). The separated (or distinguished) layers shown in FIG. 6 are for convenience of explanation only and the structure of the layers of the present disclosure is not limited thereto.

[0080] Referring to FIG. 6, an RRC layer (610), a PDCP layer (620), a high RLC layer (631), a low RLC layer (632), a high MAC layer (641), a low MAC layer (642), a high physical layer (651), a low physical layer (652), and an RF (radio frequency) layer (660) may be defined. In FIG. 6, each layer may be referred to by the functions of each layer.

[0081] The functions of the RRC layer (610) may include at least some of the following functions.

[0082] - Broadcasting system information related to AS (Access Stratum) and NAS

[0083] - Paging initiated by 5GC (5G Core) or NG-RAN (Next Generation-Radio Access network)

[0084] - Establishment, maintenance, and release of the RRC connection between the UE and NG-RAN, including, specifically, control over RLC, MAC, and PHY:

[0085] - Adding, modifying, and removing Carrier Aggregation

[0086] - Add, modify, and disable dual connectivity between NR or E-UTRA and NR.

[0087] - Security features including Key Management;

[0088] - Setup, configuration, maintenance, and release of SRB (Signaling Radio Bearer) and DRB (Data Radio Bearer)

[0089] - Movement functions including the following:

[0090] - Handover and context transfer;

[0091] - UE cell selection and re-selection and cell selection and re-selection control;

[0092] - Mobility between RATs.

[0093] - QoS (quality of service) management function;

[0094] - UE measurement reporting and control of reporting;

[0095] - Radio link failure detection and recovery

[0096] - Send messages from / to UE to / from NAS.

[0097] The functions of the PDCP layer (620) may include at least some of the following functions.

[0098] - Header compression and decompression features (ROHC only)

[0099] - User data transfer function (Transfer of user data)

[0100] - Sequential delivery function (In-order delivery of upper layer PDU (protocol data unit)s)

[0101] - Out-of-order delivery of upper layer PDUs

[0102] - Reordering function (PDCP PDU reordering for reception) (hereinafter, reordering)

[0103] - Duplicate detection function (Duplicate detection of lower layer SDUs)

[0104] - Retransmission of PDCP SDUs

[0105] - Encryption and decryption functions (Ciphering and deciphering)

[0106] - Timer-based SDU discard in uplink.

[0107] The functions of the RLC layer may include at least some of the following functions. For example, some of the functions of the RLC layer (e.g., non-real-time functions) may be functions of the upper RLC layer (631). For example, other functions of the RLC layer (e.g., real-time functions) may be functions of the lower RLC layer (632).

[0108] - Data transfer function (Transfer of upper layer PDUs)

[0109] - Sequential delivery function (In-sequence delivery of upper layer PDUs)

[0110] - Out-of-sequence delivery of upper layer PDUs

[0111] - ARQ function (Error Correction through ARQ)

[0112] - Concatenation, segmentation, and reassembly functions of RLC SDUs

[0113] - Re-segmentation function (Re-segmentation of RLC data PDUs)

[0114] - Reordering function (Reordering of RLC data PDUs)

[0115] - Duplicate detection

[0116] - Error detection function (Protocol error detection)

[0117] - RLC SDU discard function

[0118] RLC re-establishment function

[0119] The MAC layer may be connected to multiple RLC layers configured in a single terminal. By example, without limitation, the functions of the MAC layer may include at least some of the following functions. For example, some of the functions of the MAC layer may be functions of the upper MAC layer (641). For example, other functions of the MAC layer may be functions of the lower MAC layer (642).

[0120] - Mapping function between logical channels and transport channels

[0121] - Multiplexing and demultiplexing of MAC SDUs

[0122] - Scheduling information reporting function

[0123] Error correction through HARQ

[0124] - Priority handling between logical channels of one UE

[0125] - Priority handling between UEs by means of dynamic scheduling

[0126] - MBMS service identification

[0127] - Transport format selection function

[0128] - Padding

[0129] The physical layer may include functions for channel coding and modulating upper layer data, creating OFDM symbols and transmitting them over a wireless channel, or demodulating OFDM symbols received over a wireless channel and channel decoding them to transmit them to an upper layer. For example, some of the functions of the physical layer may be functions of the upper physical layer (651). For example, other functions of the physical layer may be functions of the lower physical layer (652).

[0130] More specifically, for the downlink (DL) that transmits a signal to a terminal via a wireless network, the base station may sequentially perform channel encoding / scrambling, modulation, layer mapping, antenna mapping, RE mapping, digital beamforming (e.g., precoding), iFFT transform / CP insertion, and RF transformation. For the uplink (UL) that receives a signal from a terminal via a wireless network, the base station may sequentially perform RF transformation, FFT transform / CP removal, digital beamforming (pre-combining), RE demapping, channel estimation, layer demapping, demodulation, and decoding / scrambling.

[0131] In FIG. 6, layers on the control plane are illustrated for convenience of explanation, but the present disclosure is not limited thereto. The present disclosure may be applied to layers on the user plane. For example, the layers on the user plane may further include a service data adaptation protocol (SDAP) layer, a general packet radio service (GPRS) tunnelling protocol (GTP) layer, or an internet protocol (IP) layer, together with a PDCP layer (620), a high RLC layer (631), a low RLC layer (632), a high MAC layer (641), a low MAC layer (642), a high physical layer (651), a low physical layer (652), and an RF layer (660).

[0132] As a non-limiting example, the RRC layer (610), PDCP layer (620), upper RLC layer (631), lower RLC layer (632), upper MAC layer (641), lower MAC layer (642), upper physical layer (651), and lower physical layer (652) can be classified into multiple layer levels (670). For example, the upper physical layer (651) and lower physical layer (652) can be classified as Layer 1 (671). For example, the upper RLC layer (631), lower RLC layer (632), upper MAC layer (641), and lower MAC layer (642) can be classified as Layer 2 (672). For example, the RRC layer (610) and PDCP layer (620) can be classified as Layer 3 (673).

[0133] For example, CU (210) performing the functions of the RRC layer (610) and DU (220) performing the functions of the PDCP layer (620) or lower may be referred to as Option 1 (691). For example, CU (210) performing the functions of the RRC layer (610) and the PDCP layer (620), and DU (220) performing the functions of the upper RLC layer (631) or lower may be referred to as Option 2 (692). For example, CU (210) performing the functions of the RRC layer (610) or the upper RLC layer (631), and DU (220) performing the functions of the lower RLC layer (632) or lower may be referred to as Option 3 (693). For example, CU (210) performing functions of the RRC layer (610) or lower RLC layer (632) and DU (220) performing functions of the upper MAC layer (641) or lower may be referred to as Option 4 (694). For example, CU (210) performing functions of the RRC layer (610) or upper MAC layer (641) and DU (220) performing functions of the lower MAC layer (642) or lower may be referred to as Option 5 (695). For example, CU (210) performing functions of the RRC layer (610) or lower MAC layer (642) and DU (220) performing functions of the upper physical layer (651) or lower may be referred to as Option 6 (696). For example, CU (210) performing functions of the RRC layer (610) or the upper physical layer (651), and DU (220) performing functions of the lower physical layer (652) or lower, may be referred to as Option 7 (697). For example, CU (210) performing functions of the RRC layer (610) or the lower physical layer (652), and DU (220) performing functions of the RF layer (660), may be referred to as Option 8 (698).

[0134] Although not illustrated in FIG. 6, further functional separation between DU (220) and RU (230) may be considered. For example, functional separation between DU (220) and RU (230) may be related to functional separation between CU (210) and DU (220). As a non-limiting example, if the functional separation between CU (210) and DU (220) is Option 2 (692), the functional separation between DU (220) and RU (230) may be one of the options below Option 2 (692) (e.g., Option 7 (697) or Option 7.2x).

[0135] Figure 7 illustrates an example of how to define each of the layers using a containerized network function (CNF).

[0136] FIG. 7 illustrates an example of how each of the layers is defined using CNF. For example, the layers may include the layers illustrated in FIG. 6. In FIG. 7, for convenience of explanation, the RLC layer (710), PDCP layer (720), and RRC layer (730) are illustrated, but the present disclosure is not limited thereto.

[0137] For example, a CNF may represent an entity that performs functions of a layer, or a portion of functions of a layer. For example, a CU (210) may include (or be composed of) multiple CNFs. For example, a CNF may perform functions using resources of a network node (e.g., CU (210) or DU (220)). For example, the resources of the network node may include a core of a processing circuit (e.g., CPU of a processor (430)), memory (e.g., memory (420)), and the frequency (or operating frequency) of the core. For example, the core may represent hardware (or a combination of hardware and software) that performs calculations (or processing) required (or utilized) for the function of the CNF. For example, information about resources utilized by a specific CNF (hereinafter, resource information) may include the number of cores utilized for the specific CNF, memory usage, and the operating frequency of each core.

[0138] Referring to FIG. 7, as a non-limiting example, the functions of the RLC layer (710) may include a first function (711), a second function (712), and a third function (713). As a non-limiting example, the functions of the PDCP layer (720) may include a fourth function (721) and a fifth function (722). As a non-limiting example, the functions of the RRC layer (730) may be defined as a single function (or a set of functions).

[0139] For example, among the functions of the RLC layer (710), the first function (711) may be composed of (or defined by) the first CNF. For example, the first function (711) being composed of the first CNF (or the first function (711) being defined by the first CNF) may indicate that the first CNF provides (or performs) the first function (711). For example, among the functions of the RLC layer (710), the second function (712) may be composed of a second CNF different from the first CNF. Also, for example, among the functions of the RLC layer (710), the third function (713) may be composed of the second CNF. In other words, the second CNF may provide (or perform) the second function (712) and the third function (713). For example, among the functions of the PDCP layer (720), the fourth function (721) may be composed of a third CNF that is different from each of the first CNF and the second CNF. Additionally, for example, among the functions of the PDCP layer (720), the fifth function (722) may be composed of a fourth CNF that is different from each of the first CNF to the third CNF. For example, the functions (or a set of functions) of the RRC layer (730) may be composed of a fifth CNF that is different from each of the first CNF to the fourth CNF.

[0140] Referring to FIG. 7, the functions of the RLC layer (710), PDCP layer (720), and RRC layer (730) may be composed of five CNFs. In the example of FIG. 7, the functions of one layer may be composed of one CNF or multiple CNFs. However, the present disclosure is not limited thereto. For example, the functions of each of all layers may be composed of one CNF. In other words, a CNF may be composed (or assigned) for each layer, and all functions of each layer may be composed (or performed) by said single CNF. Or, for example, some functions of multiple layers (e.g., third function (713) and fourth function (721)) may be composed of one CNF.

[0141] Referring to FIGS. 6 and 7, the functions (or all functions) of the CU (210) and DU (220) can be split into the functions of the CU (210) (or functions performed by CNFs included in the CU (210)) and the functions of the DU (220) (or functions performed by CNFs included in the DU (220). As described above, when the all functions of the CU (210) and DU (220) are split according to a specific option of functional splitting in a V-RAN system, the functions of the CU (210) and the functions of the DU (220) can be fixed.

[0142] In contrast, the present disclosure allows the functions of the CU (210) and the functions of the DU (220), implemented by using CNFs in a C-RAN system, to be dynamically adjusted. More specifically, after the first functions of the CU (210) and the second functions of the DU (220) among the total functions are divided according to a specific option, the functions of the CU (210) can be changed from the first functions to the third functions, and in accordance with the change from the first functions to the third functions, the functions of the DU (220) can be changed from the second functions to the fourth functions. For example, the present disclosure can adjust (or change) resource information of the CNFs (or functions) of a network node using a trained model according to the load of the network node (e.g., CU (210) and / or DU (220)). For example, the present disclosure may perform coordination (or redistribution, redistribution) between the functions of a network node (e.g., CU (210)) and the functions of another network node (e.g., DU (220)) when the quality of service provided through CNFs according to changed resource information is lower than a certain level.

[0143] Specific details regarding the method of dynamically adjusting the functions of CU (210) and DU (220) may be referenced in FIG. 8 below.

[0144] Figure 8 illustrates an example of a signal flow for a method of adjusting the functional split between the CU and the DU.

[0145] FIG. 8 illustrates an example of a signal flow for coordinating functional separation between a CU (801) and a DU (802). The CU (801) of FIG. 8 may be an example of the CU (210) of FIG. 2 (or the upper network node (310) of FIG. 3). The DU (802) of FIG. 8 may be an example of the DU (220) of FIG. 2 (or the lower network node (320) of FIG. 3). The SMO (803) of FIG. 8 may be an example of the SMO (250) of FIG. 2.

[0146] Although not illustrated in FIG. 8, CU (801), DU (802), and SMO (803) may be connected. For example, SMO (803) may be connected to one or more CUs. For example, the one or more CUs may include CU (801). For example, CU (801) may be connected to one or more DUs. For example, the one or more DUs may include DU (802). Although not illustrated in FIG. 8, DU (802) may be connected to one or more RUs.

[0147] Referring to FIG. 8, in operation (805), CU (801) and DU (802) can perform functional separation. For example, CU (801) can identify the functions of CU (801) and the functions of DU (802) among the functions of CU (801) and DU (802) (hereinafter, all functions). By example, without limitation, the functions of CU (801) may include first functions among the all functions. By example, without limitation, the functions of DU (802) may include second functions among the all functions. For example, the second functions may be distinguished (or different, separated) from the first functions among the all functions. For example, CU (210) can identify the first functions of CU (210) and the second functions of DU (802).

[0148] For example, CU (801) may allocate (or distribute) resources for each of the first functions. For example, each of the first functions may be configured by CNF. For example, resources for each of the first functions may include the number of cores used for CNF, memory usage, and the operating frequency of each core. Information on resources for each of the first functions may be referenced as first resource information.

[0149] For example, the CU (801) may transmit configuration information to the DU (802) instructing the DU (802) to configure the second functions. For example, the DU (802) may allocate (or distribute) resources for each of the second functions based on the received configuration information. For example, each of the second functions may be configured by a CNF. For example, resources for each of the second functions may include the number of cores used for the CNF, memory usage, and the operating frequency of each core. Information on resources for each of the second functions may be referenced as second resource information.

[0150] In operation (810), the CU (801) can obtain resource information. For example, the CU (801) can obtain the first resource information of the first functions. For example, the first resource information may represent resources being used according to the current function of the CU (801). For example, the first resource information may be information related to the load of the CU (801).

[0151] As a non-limiting example, the CU (801) may obtain a key performance indicator (KPI) along with the first resource information. For example, the KPI may represent a KPI related to the load of the CU (801). For example, the KPI may be defined by the hierarchy of the CU (801). For example, the hierarchy of the CU (801) may represent the hierarchy of the functions of the CU (801) (e.g., the first functions). As a non-limiting example, if the functions of the CU (801) include functions of the RRC hierarchy (e.g., the RRC hierarchy (610) in FIG. 6) and functions of the PDCP hierarchy (e.g., the PDCP hierarchy (620) in FIG. 6), the hierarchy of the CU (801) may include the RRC hierarchy and the PDCP hierarchy. Specific examples of the KPI obtained by the CU (801) may be referenced below in FIG. 9.

[0152] Referring to the foregoing, the CU (801) may obtain the resource information and / or the KPI. For example, the CU (801) may collect the resource information and / or the KPI periodically (or non-periodically). In this disclosure, the resource information and / or KPI may be referred to as the input, input data, or input information of a trained model stored in the CU (801). In this disclosure, a trained model stored in a network node (e.g., CU (801), DU (802), or SMO (803)) may be referred to as an agent or ML agent.

[0153] In operation (815), the DU (802) can obtain resource information. For example, the DU (802) can obtain the second resource information of the second functions. For example, the second resource information may represent resources being used according to the current function of the DU (802). For example, the second resource information may be information related to the load of the DU (802).

[0154] As an example without limitation, the DU (802) may obtain a key performance indicator (KPI) along with the second resource information. For example, the KPI may represent a KPI related to the load of the DU (802). For example, the KPI may be defined by hierarchy of the DU (802). For example, the hierarchy of the DU (802) may represent the hierarchy of the functions of the DU (802) (e.g., the second functions). As a non-limiting example, if the functions of the DU (802) include functions of an RLC layer (e.g., upper RLC layer (631) and lower RLC layer (632) of FIG. 6), functions of a MAC layer (e.g., upper MAC layer (641) and lower MAC layer (642) of FIG. 6), and functions of a physical layer (e.g., upper physical layer (651) and lower physical layer (652) of FIG. 6), the layers of the DU (802) may include an RLC layer, a MAC layer, and a physical layer. Specific examples of the KPIs obtained by the DU (802) may be referenced below in FIG. 9.

[0155] Referring to the foregoing, the DU (802) may acquire the resource information and / or the KPI. For example, the DU (802) may collect the resource information and / or the KPI periodically (or non-periodically). In this disclosure, the resource information and / or KPI may be referred to as the input, input data, or input information of a trained model stored in the DU (802).

[0156] In FIG. 8, the operation (810) and the operation (815) are depicted as being performed simultaneously, but the present disclosure is not limited thereto. For example, the operation (810) may be performed before the operation (815), or the operation (815) may be performed before the operation (810).

[0157] In operation (820), the SMO (803) may transmit reference resource information. For example, the SMO (803) may transmit the reference resource information to the CU (801) and / or DU (802). For example, the reference resource information may be used as a limiting condition for the current function of the CU (801) (e.g., the first functions). Also, for example, the reference resource information may be used as a limiting condition for the current function of the DU (802) (e.g., the second functions). For example, the reference resource information may include the maximum and minimum number of cores used for each of the functions (e.g., the first functions or the second functions). Or, for example, the reference resource information may include the maximum and minimum usage of memory used for each of the functions (e.g., the first functions or the second functions). Alternatively, for example, the reference resource information may include the maximum operating frequency and minimum operating frequency (or available operating frequency band) of the core used for each of the functions (e.g., the first functions or the second functions). In the present disclosure, the reference resource information may be referred to as a policy or guide information.

[0158] Although not illustrated in FIG. 8, SMO (803) can generate the reference resource information using a trained model stored within SMO (803). For example, SMO (803) can generate the reference resource information by providing KPIs of one or more CUs connected to SMO (803) to a trained model stored within SMO (803). For example, the one or more CUs connected to SMO (803) may include CU (801). For example, specific details regarding the KPIs used in SMO (803) may be referenced below in FIG. 9.

[0159] In FIG. 8, the operation (820) is illustrated as being performed after the operation (810) and the operation (815), but the present disclosure is not limited thereto. For example, the operation (820) may be performed before the operation (810) and the operation (815), or simultaneously.

[0160] In operation (825), the CU (801) can generate resource information using a trained model. For example, the CU (801) can provide the first resource information to a trained model stored within the CU (801). For example, the CU (801) can generate third resource information by providing the first resource information to a trained model stored within the CU (801). For example, the third resource information can be used for each of the functions of the CU (801) (e.g., the first functions). In other words, the third resource information may indicate the number of cores, memory usage, or operating frequency of each core, which is resource information for each of the first functions, like the first resource information, but is different from the first resource information. Or, for example, the third resource information may include a change in the number of cores from the first resource information.

[0161] In the above example, the third resource information is exemplified as being generated using the first resource information, but the present disclosure is not limited thereto. For example, KPIs and / or reference resource information may be further utilized together with the first resource information. For example, when the KPI is further provided to a trained model stored in the CU (801), the third resource information may be generated to satisfy the service quality (or performance) according to the KPI. Or, for example, when the reference resource information is further provided to a trained model stored in the CU (801), the third resource information may be generated to satisfy the reference resource information.

[0162] In the present disclosure, the trained model may be referred to as a model learned based on reinforcement learning (RL) (or an RL model). For example, the trained model may be referred to as a machine learning (ML) model. By example, without limitation, the trained model of the present disclosure may be learned not only within a specific network node (e.g., CU (801), DU (802)) but also at other network nodes (e.g., SMO (803) or a server). More specifically, the trained model of the present disclosure may be federated learning using information about models of multiple network nodes. Specific details regarding this may be referenced below in FIGS. 10a and FIGS. 10b.

[0163] In operation (830), the DU (802) can generate resource information using a trained model. For example, the DU (802) can provide the second resource information to a trained model stored within the DU (802). For example, the trained model stored within the DU (802) may represent a model distinct from the trained model stored within the CU (801). However, the trained model stored within the DU (802) and the trained model stored within the CU (801) may be models generated according to the same learning technique (e.g., federated learning). For example, the DU (802) can generate a fourth resource information by providing the second resource information to a trained model stored within the DU (802). For example, the fourth resource information may be used for each of the functions of the DU (802) (e.g., the second functions). In other words, the fourth resource information, like the second resource information, is resource information for each of the second functions, but may indicate the number of cores, memory usage, or operating frequency of each core that is different from the second resource information. Alternatively, for example, the fourth resource information may include a change in the number of cores from the second resource information.

[0164] In the above example, the fourth resource information is exemplified as being generated using the second resource information, but the present disclosure is not limited thereto. For example, KPIs and / or reference resource information may be further utilized together with the second resource information. For example, when the KPI is further provided to a trained model stored in the DU (802), the fourth resource information may be generated to satisfy the service quality (or performance) according to the KPI. Or, for example, when the reference resource information is further provided to a trained model stored in the DU (802), the fourth resource information may be generated to satisfy the reference resource information.

[0165] Specific details regarding the operation (825) and operation (830) may be referenced below in FIG. 9. In FIG. 8, the operation (825) and operation (830) are depicted as being performed simultaneously, but the present disclosure is not limited thereto. For example, the operation (825) may be performed before the operation (830), or the operation (830) may be performed before the operation (825).

[0166] In operation (835), the DU (802) may transmit a request for distribution. For example, the DU (802) may transmit the request to the CU (801). As an example without limitation, the DU (802) may generate the request if the quality of service provided through the second functions configured based on the fourth resource information generated by the DU (802) in operation (830) is below the reference quality. For example, the DU (802) may configure the second functions based on the fourth resource information.

[0167] In the present disclosure, a network node (e.g., CU (801), or DU (802)) configuring functions in resource information may include configuring a CNF that performs (or, for a specific function) using resources identified based on resource information (e.g., number of cores, memory usage, or operating frequency of each core).

[0168] For example, the DU (802) can identify whether the service quality provided through the second functions configured based on the fourth resource information is below the reference quality. For example, if the service quality is below the reference quality, the DU (802) can generate the request for the redistribution of all functions (or redistribution of functional separation) between the CU (801) and the DU (802) and transmit the request to the CU (801). In the above example, the DU (802) is illustrated as comparing the service quality with the reference quality, but the present disclosure is not limited thereto. For example, the DU (802) may compare the power consumption and the reference power consumption when using the second functions configured based on the fourth resource information. Or, for example, the DU (802) may compare the performance of each function and the reference performance when using the second functions configured based on the fourth resource information.

[0169] In FIG. 8, operation (835) is depicted as being performed, but the present disclosure is not limited thereto. For example, operation (835) may be omitted. For example, if the service quality is above the reference quality, the DU (802) may provide (or maintain providing) the service using the second functions configured based on the fourth resource information. In this case, the DU (802) may refrain from (or bypass, skip, or not perform) the transmission of the request for distribution.

[0170] For example, as operation (835) is performed, CU (801) can determine whether to perform redistribution of all functions (or redistribution of functional separation) between CU (801) and DU (802) at the request of DU (802). Or, for example, even if operation (835) is not performed, CU (801) can determine whether to perform redistribution of all functions (or redistribution of functional separation) between CU (801) and DU (802) by performing operation (840) described later.

[0171] In operation (840), the CU (801) can identify the service quality. For example, the CU (801) can configure the first functions based on the third resource information. For example, the CU (801) can identify whether the service quality provided through the first functions configured based on the third resource information is below a reference quality. The service quality identified in operation (840) may represent the quality of the service provided according to the first functions of the CU (801). In other words, the service quality identified in operation (840) may differ from the service quality identified by the DU (802) in relation to operation (835) (or the quality of the service provided according to the second functions of the DU (802)). Additionally, the reference quality compared with the service quality in operation (840) may differ from the reference quality compared with the service quality identified by the DU (802) in relation to operation (835). However, the present disclosure is not limited thereto. For example, the reference quality compared with the service quality in operation (840) may be the same as the reference quality compared with the service quality identified by the DU (802) in relation to operation (835).

[0172] In the above example, CU (801) is illustrated as comparing the service quality and the reference quality, but the present disclosure is not limited thereto. For example, CU (801) may compare power consumption and reference power consumption with respect to power consumption when using the first functions configured based on the third resource information. Or, for example, CU (801) may compare the performance of each function and reference performance when using the first functions configured based on the third resource information.

[0173] As an example not limited to, when CU (801) receives the request from DU (802) in operation (835), it may respond to the receipt of the request by identifying the service quality and comparing the identified service quality with the reference quality.

[0174] For example, CU (801) may perform operation (845) when the service quality is below the reference quality. Alternatively, CU (801) may provide (or maintain providing) the service using the first functions configured based on the third resource information when the service quality is above the reference quality.

[0175] In operation (845), CU (801) and DU (802) can perform a distribution of functional separation. For example, CU (801) can identify third functions modified from the first functions as the functions of CU (801) among the total functions of CU (801) and DU (802), based on identifying that the service quality provided through the first functions configured based on the third resource information is below the standard quality. For example, CU (801) can identify fourth functions modified from the second functions as the functions of DU (802) among the total functions of CU (801) and DU (802), based on identifying that the service quality provided through the first functions configured based on the third resource information is below the standard quality. For example, the fourth functions may be distinguished (or different, separated) from the third functions among the entire set of functions. For example, CU (210) may identify the third functions of CU (210) and the fourth functions of DU (802).

[0176] For example, CU (801) may allocate (or distribute) resources for each of the third functions. For example, each of the third functions may be configured by CNF. For example, resources for each of the third functions may include the number of cores used for CNF, memory usage, and the operating frequency of each core. Information on resources for each of the third functions may be referenced as fifth resource information. For example, the fifth resource information may be different from the third resource information.

[0177] For example, the CU (801) may generate configuration information instructing the DU (802) to configure the fourth functions. For example, the CU (801) may transmit the configuration information to the DU (802). For example, the DU (802) may allocate (or distribute) resources for each of the fourth functions based on the received configuration information. For example, each of the fourth functions may be configured by a CNF. For example, the resources for each of the fourth functions may include the number of cores used for the CNF, memory usage, and the operating frequency of each core.

[0178] Referring to the above description, the CU (801) can perform the distribution (or redistribution, separation) of functional separation by configuring the third functions using CNFs. Additionally, the CU (801) can cause (or allow) the DU (802) to perform the distribution (or redistribution, separation) of functional separation by transmitting the configuration information to the DU (802) instructing it to change to the fourth functions distinct from the third functions.

[0179] Figure 9 illustrates an example of a method for generating resource information using a trained model.

[0180] FIG. 9 illustrates an example of a method for generating resource information using a trained model (900) stored in a network node of the present disclosure. For example, the trained model (900) may be stored in each of the CU (210), DU (220), and SMO (250) of FIG. 2.

[0181] Referring to FIG. 9, the trained model (900) may include an actor (901). The components of the trained model (900) illustrated in FIG. 9 (e.g., the actor (901)) may be associated with a reinforcement learning algorithm of the trained model (900) (e.g., an actor-environment). For example, although not illustrated in FIG. 9, the trained model (900) may further include an environment for generating a reward (930) from an output (920) (or action). FIG. 9 illustrates a trained model (900) trained based on a reinforcement learning algorithm, but the present disclosure is not limited thereto. For example, the trained model (900) may be trained based on Q-learning.

[0182] For example, an actor (901) may represent a configuration that generates an output (920) based on an input (910). For example, the input (910) and the output (920) may depend on network nodes that store (or include) a trained model (900). For example, the network nodes may include a CU (210), a DU (220), and an SMO (250).

[0183] For example, if the network node storing (or including) the trained model (900) is a CU (210) or a DU (220), the input (910) may include resource information. For example, the resource information may include resource information currently being used by the network node (e.g., CU (210) or DU (220)). For example, the resource information may include the number of cores used for the CNF, memory usage, and the operating frequency of each core.

[0184] Alternatively, if the network node storing (or including) the trained model (900) is a CU (210) or DU (220), the input (910) may include KPIs defined by layer of the network node. Examples of KPIs defined by layer may be exemplified as follows.

[0185] As a non-limiting example, KPIs related to the physical layer may include at least one of SNR (signal-to-noise ratio), BER (bit error rate), physical layer throughput, physical layer latency, or physical layer capacity. For example, the SNR may represent the strength of the received signal (or data) relative to noise. For example, the BER may represent the ratio of bits received incorrectly due to factors such as errors. For example, the physical layer throughput may represent the amount of data successfully transmitted through the physical layer during a unit of time (or period). For example, the physical layer latency may represent the time (or average time) spent in the physical layer before transmitting data. For example, the physical layer capacity may represent the maximum amount of data that can be transmitted through the physical layer.

[0186] As a non-limiting example, KPIs related to the MAC layer may include at least one of channel utilization, MAC layer packet delay, MAC layer packet loss rate, MAC layer throughput, or MAC layer fairness. For example, the channel utilization may represent the ratio of the time used for data transmission to the total available time. For example, the MAC layer packet delay may represent the time (or average time) that packets wait in a queue before being transmitted through the channel. For example, the MAC layer packet loss rate may represent the ratio of packets dropped (or lost) due to factors such as errors. For example, the MAC layer throughput may represent the amount of data successfully transmitted through the MAC layer during a unit of time (or cycle). For example, the MAC layer fairness may represent the extent to which the MAC layer can provide fair access to the channel for all users (or devices).

[0187] As a non-limiting example, KPIs related to the RLC layer may include at least one of the RLC layer throughput, the RLC layer packet loss rate, the RLC layer buffer occupancy, the RLC retransmission rate, or the RLC latency. For example, the RLC layer throughput may represent the amount of data successfully transmitted through the RLC layer during a unit of time (or cycle). For example, the RLC layer packet loss rate may represent the ratio of packets dropped (or lost) due to factors such as errors. For example, the RLC layer buffer occupancy may represent the amount of data stored in RLC buffers at the base station and the terminal. For example, the RLC retransmission rate may represent the ratio of packets retransmitted due to factors such as errors. For example, the RLC layer latency may represent the time (or average time) spent in the RLC layer before transmitting data.

[0188] As a non-limiting example, KPIs related to the PDCP layer may include at least one of the PDCP layer throughput, the PDCP layer packet loss rate, the PDCP layer buffer occupancy, the PDCP retransmission rate, or the PDCP latency. For example, the PDCP layer throughput may represent the amount of data successfully transmitted through the PDCP layer during a unit of time (or cycle). For example, the PDCP layer packet loss rate may represent the ratio of packets dropped (or lost) due to factors such as errors. For example, the PDCP layer buffer occupancy may represent the amount of data stored in PDCP buffers at the base station and the terminal. For example, the PDCP retransmission rate may represent the ratio of packets retransmitted due to factors such as errors. For example, the PDCP layer latency may represent the time (or average time) spent in the PDCP layer before transmitting data.

[0189] As a non-limiting example, KPIs related to the GTP layer may include at least one of the GTP layer throughput, the GTP layer packet loss rate, the GTP layer buffer occupancy, the GTP retransmission rate, or the GTP latency. For example, the GTP layer throughput may represent the amount of data successfully transmitted through the GTP layer during a unit of time (or cycle). For example, the GTP layer packet loss rate may represent the ratio of packets dropped (or lost) due to factors such as errors. For example, the GTP layer buffer occupancy may represent the amount of data stored in GTP buffers at the base station and the terminal. For example, the GTP retransmission rate may represent the ratio of packets retransmitted due to factors such as errors. For example, the GTP layer latency may represent the time (or average time) spent in the GTP layer before transmitting data.

[0190] As a non-limiting example, KPIs related to the IP layer may include at least one of IP layer throughput, IP layer packet loss rate, IP layer buffer occupancy, IP retransmission rate, or IP latency. For example, the IP layer throughput may represent the amount of data successfully transmitted through the IP layer during a unit of time (or cycle). For example, the IP layer packet loss rate may represent the ratio of packets dropped (or lost) due to factors such as errors. For example, the IP layer buffer occupancy may represent the amount of data stored in IP buffers at base stations and terminals. For example, the IP retransmission rate may represent the ratio of packets retransmitted due to factors such as errors. For example, the IP layer latency may represent the time (or average time) spent in the IP layer before transmitting data.

[0191] Alternatively, if the network node storing (or including) the trained model (900) is a CU (210) or DU (220), the input (910) may include additional parameters. For example, the additional parameters may include inter / intra base station interference information, the number of active cells, QoS (quality of service) load, SCS (sub carrier spacing) (or numerology), CPU architecture, core temperatures, and memory types.

[0192] Alternatively, if the network node storing (or including) the trained model (900) is a CU (210) or DU (220), the input (910) may further include target parameters to satisfy specific KPIs (or service quality). As an example without limitation, the target parameters may include policies (e.g., reference resource information) received from the SMO (250).

[0193] For example, if the network node storing (or including) the trained model (900) is a CU (210) or a DU (220), the output (920) may include resource information. For example, the resource information of the output (920) may include the number of cores to be used for CNF, memory usage, and the operating frequency of each core. In the above example, the resource information of the output (920) is exemplified as including the number of cores, but the present disclosure is not limited thereto. For example, the resource information of the output (920) may include a number (or amount of change, increase, decrease) that changes from the current number of cores. In the present disclosure, the output (920) may be referred to as an action.

[0194] For example, a network node (or a communication device configured to perform the function of a network node) that stores (or includes) a trained model (900) may perform functions that utilize modified resources using an output (920). For example, the network node may identify the quality of service provided through the functions that utilize modified resources. In the above example, identifying the quality of service is illustrated, but the present disclosure is not limited thereto. For example, the network node may identify power consumption or performance.

[0195] For example, a network node (or a communication device configured to perform the function of a network node) that stores (or includes) a trained model (900) may generate a reward (930) using the quality of service (and / or power consumption, performance). For example, the reward (930) may be used as the target parameter included in the input (910) of the trained model (900). For example, the reward (930) may include a policy that may be used to output resource information such that the quality of service (and / or power consumption, performance) satisfies a reference quality.

[0196] In the above example, the network node storing (or including) the trained model (900) is exemplified as a CU (210) or DU (220), but the present disclosure is not limited thereto. Below, the network node storing (or including) the trained model (900) is exemplified as an SMO (250).

[0197] If the network node storing (or including) the trained model (900) is an SMO (250), the input (910) may include a KPI for optimizing power consumption. For example, the KPI for optimizing power consumption may include KPIs for managing one or more CUs (and / or one or more DUs) connected to the SMO (25). For example, the KPI for optimizing power consumption may be exemplified as follows.

[0198] As a non-limiting example, the KPI used as input in SMO (250) may include at least one of a resource utilization metric, service demand and load, service response time, service availability and downtime, configuration and policy settings, environment factors, workload characteristics, or energy consumption metric.

[0199] For example, the resource usage metrics may represent the usage of resources such as CPU usage, memory usage, and network bandwidth. For example, the service demand and load may represent demand and load, such as the number of service requests, transaction volumes, and user activity levels. For example, the service response time may represent the response time of services managed by the SMO. For example, the service availability and downtime may represent the availability and downtime of services managed by the SMO. For example, the configuration and policy settings may represent metrics related to the configuration and policy settings of the SMO. For example, the environmental factors may represent environmental factors that can affect power consumption, such as temperature, humidity, and cooling performance. For example, the above workload characteristics may represent workload characteristics such as the type of workload (e.g., compute-intensive, input / output-intensive), data size, and data patterns. For example, the above energy consumption metric may represent metrics directly related to energy consumption, such as PUE (power usage effectiveness), EUE (energy usage effectiveness), and specific power consumption measurements.

[0200] For example, if the network node storing (or containing) the trained model (900) is an SMO (250), the output (920) may contain a policy (or target parameter). For example, the policy included in the output (920) of the trained model (900) of the SMO (250) may be used as the target parameter included in the input (910) of the trained model (900) of the CU (210) (and / or DU (220)).

[0201] For example, the compensation (930) can be used for positive functions such as improving resource utilization, reducing waiting time, reducing power consumption, or improving or maintaining network performance. In other words, the compensation (930) can provide negative feedback to the output (920) that causes resource bottlenecks, increased waiting time, or degraded network performance.

[0202] Referring to the above description, each of the CU (210), DU (220), and SMO (250) can store a trained model (900). In the present disclosure, each of the CU (210), DU (220), and SMO (250) can perform dynamic adjustment of functional separation by utilizing the output of the trained model (900). The trained model (900) stored in each of the CU (210), DU (220), and SMO (250) can be federated to optimize the quality of service, power consumption, or performance of the entire network. Specific details regarding federated learning may be referenced below in FIG. 10a and FIG. 10b.

[0203] FIG. 10a illustrates an example of federated learning. FIG. 10b illustrates an example of federated learning between SMOs, CUs, and DUs.

[0204] Referring to FIG. 10a, an example (1000) of federated learning performed by a plurality of network nodes (1001, 1011, 1012, 1013) is illustrated. For example, a network node (1001) may be connected to network nodes (1011, 1012, 1013). By example, without limitation, the network node (1001) may be an SMO (250) (or a server). By example, without limitation, the network nodes (1011, 1012, 1013) may be a CU (210) and / or a DU (220).

[0205] For example, the federated learning described above may represent a technique for generating a trained model (900) by having multiple network nodes (1001, 1011, 1012, 1013) cooperate. For example, a network node (1001) for the federated learning may generate (or train) a model. The model initially generated by the network node (1001) may be referenced as a base model or a reference model. The generated reference model may be transmitted from the server to multiple network nodes (1011, 1012, 1013) connected to the network node (1001). Each of the multiple network nodes (1011, 1012, 1013) may provide functions based on the received reference model. For example, federated learning can enable the training of models across a distributed network environment while addressing privacy issues by utilizing local data (e.g., multiple nodes (1011, 1012, 1013)). More specifically, federated learning can be used in situations where it is difficult to use centralized data or where privacy is important.

[0206] For example, each of the plurality of network nodes (1011, 1012, 1013) may coordinate resources for the functions or perform redistribution of functions (or redistribution of functional separation) while providing the functions. Each of the plurality of network nodes (1011, 1012, 1013) may train the reference model using the data used while providing the functions (e.g., input (910) and output (920) of FIG. 9). For example, network node (1011) may generate (or train) a first trained model (1021). For example, network node (1012) may generate (or train) a second trained model (1022). For example, network node (1013) may generate (or train) a third trained model (1023).

[0207] For example, each of the multiple network nodes (1011, 1012, 1013) can transmit a trained model (or configuration information for the trained model) to the network node (1001). For example, each of the network nodes (1011, 1012, 1013) can transmit a trained model (or configuration information for the trained model) trained so far to the network node (1001).

[0208] For example, a network node (1001) can generate a combined (or synthesized) model (1025) using the received trained models (1021, 1022, 1023). For example, the network node (1001) can transmit information about the model (1025) or information for updating the model (1025) (or update information) to the network nodes (1011, 1012, 1013). Afterward, each of the network nodes (1011, 1012, 1013) can generate a newly trained model from the model (1025) in substantially the same way as generating a trained model (e.g., a first trained model (1021)) from the reference model.

[0209] FIG. 10b illustrates an example where the network node (1001) is an SMO (500) (or, the SMO (250) of FIG. 2), and the network nodes (1011, 1012, 1013) are CUs (510, 520) (or, the CU (210) of FIG. 2) and DUs (531, 532, 540) (or, the DU (220) of FIG. 2).

[0210] For example, the SMO (500) may create (or initialize) a reference model. For example, the SMO (500) may transmit the reference model to each of the CUs (510, 520) and DUs (531, 532, 540). As an example without limitation, each of the CUs (510, 520) and DUs (531, 532, 540) may create (or initialize) the reference model.

[0211] For example, each of the CUs (510, 520) and DUs (531, 532, 540) may acquire (or collect) input data. By example, without limitation, the input data may include resource information and hierarchical KPIs related to currently used functions (or options for functional separation). For example, each of the CUs (510, 520) and DUs (531, 532, 540) may perform training (or updating) of the reference model using the input data. For example, each of the CUs (510, 520) and DUs (531, 532, 540) may acquire (or update) the gradient parameters and parameter parameters of the trained model. For example, the gradient parameters and parameter parameters may be referenced as update information.

[0212] For example, each of the CUs (510, 520) and DUs (531, 532, 540) may transmit update information of the trained model to the SMO (500). For example, the SMO (500) may create a new reference model based on the update information. For example, the SMO (500) may transmit (or distribute) configuration information (or update information) for the new reference model to each of the CUs (510, 520) and DUs (531, 532, 540). For example, when creating the new reference model, the SMO (500) may update the policy using the trained model stored within the SMO (500) and transmit the updated policy to each of the CUs (510, 520) and DUs (531, 532, 540).

[0213] By repeatedly performing the operations as described above, resources used in the CNF to provide functions within the SMO (500), CUs (510, 520), and DUs (531, 532, 540) can be optimized, and service quality (or power consumption, performance) can be optimized.

[0214] Referring to FIG. 10b, for example, the first CU (510) and the first DU (531) may utilize a functional separation of the first option (1031) (e.g., Option 2 (692) in FIG. 6). For example, in the first option (1031), the first functions (1031-1) may be performed in the first CU (510), and the second functions (1031-2) may be performed in the first DU (531). For example, the first CU (510) and the second DU (532) may utilize a functional separation of the second option (1032) (e.g., Option 6 (696) in FIG. 6). For example, in the second option (1032), the first functions (1032-1) may be performed in the first CU (510), and the second functions (1032-2) may be performed in the second DU (532). For example, the second CU (520) and the third DU (540) may utilize the functional separation of the third option (1040) (e.g., Option 6 (696) of FIG. 6). For example, in the third option (1040), the first functions (1040-1) may be performed in the second CU (520), and the second functions (1040-2) may be performed in the third DU (540). By example, without limitation, the third option (1040) may be identical to the second option (1032). In other words, because the characteristics of the network environment (e.g., load) of the first CU (510) and the second DU (532) are similar to the characteristics of the network environment (e.g., load) of the second CU (520) and the third DU (540), the same functional separation can be utilized in the CUs (510, 520) and DUs (532, 540) located in different regions. In one example, the SMO (500) may propose (or recommend) a third option (1040), which is the functional separation used in the second CU (520) and the third DU (540), to the first CU (510) which has identified that a change in functional separation is necessary. As an example without limitation, the SMO (500) may send a policy to the first CU (510) that causes the use of the third option (1040).The first CU (510) that receives the above policy can generate configuration information for the second option (1032) according to the third option (1040) and transmit the configuration information to the second DU (532).

[0215] FIG. 11 illustrates an example of an operation flow for how an upper network node coordinates the functions of an upper network node and a lower network node.

[0216] At least some of the above methods of FIG. 11 may be performed by a communication device. For example, the communication device may be an example of the electronic device (400) of FIG. 4. For example, the communication device may be configured to perform the function of the upper network node (e.g., the CU (210) of FIG. 2 or the upper network node (310) of FIG. 3) as an example of the electronic device (400). For example, at least some of the above methods may be controlled by a processor of the communication device (e.g., the processor (430) of FIG. 4). In the following embodiments, each operation may be performed sequentially, but is not necessarily performed sequentially. For example, the order of each operation may be changed, and at least two operations may be performed in parallel.

[0217] In operation (1110), the communication device may obtain first resource information used in each of the first functions of the upper network node. For example, the first resource information may represent resources being used according to the current function of the upper network node. For example, the first resource information may be information related to the load of the upper network node.

[0218] As an example without limitation, the upper network node may acquire a key performance indicator (KPI) along with the first resource information. For example, the KPI may represent a KPI related to the load of the upper network node. For example, the KPI may be defined by the hierarchy of the upper network node. For example, the hierarchy of the upper network node may represent the hierarchy of the functions (e.g., the first functions) of the upper network node.

[0219] In operation (1120), the communication device may generate second resource information to be used in each of the first functions by providing the first resource information to a trained model stored in the upper network node. For example, the communication device may provide the first resource information to a trained model (e.g., the trained model (900) of FIG. 9) stored in the upper network node. For example, the communication device may generate the second resource information. For example, the second resource information may be used for each of the functions (e.g., the first functions) of the upper network node. In other words, the second resource information may indicate the number of cores, memory usage, or operating frequency of each core, which are resource information for each of the first functions, just like the first resource information, but are different from the first resource information. Or, for example, the second resource information may include a change in the number of cores from the first resource information.

[0220] In the above example, the second resource information is exemplified as being generated using the first resource information, but the present disclosure is not limited thereto. For example, KPIs and / or reference resource information may be further utilized together with the first resource information. For example, when the KPI is further provided to a trained model stored within the upper network node, the second resource information may be generated to satisfy the service quality (or performance) according to the KPI. Or, for example, when the reference resource information is further provided to a trained model stored within the upper network node, the second resource information may be generated to satisfy the reference resource information.

[0221] In operation (1130), the communication device can identify the quality of service provided through the first functions configured based on the second resource information. For example, the communication device can configure the first functions based on the second resource information. For example, the communication device can identify whether the quality of service provided through the first functions configured based on the second resource information is below a standard quality. For example, if the quality of service is below the standard quality, the communication device can perform operation (1140) and operation (1150).

[0222] Operations substantially identical to operations (1110), (1120), and (1130) may be performed at a sub-network node connected to the upper network node (e.g., DU (220) in FIG. 2 or sub-network node (320) in FIG. 3). For example, the sub-network node may acquire resource information used in each of the functions of the sub-network node. For example, the sub-network node may generate other resource information to be used in each of the functions of the sub-network node by providing the acquired resource information to a trained model stored within the sub-network node. For example, the sub-network node may identify the quality of service provided through the functions configured based on the other resource information. For example, the sub-network node may identify whether the quality of service is below a different standard quality. For example, the other standard quality used in the sub-network node may be the same as or different from the standard quality used in the upper network node. For example, if the service quality is below the other standard quality, the lower network node may request the redistribution of the functions of the upper network node and the lower network node. For example, the lower network node may transmit the request to the upper network node (or the communication device).

[0223] In operation (1140), the communication device can identify second functions modified from the first functions among the functions of the upper network node and the lower network node (e.g., DU (220) in FIG. 2 or lower network node (320) in FIG. 3) connected to the upper network node and the functions of the upper network node. For example, the communication device can identify second functions modified from the first functions among the functions of the upper network node and the lower network node among the entire functions of the upper network node and the lower network node, based on identifying that the service quality provided through the first functions configured based on the second resource information is below the reference quality. For example, the communication device can identify fourth functions modified from third functions among the entire functions of the upper network node and the lower network node, based on identifying that the service quality provided through the first functions configured based on the second resource information is below the reference quality. For example, the above third functions are functions used as the functions of the above sub-network node, and may be distinguished (or different, separated) from the above first functions among the entire set of functions. For example, the above fourth functions are functions to be used as the functions of the above sub-network node, and may be distinguished (or different, separated) from the above second functions among the entire set of functions.

[0224] As an example not limited to, when the communication device receives the request from the sub-network node, it may perform the redistribution of the entire functions as described above.

[0225] For example, the communication device may allocate (or distribute) resources for each of the second functions. For example, each of the second functions may be configured by a CNF. For example, resources for each of the second functions may include the number of cores used for the CNF, memory usage, and the operating frequency of each core.

[0226] In operation (1150), the communication device may transmit configuration information to the sub-network node that instructs the sub-network node to change the function of the sub-network node. For example, the communication device may generate configuration information that instructs the sub-network node to configure the fourth functions. For example, the communication device may transmit the configuration information to the sub-network node (or another communication device configured to perform the function of the sub-network node). For example, the sub-network node may allocate (or distribute) resources for each of the fourth functions based on the received configuration information. For example, each of the fourth functions may be configured by CNF. For example, resources for each of the fourth functions may include the number of cores used for CNF, memory usage, and the operating frequency of each core.

[0227] In the example of FIG. 11, the case where the upper network node and the lower network node are separated (or implemented in separated devices) is illustrated, but the present disclosure is not limited thereto. For example, a base station may be configured to perform the functions of the upper network node and the functions of the lower network node. For example, the upper network node (and the functions of the upper network node) and the lower network node (and the functions of the lower network node) may be functionally separated within the base station, but may also be implemented within the base station as a single entity.

[0228] For example, the base station may identify first functions and second functions distinguished from the first functions among the functions of the base station. For example, the first functions may be the functions of the upper network node of the base station. For example, the second functions may be the functions of the lower network node of the base station.

[0229] For example, the base station may acquire first resource information used in each of the first functions and second resource information used in each of the second functions. For example, the base station may generate third resource information to be used in each of the first functions by providing the first resource information to a first trained model stored within the base station. For example, the first trained model may be a model trained to optimize the function of the upper network node. For example, the base station may generate fourth resource information to be used in each of the second functions by providing the second resource information to a second trained model stored within the base station. For example, the second trained model may be a model trained to optimize the function of the lower network node.

[0230] For example, the base station can identify the quality of service provided through the first functions configured based on the third resource information and the second functions configured based on the fourth resource information. For example, the base station can change the first functions to the third functions among the functions of the base station based on the quality of service that is below the reference quality. For example, the base station can change the second functions to the fourth functions among the functions of the base station that are distinguished from the third functions based on the quality of service that is below the reference quality. For example, the base station can reconfigure the functions of the base station by performing a change from the first functions to the third functions and a change from the second functions to the fourth functions.

[0231] The effects obtainable from the present disclosure are not limited to those mentioned above, and other unmentioned effects will be clearly understood by those skilled in the art to which the present disclosure belongs from the description below.

[0232] A communication device configured to perform the functions of an upper network node as described above may include a memory for storing instructions. The communication device may include at least one processor. When the instructions are executed individually or collectively by the at least one processor, the communication device may cause to acquire first resource information used in each of the first functions of the functions of the upper network node. When the instructions are executed individually or collectively by the at least one processor, the communication device may cause to generate second resource information to be used in each of the first functions by providing the first resource information to a trained model stored within the upper network node. When the instructions are executed individually or collectively by the at least one processor, the communication device may cause to identify the quality of service provided through the first functions configured based on the second resource information. When the above instructions are executed individually or collectively by the at least one processor, the communication device may cause the device to identify second functions that have been changed from the first functions to the functions of the upper network node among the functions of the upper network node and the lower network node connected to the upper network node, based on the service quality below the reference quality, and to transmit configuration information to the lower network node that instructs the change of the functions of the lower network node.

[0233] According to one embodiment, when the instructions are executed individually or collectively by the at least one processor, the communication device may cause the communication device to perform a reconfiguration of the functions of the upper network node by changing the first functions to the second functions. When the instructions are executed individually or collectively by the at least one processor, the communication device may cause the communication device to generate the configuration information instructing the change of the functions of the lower network node by identifying the second functions as the functions of the upper network node among the functions of the lower network node and the upper network node.

[0234] According to one embodiment, when the instructions are executed individually or collectively by the at least one processor, the communication device may cause the communication device to receive third resource information used in each of the third functions of the sub-network node from the sub-network node. When the instructions are executed individually or collectively by the at least one processor, the communication device may cause the configuration information to be generated based on the second resource information and the third resource information. The third resource information may be generated from a trained model stored within the sub-network node. The configuration information may cause the sub-network node to identify fourth functions modified from the third functions by the function of the sub-network node.

[0235] According to one embodiment, when the instructions are executed individually or collectively by the at least one processor, the communication device may cause the communication device to receive a request from the lower network node for the distribution of the functions of the lower network node and the upper network node. When the instructions are executed individually or collectively by the at least one processor, the communication device may cause the communication device to identify whether the service quality is below the reference quality in response to the request. The request may be generated when another service quality provided through the third functions configured based on the third resource information is below another reference quality.

[0236] According to one embodiment, the functions of the lower network node and the upper network node may include functions of the physical layer, functions of the media access control (MAC) layer, functions of the radio link control (RLC) layer, functions of the packet data convergence protocol (PDCP) layer, and functions of the radio resource control (RRC) layer. The functions of the upper network node may include at least some of the functions of the physical layer, the functions of the MAC layer, the functions of the RLC layer, the functions of the PDCP layer, and the functions of the RRC layer. The functions of the lower network node may include the remaining portions of the functions of the physical layer, the functions of the MAC layer, the functions of the RLC layer, the functions of the PDCP layer, and the functions of the RRC layer, excluding at least some of the above.

[0237] According to one embodiment, each of the first functions of the upper network node may be defined as a containerized network function (CNF). Each of the second functions of the upper network node may be defined as a CNF. The CNF may include at least some of the functions of one of the physical layer, the MAC layer, the RLC layer, the PDCP layer, and the RRC layer.

[0238] According to one embodiment, among the functions of the lower network node and the upper network node, the first functions of the upper network node and the third functions of the lower network node distinguished from the first functions may be defined as a first option of functional split. Among the functions of the lower network node and the upper network node, the second functions of the upper network node and the fourth functions of the lower network node distinguished from the second functions may be defined as a second option different from the first option of functional split.

[0239] According to one embodiment, each of the first resource information and the second resource information may include at least one of the number of cores of each of the first functions of the upper network node, memory usage for each of the first functions, or the operating frequency of each of the cores.

[0240] According to one embodiment, when the instructions are executed individually or collectively by the at least one processor, the communication device may cause the communication device to acquire a key performance indicator (KPI) associated with each of the first functions of the upper network node. When the instructions are executed individually or collectively by the at least one processor, the communication device may cause the second resource information to be generated by further providing the KPI to the trained model. The KPI is defined by layer and may include at least one of throughput, packet loss rate, buffer occupancy, retransmission rate, or latency.

[0241] According to one embodiment, when the instructions are executed individually or collectively by the at least one processor, the communication device may cause the communication device to receive reference resource information regarding the resource information of the function of the upper network node from the SMO (service management and orchestration) node. When the instructions are executed individually or collectively by the at least one processor, the communication device may cause the second resource information to be generated by further providing the reference resource information to the trained model. The reference resource information may include the maximum and minimum number of cores used for each of the first functions. The reference resource information may be generated from the trained model stored within the SMO node.

[0242] According to one embodiment, the upper network node may include a central unit (CU). The lower network node may include a distributed unit (DU).

[0243] According to one embodiment, the upper network node may be connected to the lower network node and another lower network node. The functional split between the upper network node and the lower network node may be defined as a first option. The functional split between the upper network node and the other lower network node may be defined as a second option different from the first option.

[0244] According to one embodiment, when the instructions are executed individually or collectively by the at least one processor, the communication device may cause the service management and orchestration (SMO) node to transmit configuration information regarding the trained model of the upper network node. When the instructions are executed individually or collectively by the at least one processor, the communication device may cause the communication device to receive update information for the trained model from the SMO node. When the instructions are executed individually or collectively by the at least one processor, the communication device may cause the communication device to perform an update of the trained model based on the update information. The update information may include the configuration information regarding the trained model and the result of federated learning using the configuration information regarding the trained model stored within the lower network node.

[0245] A method performed in a communication device configured to perform the functions of an upper network node as described above may include an operation of acquiring first resource information used in each of the first functions of the upper network node. The method may include an operation of generating second resource information to be used in each of the first functions by providing the first resource information to a trained model stored in the upper network node. The method may include an operation of identifying the quality of service provided through the first functions configured based on the second resource information. The method may include an operation of identifying second functions modified from the first functions by the upper network node's functions among the lower network node connected to the upper network node and the functions of the upper network node based on the quality of service that is less than a reference quality, and an operation of transmitting to the lower network node configuration information instructing the modification of the lower network node's functions.

[0246] A non-transient computer-readable storage medium as described above may store one or more programs including instructions that cause the communication device to acquire first resource information used in each of the first functions of said function of said function when executed individually or collectively by at least one processor of a communication device configured to perform the function of said upper network node. The non-transient computer-readable storage medium may store one or more programs including instructions that cause the communication device to generate second resource information to be used in each of said first functions by providing said first resource information to a trained model stored in said upper network node when executed individually or collectively by said at least one processor. The non-transient computer-readable storage medium may store one or more programs including instructions that cause the communication device to identify the quality of service provided through said first functions configured based on said second resource information when executed individually or collectively by said at least one processor. The above non-transient computer-readable storage medium may store one or more programs including instructions that, when executed individually or collectively by the at least one processor, cause the communication device to identify second functions that have been changed from the first functions to the functions of the upper network node among the functions of the upper network node and the lower network node connected to the upper network node based on the service quality below the reference quality, and to transmit configuration information to the lower network node that directs the change of the functions of the lower network node.

[0247] As described above, the base station may include a memory for storing instructions. The base station may include at least one processor. When the instructions are executed individually or collectively by the at least one processor, the base station may cause the base station to identify first functions and second functions distinguished from the first functions among the functions of the base station. When the instructions are executed individually or collectively by the at least one processor, the base station may cause the base station to acquire first resource information used in each of the first functions and second resource information used in each of the second functions. When the instructions are executed individually or collectively by the at least one processor, the base station may cause the base station to generate third resource information to be used in each of the first functions by providing the first resource information to a first trained model stored within the base station. When the above instructions are executed individually or collectively by the at least one processor, the base station may generate fourth resource information to be used in each of the second functions by providing the second resource information to a second trained model stored within the base station. When the above instructions are executed individually or collectively by the at least one processor, the base station may identify the quality of service provided through the first functions configured based on the third resource information and the second functions configured based on the fourth resource information.When the above instructions are executed individually or collectively by the at least one processor, the base station may cause the base station to perform a reconfiguration of the base station’s functions by changing the first functions to third functions among the base station’s functions and changing the second functions to fourth functions among the base station’s functions that are distinct from the third functions, based on the service quality being below a reference quality.

[0248] According to one embodiment, the functions of the base station may include functions of the physical layer, functions of the media access control (MAC) layer, functions of the radio link control (RLC) layer, functions of the packet data convergence protocol (PDCP) layer, and functions of the radio resource control (RRC) layer. The third functions may include at least some of the functions of the physical layer, the functions of the MAC layer, the functions of the RLC layer, the functions of the PDCP layer, and the functions of the RRC layer. The fourth functions may include the remaining portion of the functions of the physical layer, the functions of the MAC layer, the functions of the RLC layer, the functions of the PDCP layer, and the functions of the RRC layer, excluding at least some of the above.

[0249] According to one embodiment, each of the third functions may be defined as a containerized network function (CNF). Each of the fourth functions may be defined as a CNF. The CNF may include the functions of at least some of the layers among the physical layer, the MAC layer, the RLC layer, the PDCP layer, and the RRC layer.

[0250] According to one embodiment, the first functions and the second functions may be defined as a first option of a functional split. The third functions and the fourth functions may be defined as a second option of the functional split that is different from the first option.

[0251] According to one embodiment, each of the first resource information and the third resource information may include at least one of the number of cores for each of the first functions, memory usage for each of the first functions, or operating frequency of each of the cores. Each of the second resource information and the fourth resource information may include at least one of the number of cores for each of the second functions, memory usage for each of the second functions, or operating frequency of each of the cores.

[0252] The technical problems to be solved in this disclosure are not limited to those mentioned above, and other technical problems not mentioned will be clearly understood by those skilled in the art to which this disclosure pertains.

[0253] Methods according to the claims or embodiments described in the specification of the present disclosure may be implemented in the form of hardware, software, or a combination of hardware and software.

[0254] When implemented in software, a computer-readable storage medium may be provided for storing one or more programs (software modules). One or more programs stored in the computer-readable storage medium are configured for execution by one or more processors within an electronic device. One or more programs include instructions that cause the electronic device to execute methods according to the claims or embodiments described in the specification of this 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 product. The computer program product may be distributed in the form of a device-readable storage medium (e.g., compact disc read-only memory (CD-ROM)), or distributed online (e.g., download or upload) through an application store (e.g., Play Store™) or directly between two user devices (e.g., smartphones). In the case of online distribution, at least a portion of the computer program product may be temporarily stored or temporarily created on a device-readable storage medium, such as the memory of a manufacturer's server, an application store's server, or a relay server.

[0255] Such 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. Alternatively, they may be stored in memory composed of some or all of these. Additionally, each constituent memory may include multiple units.

[0256] Additionally, the program may be stored on an attachable storage device that can be accessed via a communication network such as the Internet, Intranet, LAN (local area network), WAN (wide area network), or SAN (storage area network), or a combination thereof. Such a storage device may be connected to a device performing an embodiment of the present disclosure through an external port. Additionally, a separate storage device on a communication network may be connected to a device performing an embodiment of the present disclosure.

[0257] In the specific embodiments of the present disclosure described above, the components included in the disclosure are expressed in a singular or plural form according to the specific embodiments presented. However, the singular or plural expression is selected to suit the situation presented for convenience of explanation, and the present disclosure is not limited to singular or plural components; even if a component is expressed in the plural form, it may be composed of a singular form, and even if a component is expressed in the singular form, it may be composed of a plural form.

[0258] According to the embodiments, one or more of the aforementioned components or operations may be omitted, or one or more other components or operations may be added. Generally or additionally, a plurality of components (e.g., a module or a program) may be integrated into a single component. In this case, the integrated component may perform one or more functions of each of the plurality of components in the same or similar manner as those performed by the corresponding component among the plurality of components prior to the integration. According to the embodiments, 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.

[0259] Meanwhile, although specific embodiments have been described in the detailed description of the present disclosure, it is understood that various modifications are possible within the scope of the present disclosure.

Claims

1. In a communication device configured to perform the functions of an upper network node, Memory for storing instructions; and It includes at least one processor, When the above instructions are executed individually or collectively by the at least one processor, the communication device: Acquiring first resource information used in each of the first functions of the above-mentioned upper network node; By providing the above-mentioned first resource information to a trained model stored within the above-mentioned upper network node, second resource information to be used in each of the above-mentioned first functions is generated; Identifying the service quality provided through the first functions configured based on the second resource information; Based on the above service quality being below the standard quality: Identifying second functions modified from the first functions among the functions of the upper network node and the lower network node connected to the upper network node and the upper network node, and the functions of the upper network node; and Causing the transmission to the sub-network node configuration information that instructs a change in the function of the sub-network node, Communication device.

2. In Claim 1, When the above instructions are executed individually or collectively by the at least one processor, the communication device: By changing the above first functions to the above second functions, reconfiguration of the above functions of the upper network node is performed; and By identifying the second functions as the functions of the upper network node among the functions of the lower network node and the upper network node, thereby causing the generation of the configuration information that directs the change of the functions of the lower network node, Communication device.

3. In Claim 2, When the above instructions are executed individually or collectively by the at least one processor, the communication device: Receiving third resource information used in each of the third functions of the sub-network node from the above sub-network node; and Causing to generate the setting information based on the above second resource information and the above third resource information, and The above third resource information is generated from a trained model stored within the above sub-network node, and The above configuration information causes the sub-network node to identify the fourth functions modified from the third functions by the above function of the sub-network node, Communication device.

4. In Claim 3, When the above instructions are executed individually or collectively by the at least one processor, the communication device: Receiving a request from the lower network node for the distribution of the functions of the lower network node and the upper network node; and In response to the above request, cause to identify whether the service quality is below the standard quality, and The above request is generated when the quality of other services provided through the third functions configured based on the third resource information is lower than a different standard quality, Communication device.

5. In Claim 1, The functions of the above-mentioned lower network node and the above-mentioned upper network node include the functions of the physical layer, the functions of the MAC (media access control) layer, the functions of the RLC (radio link control) layer, the functions of the PDCP (packet data convergence protocol) layer, and the functions of the RRC (radio resource control) layer, and The above function of the upper network node includes at least some of the functions of the physical layer, the MAC layer, the RLC layer, the PDCP layer, and the RRC layer, and The above function of the above sub-network node includes the remaining parts excluding at least some of the functions of the physical layer, the MAC layer, the RLC layer, the PDCP layer, and the RRC layer. Communication device.

6. In Claim 5, Each of the first functions of the above-mentioned upper network node is defined as a CNF (containerized network function), and Each of the second functions of the above-mentioned upper network node is defined as CNF, and The above CNF includes the functions of at least some of the layers among the physical layer, the MAC layer, the RLC layer, the PDCP layer, and the RRC layer. Communication device.

7. In Claim 5, Among the functions of the above-mentioned lower network node and the above-mentioned upper network node, the above-mentioned first functions of the above-mentioned upper network node and the above-mentioned third functions of the above-mentioned lower network node distinguished from the above-mentioned first functions are defined as a first option of functional split, and Among the functions of the above-mentioned lower network node and the above-mentioned upper network node, the above-mentioned second functions of the above-mentioned upper network node and the fourth functions of the above-mentioned lower network node distinguished from the above-mentioned second functions are defined as a second option different from the above-mentioned first option of the functional separation, Communication device.

8. In Claim 1, Each of the above first resource information and the above second resource information comprises at least one of the number of cores of each of the first functions of the upper network node, memory usage for each of the first functions, or the operating frequency of each of the cores. Communication device.

9. In Claim 1, When the above instructions are executed individually or collectively by the at least one processor, the communication device: Acquiring key performance indicators (KPIs) associated with each of the first functions of the upper network node; and By providing the above KPI further to the above-mentioned trained model, it causes the generation of the above-mentioned second resource information, and The above KPI is defined by layer and includes at least one of throughput, packet loss rate, buffer occupancy, retransmission rate, or latency. Communication device.

10. In Claim 1, When the above instructions are executed individually or collectively by the at least one processor, the communication device: Receiving reference resource information regarding the resource information of the function of the upper network node from the SMO (service management and orchestration) node; and By further providing the above reference resource information to the above-mentioned trained model, it causes the generation of the above-mentioned second resource information, and The above reference resource information includes a maximum number and a minimum number of cores used for each of the above first functions, and The above reference resource information is generated from a trained model stored within the above SMO node, Communication device.

11. In Claim 1, The above-mentioned upper network node includes a CU (central unit), and The above-mentioned sub-network node includes a DU (distributed unit), Communication device.

12. In Claim 11, The above upper network node is connected to the above lower network node and other lower network nodes, and The functional split between the upper network node and the lower network node is defined as a first option (potion), and The functional separation between the upper network node and the other lower network node is defined as a second option different from the first option, Communication device.

13. In Claim 1, When the above instructions are executed individually or collectively by the at least one processor, the communication device: Transmit configuration information for the trained model of the upper network node to the SMO (service management and orchestration) node; Receive update information for the trained model from the above SMO node; and Causing to perform an update of the trained model based on the above update information, The above update information includes the result of federated learning using the configuration information for the above-mentioned trained model and the configuration information for the above-mentioned trained model stored within the above-mentioned sub-network node. Communication device.

14. A method performed in a communication device configured to perform the functions of an upper network node, An operation to acquire first resource information used in each of the first functions of the above-mentioned upper network node; An operation to generate second resource information to be used in each of the first functions by providing the first resource information to a trained model stored in the upper network node; An operation to identify the service quality provided through the first functions configured based on the second resource information; Based on the above service quality being below the standard quality: An operation to identify second functions modified from the first functions among the functions of the upper network node and the lower network node connected to the upper network node and the upper network node, with the functions of the upper network node; and A method comprising the operation of transmitting to the sub-network node configuration information that instructs a change in the function of the sub-network node, the sub-network node. method.

15. In a non-transient computer-readable storage medium, when executed individually or collectively by at least one processor of a communication device configured to perform the function of an upper network node, said communication device: Acquiring first resource information used in each of the first functions of the above-mentioned upper network node; By providing the above-mentioned first resource information to a trained model stored within the above-mentioned upper network node, second resource information to be used in each of the above-mentioned first functions is generated; Identifying the service quality provided through the first functions configured based on the second resource information; Based on the above service quality being below the standard quality: Identifying second functions modified from the first functions among the functions of the upper network node and the lower network node connected to the upper network node and the upper network node, and the functions of the upper network node; and Storing one or more programs including instructions that cause the lower network node to transmit configuration information directing a change in the function of the lower network node. Non-transient computer-readable storage media.

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