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WO2025174330A1 - Politiques dans des domaines de réseau et d'entreprise - Google Patents

Politiques dans des domaines de réseau et d'entreprise

Info

Publication number
WO2025174330A1
WO2025174330A1 PCT/TR2024/050115 TR2024050115W WO2025174330A1 WO 2025174330 A1 WO2025174330 A1 WO 2025174330A1 TR 2024050115 W TR2024050115 W TR 2024050115W WO 2025174330 A1 WO2025174330 A1 WO 2025174330A1
Authority
WO
WIPO (PCT)
Prior art keywords
ran
information
ues
node
policies
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
PCT/TR2024/050115
Other languages
English (en)
Inventor
Sultan ERTAŞ
Ahmet Cihat BAKTIR
Elham Dehghan Biyar
Yunus DÖNMEZ
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Telefonaktiebolaget LM Ericsson AB
Original Assignee
Telefonaktiebolaget LM Ericsson AB
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Telefonaktiebolaget LM Ericsson AB filed Critical Telefonaktiebolaget LM Ericsson AB
Priority to PCT/TR2024/050115 priority Critical patent/WO2025174330A1/fr
Publication of WO2025174330A1 publication Critical patent/WO2025174330A1/fr
Pending legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/70—Services for machine-to-machine communication [M2M] or machine type communication [MTC]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. Transmission Power Control [TPC] or power classes
    • H04W52/02—Power saving arrangements
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. Transmission Power Control [TPC] or power classes
    • H04W52/02—Power saving arrangements
    • H04W52/0203—Power saving arrangements in the radio access network or backbone network of wireless communication networks
    • H04W52/0206—Power saving arrangements in the radio access network or backbone network of wireless communication networks in access points, e.g. base stations
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. Transmission Power Control [TPC] or power classes
    • H04W52/02—Power saving arrangements
    • H04W52/0209—Power saving arrangements in terminal devices
    • H04W52/0251—Power saving arrangements in terminal devices using monitoring of local events, e.g. events related to user activity
    • H04W52/0258—Power saving arrangements in terminal devices using monitoring of local events, e.g. events related to user activity controlling an operation mode according to history or models of usage information, e.g. activity schedule or time of day

Definitions

  • Embodiments of the present disclosure relate to communication networks, and particularly to policies in network and enterprise domains.
  • Open Radio Access Network (O-RAN) WG1 recently initiated a work item regarding network energy saving use cases (see O-RAN Alliance document "O-RAN Network Energy Saving Use Cases Technical Report 2.0 (O-RAN. WG1. Network-Energy-Savings- Technical-Report-R003-v02.00),", 2023. [Available at This technical report discusses relevant counters and Key Performance Indicators (KPIs) for monitoring and reporting, requirements, key issues, use cases, solution deployment options, and potential impacts on interfaces.
  • KPIs Key Performance Indicators
  • Efficient wireless communication methods employed by smart devices aim to enhance the efficiency of their wireless communication technology by optimizing energy consumption.
  • embodiments of the present disclosure enable RAN energy-saving policies to be activated (i.e., configured) based on requirements and/or constraints of both network and industrial domains. As discussed above, this is beneficial, as existing systems may implement network-centric energy efficiency methodologies which do not consider the constraints and/or requirements of other domains (e.g., industry domains).
  • a RAN has a set of energy saving policies and provides connectivity to a plurality of UEs.
  • the plurality of UEs are for performing one or more operational tasks in an enterprise domain.
  • the method comprises obtaining, from the RAN, first information relating to the operation of the RAN.
  • the method further comprises obtaining, from the enterprise domain, second information relating to the one or more operational tasks.
  • the method further comprises, based on the first information and second information, selecting one or more of the set of energy saving policies for use by the RAN and determining a configuration of the selected one or more energy saving policies.
  • the node comprises processing circuitry configured to cause the node to obtain, from the RAN, first information relating to the operation of the RAN.
  • the processing circuitry is further configured to cause the node to obtain, from the enterprise domain, second information relating to the one or more operational tasks.
  • the processing circuitry is further configured to cause the node to, based on the first information and second information, select one or more of the set of energy saving policies for use by the RAN and determine a configuration of the selected one or more energy saving policies.
  • a node has a set of energy saving policies and provides connectivity to a plurality of UEs.
  • the plurality of UEs are for performing one or more operational tasks in an enterprise domain.
  • the node comprises processing circuitry configured to cause the node to perform a method according to the first aspect (or any embodiment thereof) and a method according to the second aspect (or any embodiment thereof).
  • a computer program product comprising a computer readable medium having computer readable code embodied therein, the computer readable code being configured such that, on execution by a suitable computer or processor, the computer or processor is caused to perform the method according to any of the above described aspects or any embodiment thereof.
  • Enhanced integration of 5G network into the enterprise domain can be implemented using methods of the present disclosure.
  • the energy saving policies to be applied in RAN can be selected and configured according to the requirements and constraints defined by operational processes in the enterprise domain (e.g., so that the energy saving policies may be optimized).
  • the operational processes (such as tasks performed at a factory floor) can be adjusted for further enhancement of energy saving in the RAN.
  • Information regarding these energy saving policies in the enterprise ecosystem, device behavior at the factory floor, network conditions and other related information can be aggregated in a single Energy Efficiency Module (EEM) for more efficient application of RAN energy efficiency measures.
  • EEM Energy Efficiency Module
  • embodiments of the present disclosure may incorporate AI/ML techniques to analyze information collected from different sources.
  • an AI/ML model can be used to determine an improved/optimal policy or set of policies to be recommended to the network while handling potential conflicts with the expectations and constraints of the enterprise domain.
  • enhanced energy efficiency in RAN might lead to a decreased performance in production in the enterprise domain.
  • Embodiments of the present disclosure are able to enable energy saving while meeting the expectations of prioritized production processes.
  • Figure 2 illustrates an ML model utilised in embodiments of the disclosure for determining configurations of energy saving policies
  • Figure 4 illustrates an ML model utilised in a first use case implementing embodiments of the disclosure
  • Figure 5 is a schematic diagram illustrating an operational policy generator according to embodiments of the disclosure.
  • Figure 8 is a flowchart showing a method in accordance with embodiments of the disclosure.
  • Embodiments of the present disclosure relate to methods for improving energy efficiency in a RAN.
  • some embodiments allow network energy saving policies to be optimised with the support of data provided by the enterprise domain (also referred to herein as being, or comprising, vertical domains, industrial domains, operational domains, etc).
  • the enterprise domain also referred to herein as being, or comprising, vertical domains, industrial domains, operational domains, etc.
  • energy saving policies can be configured such that they simultaneously meet the constraints of production processes in the enterprise domain and network performance requirements.
  • EEM Energy Efficiency Module
  • the EEM may be deployed in a RAN (e.g., as an individual network node or as part of O-RAN apparatus) or it may be deployed as an individual/separate network node outside of (but connected to) the RAN.
  • RAN e.g., as an individual network node or as part of O-RAN apparatus
  • O-RAN Operational Technology
  • embodiments of the EEM aggregate energy-related information from a network domain and an enterprise domain (e.g., a 5G system and an Operational Technology (OT) systems).
  • OT Operational Technology
  • the EEM may retrieve information from both network and enterprise domains through exposures, and store this information as historical data; and propose energy-saving policies to be applied in the RAN.
  • the EEM may then use AI/ML techniques to analyze the aggregated data (e.g., measurements, constraints, requirements) and to recommend candidate energy policies to be applied in the network.
  • the EEM may use a rulebased system to generate a set of operational policies (e.g., recommended operational policies) to be applied in the enterprise domain.
  • a set of operational policies e.g., recommended operational policies
  • FIG. 1 illustrates the operational framework of an EEM 100 according to embodiments of the present disclosure.
  • the EEM 100 may comprise an energy policy generator 102 and/or an operational policy generator 104.
  • the functionality of the energy policy generator 102 and the operational policy generator 104 is discussed in more detail below.
  • the EEM 100 may interact with the energy efficiency rApp 122 in both directions: (1) the energy efficiency rApp 122 may expose energy saving policies and relevant data to the EEM 100, and (2) the EEM 100 may provide new policy proposals for enhanced energy efficiency in RAN to the energy efficiency rApp 122.
  • Network traffic patterns e.g., peak usage times, locations with high demand, variations in traffic throughout the day, etc.
  • Device characteristics and condition monitoring e.g., UE ID, device task, task priority, device capabilities, battery status, battery lifetime, current position, etc.
  • SEC Specific Energy Consumption
  • the data preprocessing sub-module 112 may preprocess the data and/or store the data in a database.
  • tabular data in which data is stored as rows and columns. For example, each row may represent a particular sample, and each column may represent an input feature or output variable.
  • policies and related information provided by the 5G RAN 106 may be stored in the EEM 100 with one or more of the following attributes:
  • FIG. 3 illustrates a message flow according to embodiments of the present disclosure.
  • an EEM 302 is deployed as a network node and is capable of interacting with both a 5G network (in particular, and SMO 306 of the 5G network in which an energy control rApp 308 is deployed) and an industrial application 304.
  • step 310 the EEM 302 sends, to the industrial application 304, a request for information relating to devices and/or processes in the industrial application 304, such as associated tasks and priority (i.e., second information).
  • a request for information relating to devices and/or processes in the industrial application 304 such as associated tasks and priority (i.e., second information).
  • the EEM 302 sends the determined configuration of the one or more energy saving policies to the energy control rApp 308 for potential application in a RAN.
  • the determined configurations may be sent via the SMO 306 (step 324).
  • the first use case relates to smart manufacturing involving a mission critical loT service, such as remote control of UEs.
  • a mission critical loT service such as remote control of UEs.
  • performance measurements collected from lower layer nodes and connectivity requirements of the (smart manufacturing) production processes should be taken into consideration when determining the configuration of energy saving policies for application in the RAN. That is, in order to enable end-to-end automation for the smart manufacturing process, there should be smooth integration of the production processes and the 5G network, and energy saving policies in the RAN should not interfere with the production and asset management services of the smart manufacturing process.
  • a robot A and robot B controlled remotely by a human operator may have critical responsibility in the smart manufacturing process.
  • the energy saving policies implemented in the RAN may need to be activated/deactivated/configured according to the tasks and features of these robots. For example, the cells that provide connectivity for these robots may not be switched off if the production tasks associated with robots A and B are marked as “HIGH” priority. This is because, when a cell is switched off, the remote controlled UEs incorporated in robots A and B may be forced to handover, potentially causing short-term service disruptions due to unsuccessful handover. This can lead to operational failures, which is not acceptable in mission-critical scenarios.
  • the ML model of the energy policy generator may recommend energy saving policies to the RAN that result in decreased transmission (Tx) power of, or even to switch off, a cell corresponding to the location of a robot when input data indicates that robots covered by this cell have been assigned tasks with low priority.
  • Tx transmission
  • FIG 4 illustrates an ML model 400 (an embodiment of ML model 200 of Figure 2) outputting, in an output layer 402, a determined configuration of an energy saving policy when the input layer 404 of the ML model 400 comprises information indicating a task priority.
  • the input layer 404 may comprise information indicating a policy ID “switchOnOff’, a target RAN node “ru1”, a device capability “drill”, and a task priority “low”.
  • the first use case can be extended to consider different asset management tasks across the value chain in an Industry 4.0 environment, and should not be limited to considering only the performance expectations (e.g., latency) and/or priorities (e.g., 5G QoS Identifier (5QI)) assigned to traffic flow in an enterprise domain. Rather, the energy saving generator of embodiments of the present disclosure is also capable of considering business level expectations and other critical information about the enterprise domain.
  • performance expectations e.g., latency
  • priorities e.g., 5G QoS Identifier (5QI)
  • 5QI 5G QoS Identifier
  • the operational policy generator 104 functionality of the operational policy generator 104 is discussed below. It should be appreciated that, in embodiments of Figure 1 , the operational policy generator may be implemented in addition to the energy policy generator 102. In other embodiments, the operational policy generator 104 is implemented without the energy policy generator 102.
  • the EEM 100 uses the operational policy generator 104 to generate candidate operational policies that can be applied in the industrial application 108.
  • the operational policy generator 104 comprises a second data collection sub-module 116, a data preprocessing and root cause analysis (RCA) sub-module 118, and a rule-based policy generation sub-module 120.
  • the operational policy generator 104 uses these sub-modules to process collected data, execute a RCA process when an excessive energy consumption in RAN is detected, and propose operational policies that can be implemented by the industrial application 108 to increase energy saving in the 5G RAN 106 and/or industrial application 108.
  • Data from a 5G system 512 i.e., first information
  • data from an industrial domain 514 i.e., second information
  • the operational policy generator 104 receives data from a 5G system 512 and data from an industrial domain 514 and/or configuration settings.
  • the data from the 5G system 512 may comprise antenna measurements, policies, and/or configuration settings.
  • the data from the industrial domain 514 may comprise device characteristics, UE traffic patterns, and/or UE’s task-data relation.
  • Operational policies 516 are output from the operational policy generator 104, wherein the operational policies 516 may be implemented in the industrial application 108 in order to address a cause of energy consumption in the 5G RAN 106 and/or the industrial application 108.
  • the ML model 504 may be used to examine whether there are any other problems that are associated with the detected problem. For example, similar parameters and KPI values related to the detected problem may be examined using the ML model 504 to find out whether any abnormal behavior occurred or is occurring in the 5G RAN 106. Afterwards, the timeline correlation function 506 may find temporal correlations between each of the detected findings. To determine the root cause of the detected problem, the RCA function 508 reaches a conclusion with the findings (e.g., using unsupervised learning methods such as K-Means Clustering, Isolation forest and/or One Class Support Vector Machine). If the concluded root cause is satisfactory, this information is forwarded to the rule-based operation policy generator 510.
  • unsupervised learning methods such as K-Means Clustering, Isolation forest and/or One Class Support Vector Machine
  • the operational policy generator 104 repeats the operations of the ML model 504, the timeline correlation function 506, and the RCA function 508. Analysis tools and techniques, such as cause-and-effect diagrams or AI/ML methods, may be used by the operational policy generator 104 to identify the core causes of the problem.
  • operational policy generator 104 may create and prioritize a list of potential operational actions/policies that enable the detected problem to be avoided. For example, in some embodiments, a rule-based algorithm may be utilised, particularly when predefined problems and solutions are known to the rule-based operation policy generator 510. In other embodiments, an ML method that maps a solution to a problem may be used.
  • the generated operational policies 510 output from the operational policy generator 104 are shared with the industry application 108.
  • the industry application 108 may be advised to implement the operational policies 510.
  • the generated operational policies may relate to data link usage recommendations for the industrial application 108.
  • Steps 600 and 601 of Figure 6 correspond to steps 310 and 312 of Figure 3, in which: the EEM 302 sends, to the industrial application 304, a request for information relating to devices and/or processes in the industrial application 304, such as associated tasks and priority (i.e., second information); and the EEM 302 receives, from the industrial application 304, the requested information relating to devices and/or processes in the industrial application (i.e., the second information).
  • the EEM 302 sends, to the industrial application 304, a request for information relating to devices and/or processes in the industrial application 304, such as associated tasks and priority (i.e., second information)
  • the EEM 302 receives, from the industrial application 304, the requested information relating to devices and/or processes in the industrial application (i.e., the second information).
  • a second use case is discussed below, in which the EEM 100 uses an operational policy generator according to embodiments of the present disclosure to detect periodic power peaks in network provider data (occurring for some unknown reason) and to examine the power peaks relationship with the enterprise domain.
  • the adaptive transformation reduces unneeded power consumption by dynamically adjusting the Tx Power levels in response to the current environmental conditions.
  • the optimized data transmission scheme increases dependability because the transmitted packets are better adapted for the prevailing SNR levels, thereby decreasing the probability of errors and retransmissions.
  • the EEM 100 then communicates information relating to the problem and its resolution (i.e. , the data transformation schedule) to both the enterprise and network domains. It offers advice on the generated policy and seeks feedback from the service provider and enterprise domain regarding the solution report, advice, and root cause of the problem.
  • the resolution i.e. , the data transformation schedule
  • the EEM may restart the process discussed in relation to Figures 5 and 6 in order to generate new solutions and recommendations through continued communication with both domains.
  • AAS One of the key properties implemented by AAS is the concept of submodels, which characterize the asset by describing its aspects in different domains (e.g., communication, engineering, lifecycle status, asset functions).
  • An AAS instance includes active and passive parts. While the passive AAS includes various sub-models describing the asset itself, the active AAS implements relevant functions to interact with other AAS instances as well as make decisions based on the interactions.
  • a typical AAS implementation is expected to have an “Interaction Model” in the active part, which is responsible of communicating with the physical twin and other AAS instances.
  • the “Interaction Model” is extended to interact with an energy control rApp and industrial application, subscribing to the events, and fetching and storing relevant information in the corresponding submodels.
  • the active part 5G AAS can incorporate the energy policy generator 102 and operational policy generator 104 that use the information stored in a passive part (i.e., submodels) and generates output in the form of a recommended set of policies to the associated domains.
  • a RAN has a set of energy saving policies and provides connectivity to a plurality of UEs.
  • the plurality of UEs are for performing one or more operational tasks in an enterprise domain (e.g., industrial application 108, 304).
  • the node may be part of the RAN or the enterprise domain.
  • the method begins at step 802, with obtaining, from the RAN, first information relating to the operation of the RAN (e.g., via steps 314-320 of figure 3).
  • the first information may include one or more of: performance requirements of the RAN and/or the one or more UEs; and measurements collected in the RAN.
  • the virtualization environment 1100 includes components defined by the O-RAN Alliance, such as an O-Cloud environment orchestrated by a Service Management and Orchestration Framework via an O-2 interface.
  • a VM 1108 may be a software implementation of a physical machine that runs programs as if they were executing on a physical, non-virtualized machine.
  • Each of the VMs 1108, and that part of hardware 1104 that executes that VM be it hardware dedicated to that VM and/or hardware shared by that VM with others of the VMs, forms separate virtual network elements.
  • a virtual network function is responsible for handling specific network functions that run in one or more VMs 1108 on top of the hardware 1104 and corresponds to the application 1102.
  • Hardware 1104 may be implemented in a standalone network node with generic or specific components. Hardware 1104 may implement some functions via virtualization. Alternatively, hardware 1104 may be part of a larger cluster of hardware (e.g. such as in a data center or CPE) where many hardware nodes work together and are managed via management and orchestration 1110, which, among others, oversees lifecycle management of applications 1102.
  • hardware 1104 is coupled to one or more radio units that each include one or more transmitters and one or more receivers that may be coupled to one or more antennas. Radio units may communicate directly with other hardware nodes via one or more appropriate network interfaces and may be used in combination with the virtual components to provide a virtual node with radio capabilities, such as a radio access node or a base station.
  • some signaling can be provided with the use of a control system 1112 which may alternatively be used for communication between hardware nodes and radio units.
  • computing devices described herein may include the illustrated combination of hardware components, other embodiments may comprise computing devices with different combinations of components. It is to be understood that these computing devices may comprise any suitable combination of hardware and/or software needed to perform the tasks, features, functions and methods disclosed herein. Determining, calculating, obtaining or similar operations described herein may be performed by processing circuitry, which may process information by, for example, converting the obtained information into other information, comparing the obtained information or converted information to information stored in the network node, and/or performing one or more operations based on the obtained information or converted information, and as a result of said processing making a determination.
  • processing circuitry may process information by, for example, converting the obtained information into other information, comparing the obtained information or converted information to information stored in the network node, and/or performing one or more operations based on the obtained information or converted information, and as a result of said processing making a determination.
  • computing devices may comprise multiple different physical components that make up a single illustrated component, and functionality may be partitioned between separate components.
  • a communication interface may be configured to include any of the components described herein, and/or the functionality of the components may be partitioned between the processing circuitry and the communication interface.
  • non-computationally intensive functions of any of such components may be implemented in software or firmware and computationally intensive functions may be implemented in hardware.

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  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

L'invention concerne un procédé mis en œuvre par un nœud qui est fourni à un réseau d'accès radio (RAN) ayant un ensemble de politiques d'économie d'énergie et fournissant une connectivité à une pluralité d'équipements utilisateurs (UE). La pluralité d'UE sont destinés à effectuer une ou plusieurs tâches opérationnelles dans un domaine d'entreprise. Le procédé consiste à obtenir, à partir du RAN, des premières informations relatives au fonctionnement du RAN. Le procédé consiste en outre à obtenir, à partir du domaine d'entreprise, des secondes informations concernant la ou les tâches opérationnelles. Sur la base des premières informations et des secondes informations, le nœud sélectionne une ou plusieurs politiques de l'ensemble de politiques d'économie d'énergie destinées à être utilisées par le RAN et détermine une configuration de la ou des politiques d'économie d'énergie sélectionnées.
PCT/TR2024/050115 2024-02-14 2024-02-14 Politiques dans des domaines de réseau et d'entreprise Pending WO2025174330A1 (fr)

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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2023273669A1 (fr) * 2021-06-30 2023-01-05 华为技术有限公司 Procédé et appareil de configuration d'économie d'énergie
WO2023059882A1 (fr) * 2021-10-08 2023-04-13 Interdigital Patent Holdings, Inc. Procédé d'économie d'énergie pour relais de wtru-à-réseau
WO2023113674A1 (fr) 2021-12-16 2023-06-22 Telefonaktiebolaget Lm Ericsson (Publ) Fonctionnement d'équipement utilisateur (ue) avec configuration d'économie d'énergie de station de base

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2023273669A1 (fr) * 2021-06-30 2023-01-05 华为技术有限公司 Procédé et appareil de configuration d'économie d'énergie
WO2023059882A1 (fr) * 2021-10-08 2023-04-13 Interdigital Patent Holdings, Inc. Procédé d'économie d'énergie pour relais de wtru-à-réseau
WO2023113674A1 (fr) 2021-12-16 2023-06-22 Telefonaktiebolaget Lm Ericsson (Publ) Fonctionnement d'équipement utilisateur (ue) avec configuration d'économie d'énergie de station de base

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