Apparatus and method for radio access network node optimization in a wireless communication system

Abstract

This disclosure relates to fifth-generation (5G) or sixth-generation (6G) communication systems for supporting higher data transmission rates. A method and system for optimizing radio access network (RAN) nodes in a wireless network are provided. The method includes measuring downstream throughput at the interface between a base station (BS) and a user plane function (UPF) entity, and measuring upstream throughput at the interface between the BS and the UPF entity, wherein the measurement includes at least one of measuring the number of octets of General Packet Radio Service (GPRS) Tunneling Protocol (GTP) data packets incoming from the user plane function to the RAN's N3 / NgU interface, and measuring the number of octets of GTP data packets outgoing from the RAN to the UPF's N3 / NgU interface.

Description

Technical Field

[0001] This disclosure relates to wireless communications. More specifically, this disclosure relates to the optimization of radio access network (RAN) nodes in wireless networks. Background Technology

[0002] Fifth-generation (5G) mobile communication technology defines a wide frequency band, enabling high transmission rates and new services. It can be implemented not only in "sub-6GHz" bands such as 3.5GHz, but also in "above-6GHz" bands, including 28GHz and 39GHz, known as millimeter waves (mmWave). Furthermore, sixth-generation (6G) mobile communication technology (called "super 5G systems") is being considered in terahertz (THz) bands (e.g., the 95GHz to 3THz band) to achieve transmission rates fifty times faster than 5G and ultra-low latency one-tenth that of 5G.

[0003] At the outset of 5G mobile communication technology development, standardization was underway for the following technologies to support services and meet performance requirements associated with enhanced mobile broadband (eMBB), ultra-reliable low-latency communication (URLLC), and massive machine-type communication (mMTC): beamforming and massive multiple-input multiple-output (MIMO) for mitigating radio wave path loss and increasing radio wave transmission distance in millimeter waves; dynamic operation supporting parameter sets (e.g., operating multiple subcarrier spacings) and time slot formats for efficient utilization of millimeter wave resources; initial access technologies supporting multi-beam transmission and broadband; definition and operation of bandwidth portions (BWP); new channel coding methods (such as low-density parity-check (LDPC) codes for large data transmissions and polar codes for highly reliable transmission of control information); layer 2 (L2) preprocessing; and network slicing for providing dedicated networks for specific services.

[0004] Currently, given the services that 5G mobile communication technology needs to support, discussions are underway regarding improvements and performance enhancements to the initial 5G mobile communication technology, and physical layer standardization already exists for technologies such as: Vehicle-to-Everything (V2X) for assisting autonomous vehicles in determining driving based on information about the location and status of vehicles transmitted by vehicles and for enhancing user convenience; New Radio Unlicensed (NR-U) designed to make system operation in unlicensed bands comply with various regulatory requirements; New Radio (NR) User Equipment (UE) power saving; Non-Terrestrial Network (NTN) for UE-satellite direct communication to provide coverage in areas where communication with terrestrial networks is unavailable; and positioning.

[0005] Furthermore, standardization is underway in the wireless interface architecture / protocol domain for technologies such as: Industrial Internet of Things (IIoT) to support new services through interoperability and convergence with other industries; Integrated Access and Backhaul (IAB) for nodes to provide network service area extension by supporting wireless backhaul and access links in an integrated manner; mobility enhancements including conditional handover and Dual Active Protocol Stack (DAPS) handover; and two-step random access (RACH for NR) to simplify the random access process. In terms of system architecture / services, standardization is also underway for: 5G baseline architectures (e.g., service-based architectures or service-based interfaces) for combining Network Functions Virtualization (NFV) and Software-Defined Networking (SDN) technologies; and Mobile Edge Computing (MEC) for UE location-based reception services.

[0006] With the commercialization of 5G mobile communication systems, the number of connected devices will increase exponentially, necessitating enhanced functionality and performance of 5G mobile communication systems and integrated operation of connected devices. To this end, new research is planned related to: Extended Reality (XR) for effectively supporting Augmented Reality (AR), Virtual Reality (VR), Mixed Reality (MR), etc.; improving 5G performance and reducing 5G complexity by leveraging Artificial Intelligence (AI) and Machine Learning (ML); AI service support; Metaverse service support; and drone communication.

[0007] Furthermore, this development of 5G mobile communication systems will serve as a foundation for: not only developing new waveforms for providing terahertz band coverage for 6G mobile communication technologies, multi-antenna transmission technologies (such as full-dimensional MIMO (FD-MIMO), array antennas, and massive MIMO), metamaterial-based lenses and antennas for improving terahertz band signal coverage, high-dimensional spatial multiplexing technologies using orbital angular momentum (OAM), and reconfigurable smart surfaces (RIS), but also developing full-duplex technologies to improve the frequency efficiency of 6G mobile communication technologies and enhance system networks, AI-based communication technologies to achieve system optimization by leveraging satellites and artificial intelligence (AI) from the design phase and internalizing end-to-end AI support capabilities, and next-generation distributed computing technologies to achieve services at a complexity level exceeding the operational capabilities of UEs by utilizing ultra-high-performance communication and computing resources. Summary of the Invention

[0008] [Solution to the problem]

[0009] The fifth-generation (5G) communication system, comprising the 5G access network (AN), 5G core network, and user equipment (UE) as defined in TS 23501, is designed to optimize support for a wide range of communication services, workloads, and user communities. Notably, the system is designed to meet the stringent requirements of vehicle-to-everything (V2X) services, which demand high data rates, reliability, low latency, and high speed. Furthermore, the system facilitates enhanced mobile broadband (eMBB) through fixed-mobile convergence (FMC) and supports network slicing to meet diverse service needs. The 5G system is also designed to handle massive IoT connectivity, such as those found in smart homes and smart grids, which require support for numerous high-density IoT devices.

[0010] Network slicing is a key feature within the 5G framework, allowing operators to create dedicated logical networks over shared infrastructure. This approach enables customized functionality tailored to specific customer needs, moving away from the traditional one-size-fits-all approach. Network slices can be dynamically allocated for specific purposes, with some slices being temporary (e.g., providing eMBB services to broadcasters for live events) and others being long-term (e.g., providing eMBB services to hospitals).

[0011] Despite progress, several challenges remain in effectively managing and monitoring network slices, particularly from a radio access network (RAN) perspective. Current standards, such as 3GPP TS 28530, 28531, and GSMA NG116 (released April 9, 2023), provide definitions and key performance indicators (KPIs) for downstream and upstream throughput for each network slice. However, these KPIs primarily focus on core network subnet slices and rely on measurements taken at the User Plane Function (UPF), the endpoint of the N3 / NgU interface.

[0012] A significant issue is the lack of standardized mechanisms to measure or monitor throughput on the N3 / NgU interfaces of RAN slices or slice subnets across RAN aggregation points (such as gNodeB (gNB) Central Unit-User Plane (CU-UP)). Consequently, the RAN Network Slice Subnet Management Function (NSSMF) cannot determine the throughput on the N3 / NgU interfaces provided by the gNB and CU-UP for a specific slice. Furthermore, consumer NSMFs within the core network slice subnet may require information about the N3 / NgU throughput at the RAN end (i.e., at the gNB / CU-UP). However, since there is no standardized measurement at the RAN end of the N3 / NgU interface, there is no standardized method to relay this information back to core network slice subnet consumers.

[0013] Furthermore, the throughput at the UPF end of the N3 / NgU interface can differ from the throughput at the gNB / CU-UP end of the N3 / NgU interface. For effective gNB / CU-UP capacity planning, calculating performance metrics at the gNB / CU-UP end of the N3 / NgU interface is crucial. Therefore, it is imperative to define these metrics to ensure accurate and effective network performance monitoring and management.

[0014] The above information is presented as background information only to aid in understanding this disclosure. No determination or assertion is made regarding whether any of the above content can be used as prior art with respect to this disclosure.

[0015] The aspects of this disclosure will at least address the aforementioned problems and / or disadvantages, and provide at least the following advantages. Therefore, one aspect of this disclosure is to provide a useful alternative to overcome the inter-device connection establishment and synchronization problems inherent in the current 5G network slicing management framework.

[0016] Another aspect of this disclosure is to provide a system and method for RAN node optimization in a wireless network. In the proposed solution, the gNB configuration and CU-UP configuration at the RAN node are optimized based on performance metrics collected at the gNB and / or CU-UP ends of the next-generation user plane (N3 / NgU) interface of the RAN node.

[0017] Another aspect of this disclosure is providing measurements at the gNB / CU-UP end to obtain the number of octets of General Packet Radio Service (GPRS) Tunneling Protocol (GTP) data packets transmitted over the N3 / NgU interface of the network and slice.

[0018] Another aspect of this disclosure is providing measurements at the gNB / CU-UP end to obtain the number of octets of GTP data packets incoming over the N3 / NgU interface of the network and slice.

[0019] Another aspect of this disclosure is defining new KPIs to measure the downstream and upstream throughput of network slices on the N3 / NgU interfaces at the gNB and / or CU-UP ends. Measuring throughput will enable the RAN NSSMF to be informed of the throughput on the N3 / NgU interfaces provided by the gNB and CU-UP for a specific slice.

[0020] Another aspect of this disclosure is defining new KPIs to measure the downstream and upstream throughput of network slices on the N3 / NgU interfaces at the gNB and / or CU-UP ends. Measuring throughput will enable the core network NSMF to be informed of the throughput on the N3 / NgU interfaces provided by the gNB and CU-UP for a specific slice.

[0021] Other aspects will be set forth in part in the description which follows, and in part will be apparent from the description, or may be learned by practice of the embodiments presented.

[0022] According to one aspect of this disclosure, a method for optimizing a radio access network (RAN) node in a wireless network is provided. The method includes: a producer device receiving a creation management object instance (MOI) request from a consumer device for collecting performance metrics, wherein the MOI request includes multiple attributes for collecting performance metrics, wherein the performance metrics include at least one of a measurement and a KPI collected at at least one of the gNB and CU-UP ends of the producer device's N3 / NgU interface; the producer device performing the collection of performance metrics at at least one of the gNB and CU-UP ends of the producer device's N3 / NgU interface based on the multiple attributes received in the MOI request, wherein the measurements include the N3 / NgU interface from the user plane function (UPF) to the RAN. The KPIs include at least one of the following: a measurement of the number of octets of incoming General Packet Radio Service (GPRS) Tunneling Protocol (GTP) data packets and a measurement of the number of octets of outgoing GTP data packets on the N3 / NgU interface from the RAN to the UPF; wherein the collected KPIs include at least one of the downstream throughput and upstream throughput of the network slice at at least one of the gNB and CU-UP ends of the N3 / NgU interface, and the performance metrics collected are sent by the producer equipment to the consumer equipment to optimize at least one of the gNB configuration and CU-UP configuration at the producer equipment based on the performance metrics received from the producer equipment.

[0023] In embodiments of this disclosure, the producer equipment determines whether measurement conditions are met, wherein the measurement conditions include the gNB and CU-UP receiving and / or transmitting GTP-U data protocol data units (PDUs) on the N3 / NgU interface. When the conditions are met, the producer equipment performs the measurement at the CU-UP end of the gNB and N3 / NgU interface.

[0024] In embodiments of this disclosure, the producer device divides measurements into sub-counters based on single network slice selection auxiliary information (S-NSSAI), wherein each measurement is a single integer value, and wherein the number of measurements is equal to the number of supported S-NSSAIs when an optional S-NSSAI sub-counter measurement is performed.

[0025] In embodiments of this disclosure, the measurement includes a measurement name representing the number of octets of incoming and outgoing GTP data packets on the N3 / NgU interface at the gNB and CU-UP ends, wherein the incoming and outgoing GTP data packets are generated by the GTP-U protocol entity on the N3 / NgU interface. The collection method for the measurement is a cumulative counter (CC), and the measurement is valid for packet-switched networks.

[0026] In embodiments of this disclosure, the producer equipment determines the downstream throughput of a network slice at the CU-UP end of the gNB and N3 / NgU interface. The producer equipment determines a KPI name representing the downstream throughput of a network slice instance at the gNB and CU-UP end on the N3 / NgU interface. Furthermore, the producer equipment determines the total number of downstream octets of GTP data packets provided from the UPF to the RAN associated with a single network slice via the N3 / NgU interface. The producer equipment divides the determined total number of downstream octets of GTP data packets by a predetermined time period to determine the downstream throughput.

[0027] In embodiments of this disclosure, the producer equipment determines the upstream throughput of a network slice at the CU-UP end of the gNB and N3 / NgU interface. The producer equipment determines a KPI name representing the upstream throughput of a network slice instance at the gNB and CU-UP end on the N3 / NgU interface. Furthermore, the producer equipment determines the total number of upstream octets of GTP data packets provided from the UPF to the RAN associated with a single network slice via the N3 / NgU interface. The producer equipment divides the determined total number of upstream octets of GTP data packets by a predetermined time period to determine the upstream throughput.

[0028] In embodiments of this disclosure, the consumer device determines that the upstream and downstream throughputs are less than target values ​​based on performance metrics. When the upstream and / or downstream throughputs are less than the target values, the consumer device sends a reconfiguration request to the producer device to modify or update the MOI attributes of the gNB and CU-UP.

[0029] In embodiments of this disclosure, the consumer device predicts, based on performance metrics, that upstream and / or downstream throughput will decrease to below a target value. When the upstream and / or downstream throughput is predicted to decrease to below the target value, the consumer device sends a request message to the producer device to create a guaranteed closed-loop control (ACCL).

[0030] In embodiments of this disclosure, the attributes used to collect performance metrics include objectInstances, reportingCtrl, performanceMetrics, and granularityPeriod.

[0031] According to another aspect of this disclosure, a method for optimizing a Radio Access Network (RAN) node in a wireless network is provided. The method includes: a consumer device sending a Create MOI request to a producer device for collecting performance metrics, wherein the Create MOI request includes multiple attributes for collecting performance metrics, wherein the performance metrics include at least one of measurements and KPIs collected at at least one of the gNB and CU-UP ends of the N3 / NgU interface of the producer device; the consumer device receiving performance metrics from the producer device, wherein the measurements include at least one of a measurement of the number of octets of incoming GTP data packets on the N3 / NgU interface from the UPF to the RAN and a measurement of the number of octets of outgoing GTP data packets on the N3 / NgU interface from the RAN to the UPF, wherein the collected KPIs include at least one of downstream throughput of a network slice and upstream throughput of a network slice at at least one of the gNB and CU-UP ends of the N3 / NgU interface; and optimization, wherein the consumer device determines at least one of the gNB configuration and CU-UP configuration at the producer device based on the performance metrics received from the producer device.

[0032] In embodiments of this disclosure, the measurement includes a measurement name that represents the number of octets of incoming and outgoing GTP data packets on the N3 / NgU interface of the gNB and CU-UP, wherein the incoming and outgoing GTP data packets are generated by the GTP-U protocol entity on the N3 / NgU interface, wherein the measurement is conditional upon the gNB and CU-UP receiving or sending GTP-U data PDUs on the N3 / NgU interface of the producer equipment, and wherein the measurement may optionally be divided into sub-counters according to S-NSSAI.

[0033] According to another aspect of this disclosure, a producer device for optimizing radio access network (RAN) nodes in a wireless network is provided. The producer device includes a memory, an input / output (I / O) interface, a communication processor, and a RAN node optimization controller communicatively coupled to the memory, the communication processor, and the I / O interface. The RAN node controller is configured to receive a Create MOI request from a consumer device for collecting performance metrics. The Create MOI request includes multiple attributes for collecting performance metrics, wherein the performance metrics include at least one of a measurement and a KPI collected at at least one of the gNB and N3 / NgU interface CU-UP terminals of the producer device, based on the multiple attributes received in the Create MOI request. A site performs the collection of performance metrics, wherein the measurements include at least one of the measurement of the number of octets of incoming GTP data packets on the N3 / NgU interface from the UPF to the RAN and the measurement of the number of octets of outgoing GTP data packets on the N3 / NgU interface from the RAN to the UPF, wherein the collected KPIs include at least one of the downstream throughput of the network slice and the upstream throughput of the network slice at at least one of the gNB and CU-UP ends of the N3 / NgU interface, and the collected performance metrics are sent to the consumer device to optimize at least one of the gNB configuration and CU-UP configuration at the producer device based on the performance metrics received from the producer device.

[0034] According to another aspect of this disclosure, a consumer device for optimizing radio access network (RAN) nodes in a wireless network is provided. The consumer device includes a memory, an I / O interface, a communication processor, and a RAN node optimization controller communicatively coupled to the memory, the communication processor, and the I / O interface. The RAN node controller is configured to send a Create MOI request to the producer device for collecting performance metrics. The Create MOI request includes multiple attributes for collecting performance metrics, including at least one of measurements and KPIs collected at at least one of the gNB and CU-UP ends of the N3 / NgU interface of the producer device. The device receives performance metrics from the producer device. The measurements include at least one of the number of octets of incoming GTP data packets on the N3 / NgU interface from the UPF to the RAN and the number of octets of outgoing GTP data packets on the N3 / NgU interface from the RAN to the UPF. The collected KPIs include at least one of the downstream throughput of network slices and the upstream throughput of network slices at the gNB and CU-UP ends of the N3 / NgU interface. The device optimizes at least one of the gNB configuration and CU-UP configuration at the producer device based on the performance metrics received from the producer device.

[0035] Other aspects, advantages, and salient features of this disclosure will become apparent to those skilled in the art from the following detailed description of various embodiments disclosed in conjunction with the accompanying drawings.

[0036] [Beneficial effects of the invention]

[0037] The aspects of this disclosure will at least address the aforementioned problems and / or disadvantages, and provide at least the following advantages. Therefore, one aspect of this disclosure is to provide an efficient communication method in a wireless communication system. Attached Figure Description

[0038] The above and other aspects, features and advantages of certain embodiments of the present disclosure will become more apparent from the following description taken in conjunction with the accompanying drawings, in which:

[0039] Figure 1 This is a block diagram of a producer device for RAN node optimization in a wireless network according to embodiments of the present disclosure;

[0040] Figure 2 This is a block diagram of a consumer device for RAN node optimization in a wireless network according to embodiments of the present disclosure;

[0041] Figure 3 This is a sequence diagram illustrating a method for RAN node optimization in a wireless network according to embodiments of the present disclosure;

[0042] Figure 4 This is a flowchart illustrating a method for RAN node optimization by a producer device in a wireless network according to an embodiment of the present disclosure;

[0043] Figure 5 This is a flowchart illustrating a method for RAN node optimization by a consumer device in a wireless network according to an embodiment of the present disclosure;

[0044] Figure 6 The structure of a base station according to an embodiment of the present disclosure is shown; and

[0045] Figure 7 The structure of a network entity according to an embodiment of this disclosure is shown.

[0046] Throughout the accompanying drawings, the same reference numerals are used to denote the same elements. Detailed Implementation

[0047] The aspects of this disclosure will at least address the aforementioned problems and / or disadvantages, and provide at least the following advantages. Therefore, one aspect of this disclosure is to provide a terminal in a wireless communication system and a communication method thereof.

[0048] The following description with reference to the accompanying drawings is intended to aid in a full understanding of the various embodiments of this disclosure as defined by the claims and their equivalents. It includes various specific details to aid understanding, but these are considered exemplary only. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the various embodiments described herein without departing from the scope and spirit of this disclosure. Additionally, for clarity and brevity, descriptions of well-known functions and structures may be omitted.

[0049] The terms and words used in the following description and claims are not limited to their literal meaning, but are used by the inventors only to enable a clear and consistent understanding of this disclosure. Therefore, those skilled in the art should understand that the following description of various embodiments of this disclosure is for illustrative purposes only and is not intended to limit the scope of this disclosure as defined by the appended claims and their equivalents.

[0050] It should be understood that, unless the context clearly specifies otherwise, the singular forms “a,” “an,” and “the” include plural indicators. Thus, for example, a reference to “component surface” includes a reference to one or more such surfaces.

[0051] Furthermore, the various embodiments described herein are not necessarily mutually exclusive, as some embodiments can be combined with one or more other embodiments to form new embodiments. Unless otherwise stated, the term "or" as used herein means non-exclusive or. The examples used herein are intended only to facilitate understanding of how the embodiments described herein can be practiced and to further enable those skilled in the art to practice the embodiments described herein. Therefore, these examples should not be construed as limiting the scope of the embodiments described herein.

[0052] As is customary in the art, embodiments are described and illustrated in terms of blocks that perform one or more described functions. These blocks (referred herein to as managers, units, modules, hardware components, etc.) are physically implemented by analog and / or digital circuitry (such as logic gates, integrated circuits, microprocessors, microcontrollers, memory circuitry, passive electronic components, active electronic components, optical components, hardwired circuitry, etc.) and optionally driven by firmware and software. For example, the circuitry is embodied in one or more semiconductor chips or on a substrate support such as a printed circuit board. The circuitry constituting a block may be implemented by dedicated hardware, by a processor (e.g., one or more programmed microprocessors and associated circuitry), or by a combination of dedicated hardware (for performing certain functions of the block) and a processor (for performing other functions of the block). Without departing from the scope of the proposed method, each block of the embodiments is physically divided into two or more interacting and discrete blocks. Similarly, without departing from the scope of the proposed method, the blocks of the embodiments may be physically combined into more complex blocks.

[0053] The accompanying drawings are provided to aid in the easy understanding of the various technical features, and it should be understood that the embodiments presented herein are not limited to the drawings. Therefore, the proposed methods are to be interpreted as extending to any changes, equivalents, and substitutions other than those specifically set forth in the drawings. Although the terms first, second, etc., are used herein to describe various elements, these elements are not limited by these terms. These terms are generally used to distinguish one element from another.

[0054] In existing methods, the KPIs outlining upstream and downstream throughput on the N3 / NgU interface for each network slice instance only apply to core network subnet slices. This is because these KPIs rely on measurements taken at the UPF, which is the endpoint of the N3 / NgU interface associated with a specific network slice subnet instance. From the RAN's perspective, there is currently no way to measure or monitor the throughput on the N3 / NgU interface of a RAN slice or slice subnet across RAN aggregation points (e.g., gNB and CU-UP). Therefore, the RAN NSSMF cannot determine the throughput on the N3 / NgU interface provided by the gNB and CU-UP for a specific slice. Furthermore, consumer NSSMFs within the core network slice subnet may want to know the N3 / NgU throughput at the RAN end (i.e., at gNB / CU-UP). However, since this measurement is not standardized at the RAN (gNB / CU-UP) end of the N3 / NgU interface, there is no standardized mechanism to relay this information back to core network slice subnet consumers. Furthermore, the throughput at the UPF end of the N3 / NgU interface can differ from the throughput at the gNB / CU-UP end of the N3 / NgU interface. To better plan gNB / CU-UP capacity, performance metrics need to be calculated at the gNB / CU-UP end of the N3 / NgU interface. Therefore, defining these metrics is important.

[0055] Unlike existing methods, the proposed solution provides a method and system for optimizing RAN nodes by combining measurements and associated throughput KPIs of RAN slices and slice subnets on the N3 / NgU interfaces of gNB and CU-UP, which are the endpoints of the N3 / NgU interfaces. With these KPIs, the RAN NSSMF can make informed decisions regarding the allocation of additional CU-UP resources and the instantiation of new CU-UPs as needed. Specifically, the solution involves new measurements at the gNB / CU-UP end to capture the number of octets of outgoing General Packet Radio Service (GPRS) Tunneling Protocol (GTP) data packets and the number of octets of incoming GTP data packets on the N3 / NgU interfaces of a specific network and slice. Furthermore, the proposed solution introduces new KPIs to determine the downstream and upstream throughput of network slices on the N3 / NgU interfaces at the gNB / CU-UP end.

[0056] Therefore, the proposed solution will allow the RAN to measure and monitor throughput on the N3 / NgU interface of each RAN slice and slice subnet across its aggregation points (such as gNB and CU-UP). Leveraging this capability, the RAN NSSMF can determine the throughput on the N3 / / NgU interface provided by the gNB and CU-UP for a specific slice. Furthermore, the proposed solution enables the RAN NSSMF to make informed decisions about where to allocate additional CU-UP resources and instantiate new CU-UPs if necessary. Thus, better RAN node optimization of throughput can be achieved using the new measurements and KPIs, which, from the RAN's perspective, help monitor and maintain the performance integrity of the slices.

[0057] Furthermore, introducing these new KPIs and measurement capabilities at the gNB / CU-UP end will bridge the existing gap between the core network and RAN in throughput monitoring. This holistic approach ensures that both ends of the N3 / NgU interface are considered, resulting in a more accurate and comprehensive understanding of network performance. The ability to monitor throughput at the RAN end will also enable more precise troubleshooting and performance tuning, thereby improving the overall quality of service experienced by end users. By standardizing these measurements, the proposed solution ensures interoperability and consistency across different network components and vendors, thus promoting a more robust and reliable network infrastructure.

[0058] Furthermore, the proposed solution's ability to provide real-time throughput data at the RAN end significantly enhances network flexibility and responsiveness. Network operators can quickly identify and resolve bottlenecks or performance issues, ensuring that network slices meet their expected Service Level Agreements (SLAs). This proactive network management approach not only improves user satisfaction but also optimizes resource utilization, reduces operating costs, and increases network operational efficiency. Overall, the proposed solution represents a significant advancement in RAN resource management and optimization, paving the way for more dynamic and efficient 5G networks.

[0059] It should be understood that the boxes in each flowchart and the combination of flowcharts can be executed by one or more computer programs that include computer-executable instructions. The entirety of one or more computer programs can be stored in a single memory device, or one or more computer programs can be divided into different parts stored in multiple different memory devices.

[0060] Any function or operation described herein can be processed by a processor or a combination of processors. A processor or combination of processors is circuitry that performs processing and includes, for example, an application processor (AP, such as a central processing unit (CPU)), a communication processor (CP, such as a modem), a graphics processing unit (GPU), a neural processing unit (NPU) (e.g., an artificial intelligence (AI) chip), a Wi-Fi chip, Bluetooth, etc. TM Circuits including chips, GPS chips, NFC chips, connectivity chips, sensor controllers, touch controllers, fingerprint sensor controllers, display driver integrated circuits (ICs), audio codec chips, Universal Serial Bus (USB) controllers, camera controllers, image processing ICs, microprocessor units (MPUs), system-on-a-chip (SoCs), and other ICs.

[0061] Now refer to the attached diagram, for more specific details. Figures 1 to 5 The corresponding features are consistently indicated by similar reference numerals throughout the figures, which illustrate preferred embodiments.

[0062] Figure 1 This is a block diagram of a producer device for RAN node optimization in a wireless network according to embodiments of the present disclosure.

[0063] refer to Figure 1 The producer device 101 includes a memory 105, a communication processor 103, an input / output (I / O) interface 104, and a RAN node optimization controller 106.

[0064] Memory 105 is configured to store instructions to be executed by communication processor 103. Memory 105 may include non-volatile storage elements. Examples of such non-volatile storage elements may include magnetic hard disks, optical disks, floppy disks, flash memory, or electrically programmable memory (EPROM) or electrically erasable programmable memory (EEPROM). Additionally, in some examples, memory 105 may be considered a non-transitory storage medium. The term non-transitory can indicate that the storage medium is not embodied in a carrier wave or propagating signal. However, the term non-transitory should not be construed as meaning that memory 105 is immovable. In some examples, memory 105 is configured to store a larger amount of information. In some instances, the non-transitory storage medium may store data that can change over time (e.g., in random access memory (RAM) or cache memory).

[0065] The communication processor 103 may include one or more processors. These processors may be general-purpose processors (such as central processing units (CPUs), application processors (APs), pure graphics processing units (such as graphics processing units (GPUs), visual processing units (VPUs)), and / or AI-specific processors (such as neural processing units (NPUs)). The communication processor 103 may include multiple cores and is configured to execute instructions stored in memory 105.

[0066] I / O interface 104 transmits information between memory 105 and external peripheral devices. Peripheral devices are input-output devices associated with network devices. I / O interface 104 receives multiple messages from various UEs, network devices, servers, etc.

[0067] In embodiments of this disclosure, the RAN node optimization controller 106 of the producer device 101 communicates with the processor 103, I / O interface 104, and memory 105 for RAN node optimization in a wireless network.

[0068] RAN Node Optimization Controller 106 receives a Create Management Object Instance (MOI) request from a consumer device for collecting performance metrics. The MOI request includes attributes for collecting performance metrics, which include measurements and KPIs collected at the gNB and CU-UP ends of the N3 / NgU interface of producer device 101. Based on the attributes received in the MOI request, RAN Node Optimization Controller 106 performs performance metric collection at the CU-UP ends of the gNB and N3 / NgU interface of producer device 101. The performance metrics include measurements of the number of octets of incoming GTP data packets on the N3 / NgU interface from UPF to RAN, measurements of the number of octets of outgoing GTP data packets on the N3 / NgU interface from RAN to UPF, KPIs for downstream throughput of network slices, and KPIs for upstream throughput of network slices at the CU-UP ends of the gNB and N3 / NgU interface. In addition, the RAN node optimization controller 106 sends the collected performance metrics to the consumer device to optimize the gNB configuration and CU-UP configuration at the producer device 101 based on the performance metrics received from the producer device 101.

[0069] In embodiments of this disclosure, the RAN node optimization controller 106 determines whether measurement conditions are met, wherein the measurement conditions include the gNB and CU-UP receiving and transmitting GTP-U data PDUs on the N3 / NgU interface. When the conditions are met, the RAN node optimization controller 106 performs measurements at the CU-UP end of the gNB and N3 / NgU interface. This ensures that performance metrics are collected only when relevant data services are present, thereby optimizing the use of computing resources and ensuring the accuracy of the collected metrics.

[0070] In embodiments of this disclosure, the RAN node optimization controller 106 divides measurements into sub-counters based on S-NSSAI, where each measurement is a single integer value. When performing optional S-NSSAI sub-counter measurements, the number of measurements equals the number of supported S-NSSAIs. This granularity allows for more detailed analysis of network performance, enabling the identification of specific issues associated with individual network slices. By breaking down measurements into sub-counters, the RAN node optimization controller 106 can provide more precise data, which is crucial for fine-tuning network configurations.

[0071] In embodiments of this disclosure, the RAN node optimization controller 106 determines the downstream throughput of a network slice at the CU-UP end of the gNB and N3 / NgU interface. The RAN node optimization controller 106 determines a KPI name representing the downstream throughput of a network slice instance at the gNB and CU-UP end of the N3 / NgU interface. Furthermore, the RAN node optimization controller 106 determines the total number of downstream octets of GTP data packets associated with a single network slice provided from the UPF to the RAN via the N3 / NgU interface. The RAN node optimization controller 106 divides the determined total number of downstream octets of GTP data packets by a predetermined time period to determine the downstream throughput. This calculation is used to evaluate the performance of the network slice in terms of data delivery efficiency and can help make informed network optimization decisions.

[0072] In embodiments of this disclosure, the RAN node optimization controller 106 determines the upstream throughput of network slices at the CU-UP end of the gNB and N3 / NgU interfaces. The RAN node optimization controller 106 determines a KPI name representing the upstream throughput of network slice instances at the gNB and CU-UP ends of the N3 / NgU interfaces. Furthermore, the RAN node optimization controller 106 determines the total number of uplink octets of GTP data packets associated with a single network slice provided from the UPF to the RAN via the N3 / NgU interface. The RAN node optimization controller 106 divides the determined total number of uplink octets of GTP data packets by a predetermined time period to determine the upstream throughput. Similar to downstream throughput, this metric is crucial for evaluating the network's ability to process data sent from user equipment to the network, thereby ensuring a balanced and efficient data flow.

[0073] The RAN node optimization controller 106 is a hardware component of this disclosure. The RAN node optimization controller 106 is integrated into the producer device 101 via processing circuitry including logic gates, integrated circuits, microprocessors, microcontrollers, memory circuitry, passive and active electronic components, optical components, hard-wired circuitry, or similar technologies. This circuitry can be displayed in one or more semiconductor chips or on a substrate support such as a printed circuit board. Compared to software-only solutions, this hardware-based approach also allows for faster processing and lower power consumption, making it ideal for modern electronic devices requiring high performance and energy efficiency. The integration of this advanced hardware component ensures that the RAN node optimization controller 106 can handle the demanding requirements of real-time network optimization and performance monitoring.

[0074] In embodiments of this disclosure, components of the RAN node optimization controller 106 can be implemented using AI models. Functions associated with the AI ​​model can be executed via memory 105 and processor 103. One or more processors control the processing of input data based on predefined operating rules or AI models stored in non-volatile memory and volatile memory. Predefined operating rules or AI models are provided through training or learning. This combination of AI allows the RAN node optimization controller 106 to dynamically adapt to changing network conditions, thereby making real-time adjustments to optimize performance. The AI ​​model can learn from historical data and predict future network behavior, providing proactive optimization rather than reactive adjustments. This capability significantly enhances network efficiency and reliability, ensuring a high-quality user experience.

[0075] Although Figure 1The hardware components of producer device 101 are depicted; however, it should be noted that alternative embodiments are not limited to these elements. In other embodiments, producer device 101 may include more or fewer hardware components. Furthermore, the labels or names assigned to these components are purely illustrative and do not limit the scope of this disclosure. Additionally, one or more components may be combined to perform the same or substantially similar functions.

[0076] Figure 2 This is a block diagram of a consumer device for RAN node optimization in a wireless network according to embodiments of the present disclosure.

[0077] refer to Figure 2 Consumer device 201 includes memory 205, communication processor 203, input / output (I / O) interface 204, and RAN node optimization controller 206.

[0078] Memory 205 is configured to store instructions to be executed by communication processor 203. Memory 205 may include non-volatile storage elements. Examples of such non-volatile storage elements may include magnetic hard disks, optical disks, floppy disks, flash memory, or electrically programmable memory (EPROM) or electrically erasable programmable memory (EEPROM). Additionally, in some examples, memory 205 may be considered a non-transitory storage medium. The term non-transitory can indicate that the storage medium is not embodied in a carrier wave or propagating signal. However, the term non-transitory should not be construed as meaning that memory 205 is immovable. In some examples, memory 205 is configured to store a larger amount of information. In some examples, the non-transitory storage medium may store data that can change over time (e.g., in random access memory (RAM) or cache memory).

[0079] The communication processor 203 may include one or more processors. These processors may be general-purpose processors (such as central processing units (CPUs), application processors (APs), pure graphics processing units (such as graphics processing units (GPUs), visual processing units (VPUs)), and / or AI-specific processors (such as neural processing units (NPUs)). The communication processor 203 may include multiple cores and is configured to execute instructions stored in memory 205.

[0080] I / O interface 204 transmits information between memory 205 and external peripheral devices. Peripheral devices are input-output devices associated with network devices. I / O interface 204 receives multiple messages from various UEs, network devices, servers, etc.

[0081] In embodiments of this disclosure, the RAN node optimization controller 206 of user equipment 201 communicates with processor 203, I / O interface 204 and memory 205 to perform RAN node optimization in a wireless network.

[0082] RAN Node Optimization Controller 206 sends a Create MOI request to Producer Device 101 for collecting performance metrics, wherein the Create MOI request includes attributes for collecting performance metrics. RAN Node Optimization Controller 206 receives performance metrics from Producer Device 101, including measurements of the number of octets of incoming GTP data packets on the N3 / NgU interface from UPF to RAN, measurements of the number of octets of outgoing GTP data packets on the N3 / NgU interface from RAN to UPF, KPIs of downstream throughput of network slices at the CU-UP end of the gNB and N3 / NgU interfaces, and KPIs of upstream throughput of network slices. Furthermore, RAN Node Optimization Controller 206 optimizes the gNB configuration and CU-UP configuration at Producer Device 101 based on the performance metrics received from Producer Device 101.

[0083] In embodiments of this disclosure, the RAN node optimization controller 206 determines that upstream and / or downstream throughput is less than a target value based on performance metrics. When upstream and / or downstream throughput is less than the target value, the RAN node optimization controller 206 sends a reconfiguration request to the producer device 101 to modify or update the MOI attributes of the gNB and CU-UP. This reconfiguration process ensures that the network maintains optimal performance levels, thereby enhancing the user experience by minimizing latency and maximizing data transmission rates. The controller's ability to dynamically adjust the configuration based on real-time performance data is crucial for maintaining network efficiency and reliability.

[0084] In another embodiment of this disclosure, the RAN node optimization controller 206 predicts, based on performance metrics, that upstream and / or downstream throughput will decrease to below target values. When the upstream and / or downstream throughput is predicted to decrease to below target values, the RAN node optimization controller 206 sends a request message to the producer device 101 to create an ACCL. This predictive capability allows the controller to proactively address potential performance issues before they impact the network, thereby ensuring a more stable and consistent user experience. By predicting and mitigating potential bottlenecks, the controller helps maintain the overall health and efficiency of the network.

[0085] In this embodiment, components of the RAN node optimization controller 206 can be implemented using an AI model. Functions associated with the AI ​​model can be executed via memory 205 and processor 203. One or more processors control the processing of input data based on predefined operating rules or AI models stored in non-volatile memory and volatile memory. The predefined operating rules or AI models are provided through training or learning.

[0086] Here, "learning by providing" means developing predefined operating rules or AI models with desired characteristics by applying learning processing to multiple learning data sets. Learning can be performed within the device itself that performs the AI ​​according to embodiments of this disclosure and / or can be implemented via a separate server / system.

[0087] AI models can consist of multiple neural network layers. Each layer has multiple weight values, and layer operations are performed by computing the previous layer and operating on the multiple weights. Examples of neural networks include, but are not limited to, convolutional neural networks (CNNs), deep neural networks (DNNs), recurrent neural networks (RNNs), restricted Boltzmann machines (RBMs), deep belief networks (DBNs), bidirectional recurrent deep neural networks (BRDNNs), generative adversarial networks (GANs), and deep Q-networks.

[0088] Learning processing is a method for training a predetermined target device (e.g., a robot) using multiple learning data sets to enable, allow, or control the target device to make determinations or predictions. Examples of learning processes include, but are not limited to, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning.

[0089] Although Figure 2 The hardware components of consumer device 201 are depicted; however, it should be noted that alternative embodiments are not limited to these elements. In other embodiments, consumer device 201 may include more or fewer hardware components. Furthermore, the labels or names assigned to these components are purely illustrative and do not limit the scope of this disclosure. Additionally, one or more components may be combined to perform the same or substantially similar functions.

[0090] In the context of 5G technology, consumer equipment 201 and producer equipment 101 refer to two different categories of devices based on their roles in the wireless network. User equipment 201 is typically any functional entity within an Operations Support System (OSS) or the Operations, Administration, and Maintenance (OAM) department of an operator or core network. In embodiments of this disclosure, consumer equipment 201 includes any OSS entity responsible for the performance of the gNB or NSSMF.

[0091] On the other hand, producer equipment 101 refers to equipment that generates and provides network services or data to the 5G ecosystem. These devices are indispensable for the functionality and optimization of 5G networks. Examples of producer equipment 101 include base stations, network servers, and edge computing nodes. These devices facilitate the transmission, processing, and management of data across the network. For example, a 5G base station that sends and receives data to and from network servers and user equipment, or an edge computing node that processes data locally to reduce latency, is considered producer equipment 101. Essentially, when producer equipment 101 uses network services, it enables and maintains these services, thereby ensuring seamless and efficient network operation.

[0092] Figure 3 A sequence diagram illustrating a method for RAN node optimization in a wireless network according to embodiments of the present disclosure is shown.

[0093] refer to Figure 3 In Operation 1, a network slice is provided between consumer device 201 and producer device 101 according to the process defined in TS 28531, with a throughput service level agreement (SLA) provided in the service profile.

[0094] In operation 2, MnS consumer 301 sends a createMOI request to performance guarantee (PA) MnS producer 302. The createMOI request includes a PerfMetricJob representing a performance metric production job. To activate the production of the specified performance metric, MnS consumer 301 needs to create a PerfMetricJob instance on PA MnS producer 302. Furthermore, the createMOI request includes several attributes of the performance metric.

[0095] In operation 3, PA MnS producer 302 sends a createMOI response to MnS consumer 301.

[0096] In Operation 4, PA MnS producer 302 performs performance metric collection at the CU-UP end of the gNB and N3 / NgU interfaces based on multiple attributes received in the createMOI request. Performance metrics include the measurement of the number of octets of incoming GTP data packets on the N3 / NgU interface from UPF to RAN, the measurement of the number of octets of outgoing GTP data packets on the N3 / NgU interface from RAN to UPF, KPIs for downstream throughput of network slices at the CU-UP end of the gNB and N3 / NgU interfaces, and KPIs for upstream throughput of network slices.

[0097] In operation 5, PA MnS producer 302 delivers the collected performance metrics to MnS consumer 301 to optimize the gNB configuration and CU-UP configuration at producer device 101 based on the performance metrics.

[0098] In operation 6, MnS consumer 301 checks the need for RAN node optimization based on performance metrics received from PA MnS producer 302.

[0099] In operation 6.1, when MnS consumer 301 determines, based on performance metrics, that an SLA has been violated or that upstream and downstream throughput are less than target values, MnS consumer 301 sends a reconfiguration request to the generic supply MnS producer 303 of producer device 101 to modify or update the MOI attributes of gNB and CU-UP, thereby expanding / extending gNB / CU-UP resources. Generic supply MnS producer 303 then interacts appropriately with NFV MANO 304. Generic supply MnS producer 303 interacts with NFV MANO 302 as defined in TS 28526. The modifyMOIAttributes response is sent from generic supply MnS producer 303 to MnS consumer 301.

[0100] In operation 6.2, when MnS consumer 301 predicts based on performance metrics that the SLA may be violated or that upstream and downstream throughput will decrease to below target values, MnS consumer 301 sends a request message to producer device 305 of producer device 101 to create an ACCL. In operation 301, the guarantee target will be set to the initial throughput SLA provided in the service profile. A createMOI response is sent from ACCL producer 305 to MnS consumer 301, indicating the successful creation of the closed-loop control loop. The created CCL will be used to ensure the throughput target defined in TS 28536.

[0101] Therefore, when the supply MnS consumer 301 is notified of the throughput at gNB / CU-UP on the N3 / NgU interface based on the SLA, the supply MnS consumer 301 optimizes the gNB configuration and CU-UP configuration at the producer equipment 101.

[0102] Figure 4 This is a flowchart illustrating a method for RAN node optimization by a producer device in a wireless network according to an embodiment of the present disclosure.

[0103] refer to Figure 4In operation S401, producer device 101 receives a Create MOI request from consumer device 201 for collecting performance metrics. The Create MOI request includes several attributes for collecting performance metrics, including objectInstances, reportingCtrl, performanceMetrics, and granularityPeriod.

[0104] In operation S402, producer device 101 performs performance metric collection at the gNB and CU-UP ends of the N3 / NgU interface of producer device 101 based on multiple attributes received in the MOI creation request. Performance metrics include measurements and KPIs collected at the gNB and CU-UP ends of the N3 / NgU interface.

[0105] In embodiments of this disclosure, the measurement includes a measurement name representing the number of octets of incoming and outgoing GTP data packets on the N3 / NgU interface at the gNB and CU-UP ends, wherein the incoming and outgoing GTP data packets are generated by the GTP-U protocol entity on the N3 / NgU interface. The measurement is collected using a cumulative counter (CC), and the measurement is valid for packet-switched networks.

[0106] In embodiments of this disclosure, producer equipment 101 determines whether measurement conditions are met, wherein the measurement conditions include gNB and CU-UP receiving and transmitting GTP-U data PDUs on the N3 / NgU interface. When the conditions are met, producer equipment 101 performs measurements at the CU-UP end of the gNB and N3 / NgU interface.

[0107] In embodiments of this disclosure, producer device 101 divides measurements into sub-counters based on S-NSSAI. Each measurement is a single integer value when the number of measurements equals 1. When performing optional S-NSSAI sub-counter measurements, the number of measurements equals the number of supported S-NSSAI values.

[0108] In embodiments of this disclosure, the KPI information includes KPI names representing the downstream and upstream throughput of network slice instances at the gNB and CU-UP ends on the N3 / NgU interface.

[0109] The measurement definition template is defined in Clause 33 of TS 32.404, and the KPI definition template is defined in Clause 5 of TS 28.554. In embodiments of this disclosure, the measurement and KPI descriptions proposed according to the standard templates include:

[0110] 1. Number of octets in the incoming GTP data packets from UPF to RAN on the N3 / NgU interface:

[0111] a) This measurement provides the number of octets of incoming GTP data packets on the N3 / NgU interface at the gNB / CU-UP end, which have been generated by the GTP-U protocol entity on the N3 / NgU interface. The measurement can optionally be divided into sub-counters according to S-NSSAI.

[0112] b) CC

[0113] c) gNB / CU-UP receives GTP-U data PDU from UPF on the N3 / NgU interface.

[0114] d) Each measurement is a single integer value, and the number of measurements is equal to 1. If an optional S-NSSAI sub-counter measurement is performed, the number of measurements is equal to the number of supported S-NSSAIs.

[0115] e) GTP.InDataOctN3gNB and optional GTP.InDataOctN3gNB.SNSSAI, where SNSSAI identifies S-NSSAI.

[0116] f) EP_NgU, EP_N3

[0117] g) Applicable to packet switching

[0118] h) 5GS

[0119] 2. Number of octets in the outgoing GTP data packets from RAN to UPF on the N3 / NgU interface:

[0120] a) This measurement provides the number of octets of outgoing GTP data packets on the N3 / NgU interface at the gNB / CU-UP end, which have been generated by the GTP-U protocol entity on the N3 / NgU interface. The measurement can optionally be divided into sub-counters according to S-NSSAI.

[0121] b) CC

[0122] c) gNB / CU-UP sends GTP-U data PDU to UPF on the N3 / NgU interface.

[0123] d) Each measurement is a single integer value, and the number of measurements is equal to 1. If an optional S-NSSAI sub-counter measurement is performed, the number of measurements is equal to the number of supported S-NSSAIs.

[0124] e) GTP.OutDataOctN3gNB and optional GTP.OutDataOctN3gNB.SNSSAI, where SNSSAI identifies S-NSSAI.

[0125] f) EP_NgU, EP_N3

[0126] g) Applicable to packet switching

[0127] h) 5GS

[0128] 3. Downstream throughput of network slices at gNB

[0129] a) DlThroughputNSgNBN3

[0130] (b) This KPI describes the downstream throughput of a network slice instance at the gNB / CU-UP end on the N3 / NgU interface. It is obtained by measuring the total number of downstream octets provided on the N3 / NgU interface from the UPF to the NG-RAN associated with a single network slice, divided by the granular time period (in milliseconds). The KPI unit is kbit / s, and the KPI type is MEAN.

[0131] c)

[0132]

[0133] d) NetworkSlice, SubNetwork

[0134] 4. Upstream throughput of network slices at gNB:

[0135] a) UlThroughputNSgNBN3

[0136] (b) This KPI describes the upstream throughput of a network slice instance at the gNB / CU-UP end on the N3 / NgU interface. It is obtained by measuring the total number of uplink octets provided on the N3 / NgU interface from NG-RAN to UPF associated with a single network slice, divided by the granular time period (in milliseconds). The KPI unit is kbit / s, and the KPI type is MEAN.

[0137] c)

[0138]

[0139] d) NetworkSlice, SubNetwork

[0140] In embodiments of this disclosure, the granular time period is any time period chosen by the operator during which the operator wants to calculate the throughput KPI. At operation S403, producer device 101 determines the downstream and upstream throughput of the network slice at the CU-UP end of the gNB and N3 / NgU interface. Producer device 101 determines a KPI name representing the downstream throughput of the network slice instance at the gNB and CU-UP end on the N3 / NgU interface. Furthermore, producer device 101 determines the total number of downstream octets of GTP data packets provided from the UPF to the RAN associated with the individual network slice via the N3 / NgU interface. Producer device 101 divides the determined total number of downstream octets of GTP data packets by a predetermined time period to determine the downstream throughput.

[0141] In embodiments of this disclosure, producer device 101 determines a KPI name representing the upstream throughput of a network slice instance at the gNB and CU-UP ends on the N3 / NgU interface. Furthermore, producer device 101 determines the total number of upstream octets of GTP data packets provided from the UPF to the RAN associated with a single network slice via the N3 / NgU interface. Producer device 101 divides the determined total number of upstream octets of GTP data packets by a predetermined time period to determine the upstream throughput.

[0142] In operation S404, producer device 101 sends the collected performance metrics (including upstream throughput and downstream throughput) to consumer device 201 to optimize the gNB configuration and CU-UP configuration at producer device 101.

[0143] Figure 5 This is a flowchart illustrating a method for RAN node optimization by a consumer device in a wireless network according to an embodiment of the present disclosure.

[0144] refer to Figure 5 In operation S501, consumer device 201 sends a Create MOI request to producer device 101 for collecting performance metrics. The Create MOI request includes several attributes for collecting performance metrics, including objectInstances, reportingCtrl, performanceMetrics, and granularityPeriod.

[0145] In operation S502, consumer device 201 receives performance metrics from producer device 101, wherein the performance metrics include a measurement of the number of octets of incoming GTP data packets on the N3 / NgU interface from UPF to RAN, a measurement of the number of octets of outgoing GTP data packets on the N3 / NgU interface from RAN to UPF, a KPI of downstream throughput of network slices at the gNB and CU-UP ends of the N3 / NgU interface, and a KPI of upstream throughput of network slices.

[0146] In operation S503, consumer device 201 optimizes the gNB configuration and CU-UP configuration at producer device 101 based on performance metrics received from producer device 101.

[0147] In embodiments of this disclosure, the consumer device 201 determines, based on performance metrics, that the upstream throughput and downstream throughput are less than target values. When the upstream throughput and downstream throughput are less than target values, the user device 201 sends a reconfiguration request to the producer device 101 to modify or update the MOI attributes of the gNB and CU-UP.

[0148] In embodiments of this disclosure, consumer device 201 predicts, based on performance metrics, that upstream and downstream throughput will decrease to below target values. When the upstream and / or downstream throughput is predicted to decrease to below target values, consumer device 201 sends a request message to producer device 101 to create an ACCL.

[0149] Figure 4 and Figure 5 Various actions, behaviors, blocks, steps, etc., are executed in the presented order, in different orders, or simultaneously. Furthermore, in some embodiments of this disclosure, some actions, behaviors, blocks, steps, etc., are omitted, added, modified, or skipped without departing from the scope of the proposed method.

[0150] Figure 6 The structure of a base station according to an embodiment of the present disclosure is shown.

[0151] like Figure 6 As shown, the base station according to the embodiment may include a transceiver 610, a memory 620, and a processor 630. The transceiver 610, memory 620, and processor 630 of the base station can operate according to the communication method of the base station described above. However, the components of the base station are not limited thereto. For example, the base station may include more or fewer components than those described above. Furthermore, the processor 630, transceiver 610, and memory 620 may be implemented as a single chip. In addition, the processor 630 may include at least one processor. Furthermore, Figure 6 The base station corresponds to Figure 1 producer equipment or Figure 2 Consumer devices.

[0152] Transceiver 610 is collectively referred to as a base station receiver and a base station transmitter, and can transmit / receive signals to / from a terminal (UE) or network entity. Signals transmitted to or received from a terminal or network entity may include control information and data. Transceiver 610 may include an RF transmitter for up-converting and amplifying the frequency of the transmitted signal, and an RF receiver for amplifying the frequency of the received signal for low noise and down-converting. However, this is only an example of transceiver 610, and the components of transceiver 610 are not limited to RF transmitters and RF receivers.

[0153] In addition, transceiver 610 can receive signals via a wireless channel and output them to processor 630, and can also transmit signals output from processor 630 via a wireless channel.

[0154] The memory 620 can store programs and data required for the operation of the base station. Furthermore, the memory 620 can store control information or data included in signals acquired by the base station. The memory 620 can be a storage medium such as a read-only memory (ROM), random access memory (RAM), hard disk, CD-ROM, and DVD, or a combination of storage media.

[0155] The processor 630 can control a series of processes to enable the base station to operate as described above. For example, the transceiver 610 can receive data signals including control signals transmitted by the terminal, and the processor 630 can determine the result of receiving the control signals and data signals transmitted by the terminal.

[0156] Figure 7 The structure of a network entity according to an embodiment of this disclosure is shown.

[0157] like Figure 7 As shown, the network entity according to the embodiment may include a transceiver 710, a memory 720, and a processor 730. The transceiver 710, memory 720, and processor 730 of the network entity can operate according to the communication method of the network entity described above. However, the components of the network entity are not limited thereto. For example, the network entity may include more or fewer components than those described above. Furthermore, the processor 730, transceiver 710, and memory 720 may be implemented as a single chip. Additionally, the processor 730 may include at least one processor. Furthermore, Figure 7 Network entities correspond to Figure 1 producer equipment or Figure 2 Consumer devices.

[0158] Transceiver 710 is collectively referred to as a network entity receiver and a network entity transmitter, and can transmit / receive signals to / from a terminal (UE), a base station, or another network entity. Signals transmitted to or received from a terminal, base station, or another network entity may include control information and data. Transceiver 710 may include an RF transmitter for up-converting and amplifying the frequency of the transmitted signal, and an RF receiver for amplifying the frequency of the received signal to reduce noise. However, this is only an example of transceiver 710, and the components of transceiver 710 are not limited to RF transmitters and RF receivers.

[0159] In addition, transceiver 710 can receive signals via a wireless channel and output them to processor 730, and can also transmit signals output from processor 730 via a wireless channel.

[0160] The memory 720 can store programs and data required for the operation of the network entity. Furthermore, the memory 720 can store control information or data included in signals received by the network entity. The memory 720 can be a storage medium such as a read-only memory (ROM), random access memory (RAM), hard disk, CD-ROM, and DVD, or a combination of storage media.

[0161] The processor 730 can control a series of processes to cause the network entity to operate as described above. For example, the transceiver 710 can receive data signals including control signals transmitted by a terminal or base station, and the processor 730 can determine the result of receiving the control signals and data signals transmitted by the terminal or base station.

[0162] It should be understood that the various embodiments of this disclosure described in the claims and specification can be implemented in hardware, software, or a combination of hardware and software.

[0163] Any such software may be stored in a non-transitory computer-readable storage medium. The non-transitory computer-readable storage medium stores one or more computer programs (software modules) that include computer-executable instructions, which, when executed by one or more processors of an electronic device, cause the electronic device to perform the methods of this disclosure.

[0164] Any such software may be stored in the form of volatile or non-volatile memory, such as a storage device like read-only memory (ROM), whether erasable or rewritable, or in the form of memory, such as random access memory (RAM), memory chips, devices, or integrated circuits, or stored on an optically or magnetically readable medium, such as an optical disc (CD), a digital versatile disc (DVD), a magnetic disk, or magnetic tape. It should be understood that storage devices and storage media are various embodiments of non-transitory machine-readable storage suitable for storing one or more computer programs including instructions that, when executed, implement various embodiments of this disclosure. Therefore, various embodiments provide a program and a non-transitory machine-readable storage medium for storing such a program, the program including code for implementing the means or methods claimed as any one of the claims of this specification.

[0165] While this disclosure has been shown and described with reference to various embodiments thereof, those skilled in the art will understand that various changes in form and detail may be made therein without departing from the spirit and scope of this disclosure as defined by the appended claims and their equivalents.

Claims

1. A method performed by a base station (BS) in a wireless communication system, the method comprising: measuring downstream throughput on an interface between the BS and a user plane function (UPF) entity; and measuring upstream throughput on the interface between the BS and the UPF entity. The measurement of downstream throughput comprises measuring a number of octets of incoming data packets on the interface, and 2. The method of claim 1, wherein, wherein the measurement of upstream throughput comprises measuring a number of octets of outgoing data packets on the interface. The incoming data packets and the outgoing data packets comprise GPRS Tunneling Protocol (GTP) data packets based on the GTP-U (User Plane) protocol.

3. The method of claim 2, wherein, The first number of measurements of downstream throughput and the second number of measurements of upstream throughput are equal to 1.

4. The method of claim 1, wherein, The upstream throughput and the downstream throughput are measured per network slice.

5. The method of claim 1, wherein, The upstream throughput and the downstream throughput are measured for a granularity period.

6. The method of claim 1, wherein, The BS comprises a gNodeB CU (Central Unit)-UP (User Plane).

7. The method of claim 1, wherein, The interface comprises an NgU interface.

8. The method of claim 7, wherein, 9. A base station (BS) in a wireless communication system, the BS comprising: a transceiver; and a controller coupled with the transceiver and configured to: measure downstream throughput on an interface between the BS and a user plane function (UPF) entity; and measure upstream throughput on the interface between the BS and the UPF entity. The controller is further configured to: measure a number of octets of incoming data packets on the interface, and 10. The BS of claim 9, wherein, measure a number of octets of outgoing data packets on the interface. The incoming data packets and the outgoing data packets comprise GPRS Tunneling Protocol (GTP) data packets based on the GTP-U (User Plane) protocol. The first number of measurements of downstream throughput and the second number of measurements of upstream throughput are equal to 1.

11. The BS of claim 10, wherein, The upstream throughput and the downstream throughput are measured per network slice.

12. The BS of claim 9, wherein, The upstream throughput and the downstream throughput are measured for a granularity period.

13. The BS of claim 9, wherein, The BS comprises a gNodeB or a CU-UP, and 14. The BS of claim 9, wherein, wherein the interface comprises an NgU interface.

15. The BS of claim 9, wherein, ​ ​

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