Systems and methods for optimizing carrier and / or cell on / off switching in a communication network

Abstract

A system and method for implementing optimization of carrier and / or cell on / off switching by an SMO framework in an O-RAN, the method comprising: collecting O1-related data providing an O1 configuration required to perform on / off switching of cells and / or carriers; re-training an AI / ML model based on the collected O1-related data; deploying and activating one re-trained AI / ML model for inferring data providing an O1 configuration required to perform on / off switching of cells and / or carriers within the O-RAN; monitoring O1-related data providing an O1 configuration required to perform on / off switching of cells and / or carriers; evaluating O1-related data providing an O1 configuration required to perform on / off switching of cells and / or carriers; determining to generate O1 configuration data for preparing and performing on / off switching of cells and / or carriers and sending it to an E2 node; and implementing on / off switching of cells and / or carriers within the O-RAN.

Description

Technical Field

[0001] Systems and methods consistent with embodiments of the present disclosure relate to the generation and deployment of optimizations for stopping and starting carriers and / or cells to conserve energy in a communication network.

Background Art

[0002] A radio access network (RAN) is an important component in a communication system that connects end-user devices (or user equipment) to other parts of the network. The RAN includes a combination of various network elements (NEs) that connect end-user devices to the core network. Conventionally, the hardware and / or software of a particular RAN was vendor-specific.

[0003] With the advent of open RAN (O-RAN) technology, multiple vendors can provide hardware and / or software for a communication system. For this purpose, O-RAN decomposes RAN functions into a central unit (CU), a distributed unit (DU), and a radio unit (RU). The CU is a logical node for hosting RAN sub-layers of radio resource control (RRC), service data adaptation protocol (SDAP), and / or packet data convergence protocol (PDCP). The DU is a logical node for hosting RAN sub-layers of radio link control (RLC), media access control (MAC), and physical (PHY). The RU is a physical node that converts radio signals from an antenna into digital signals that can be transmitted to the DU over a fronthaul. Since these entities have open protocols and interfaces between them, they can be developed by different vendors.

[0004] FIG. 1 illustrates an O-RAN architecture in the related art. Referring to FIG. 1, the RAN functions in the O-RAN architecture are controlled and optimized by the RIC. The RIC is a software-defined component that implements modular applications for realizing the multi-vendor operability required in the O-RAN system and automating and optimizing RAN operations. The RIC is divided into two types: non-real-time RIC (NRT-RIC) and near-real-time RIC (nRT-RIC).

[0005] The NRT-RIC is a control point of a non-real-time control loop and operates on a time scale longer than 1 second within the service management and orchestration (SMO) framework. Its functions are implemented through modular applications called rApps (rApp 1, …, rApp N), providing policy-based guidance and enrichment across the A1 interface, which is an interface that enables communication between the NRT-RIC and the nRT-RIC, performing data analytics, artificial intelligence / machine learning (AI / ML) training and inference for RAN optimization, and / or recommending configuration management actions on the O1 interface, which is an interface that connects the SMO to RAN management elements (e.g., nRT-RIC, O-RAN centralized unit (O-CU), O-RAN distributed unit (O-DU), etc.).

[0006] The nRT-RIC operates on time scales between 10 milliseconds and 1 second and connects via the E2 interface to the O-DU, the O-CU (which is decomposed into an O-CU control plane (O-CU-CP) and an O-CU user plane (O-CU-UP)), and the open evolved NodeB (O-eNB). The nRT-RIC uses the E2 interface to control the underlying RAN elements (E2 nodes / network functions (NFs)) on a near-real-time control loop. The nRT-RIC monitors, suspends / stops, overrides, and controls the E2 nodes (O-CU, O-DU, and O-eNB) via policies. For example, the nRT-RIC sets policy parameters on the functions activated in the E2 nodes. Further, the nRT-RIC hosts xApps for implementing functions such as quality of service (QoS) optimization, mobility optimization, slicing optimization, interference mitigation, load balancing, and security. Two types of RICs cooperate to optimize O-RAN. For example, the NRT-RIC provides on the A1 interface the policies, data, and artificial intelligence / machine learning (AI / ML) models that are enabled and used by the nRT-RIC for RAN optimization, and the nRT-RIC returns policy feedback (i.e., how the policies set by the NRT-RIC work).

[0007] The SMO framework in which the NRT-RIC is located manages and coordinates the RAN elements. Specifically, the SMO manages and coordinates what is represented as the O-RAN cloud (O-Cloud). The O-Cloud is a set of physical RAN nodes that host the RIC, the O-CU, and the O-DU, support software components (e.g., operating systems and runtime environments), and the SMO itself. In other words, the SMO manages the O-Cloud from within. The O2 interface is the interface between the SMO and the O-Cloud in which it resides. The SMO provides infrastructure management services (IMS) and deployment management services (DMS) via the O2 interface.

[0008] On the one hand, O-Cloud is a cloud computing platform that comprises a set of physical infrastructure nodes that meet the O-RAN specifications for hosting related O-RAN functions (e.g., nRT-RIC, O-CU-CP, O-CU-UP, O-DU, etc.), support software components (operating system, virtual machine monitor, container runtime, etc.), and appropriate management and orchestration functions.

[0009] The SMO framework where the NRT-RIC is located manages and coordinates the RAN elements. The SMO performs the management and orchestration of the RAN elements through four key interfaces (the A1 interface for RAN optimization between the NRT-RIC and nRT-RIC in the SMO; the O1 interface for FCAPS support between the SMO and the O-RAN network functions; in the case of the hybrid model, the open front haul M plane interface for FCAPS support between the SMO and the O-RU; the O2 interface for platform resource and workload management between the SMO and the O-Cloud).

[0010] In related technologies, O-RAN utilizes multiple frequency layers (carriers) to cover a service area. When the network traffic load is low (e.g., when the expected traffic volume is lower than a fixed threshold), energy savings (i.e., high energy efficiency and / or low energy consumption) can be achieved by stopping (i.e., switching off) one or more carriers or the entire cell without degrading the O-RAN user experience. When stopping (i.e., switching off) one or more carriers or the entire cell, the O-RAN users (i.e., user equipment (UE) within O-RAN) previously served by one or more carriers or the cell are offloaded by the E2 node to one or more new target carriers or cells before shutdown (i.e., switching off).

Summary of the Invention

Problems to be Solved by the Invention

[0011] From the perspective of the local or network-wide impact of shutdown in O-RAN, there is a trade-off between system performance and energy conservation. This trade-off makes the decision about switching off or on a non-trivial task. For example, other carriers and / or cells may have to cover (i.e., take over or serve) additional network traffic. Here, the network traffic changes over time. Furthermore, the E2 node may have to support some technologies that can affect energy consumption and may depend on the load (e.g., network traffic or the number of users).

[0012] As a result, even if the energy conservation for one or more carriers and / or entire cells that can be switched off is locally maximized, the overall energy consumption of the O-RAN network may increase. In this case, the local deployment of the energy-saving optimization process based on the on / off switching of carriers and / or cells leads to a deterioration in the overall energy efficiency of the network and / or the energy consumption of the O-RAN.

Means for Solving the Problems

[0013] According to an embodiment, a system and method for implementing a Service Management and Orchestration (SMO) framework are provided. The SMO framework includes a Non-Real-Time RAN Intelligent Controller (NRT-RIC), an NRT-RIC framework, at least one SMO function, and an rApp hosted by the NRT-RIC. The rApp generates and implements O1 configuration data for preparing and executing the on / off switching of cells and / or carriers by one or more E2 nodes. With the assistance of Artificial Intelligence / Machine Learning (AI / ML) technologies, the O1-related data from the E2 nodes (O-RUs) is used for re-training, deploying, and activating an AI / ML model to infer data that provides the O1 configuration required to perform the on / off switching of cells and / or carriers within the O-RAN. The on / off switching control of carriers and cells (i.e., monitoring at least one O1-related data that provides the O1 configuration required to perform the on / off switching of cells and / or carriers through the R1 interface via the NRT-RIC framework and via the O1 interface through at least one SMO function within the SMO framework) takes into account the overall energy efficiency of the network instead of local optimization in the O-RAN.

[0014] For example, the functionality of the AI / ML model may include predictions of future traffic, user mobility, and resource usage, and may also predict expected energy efficiency improvements, resource usage, and network performance for different energy savings optimization states.

[0015] As a result, the system and method implement an NRT-RIC framework that enables a network operator to flexibly configure carrier and / or cell on / off switching parameters in a cell or cluster of cells to optimize the energy efficiency of the entire network, rather than local optimizations in O-RAN.

[0016] According to an embodiment, in an Open Radio Access Network (O-RAN), a system is provided for implementing optimization of on / off switching of carriers and / or cells by a Service Management and Orchestration (SMO) framework. The system includes a memory for storing instructions, and at least one processor configured to implement a Non-Real-Time RAN Intelligent Controller (NRT-RIC), an NRT-RIC framework, at least one SMO function, and an rApp hosted by the NRT-RIC.At least one processor collects, via the R1 interface through the NRT-RIC framework and via the O1 interface through the SMO function within the SMO framework, O1-related data provided by the rApp for performing the on / off switching of cells and / or carriers, the O1-related data being collected via the open front-haul management plane (FH M-Plane) interface between the E2 node and the open radio unit (O-RU). The SMO retrains at least one artificial intelligence / machine learning (AI / ML) model based on the collected O1-related data. The rApp deploys and activates one retrained AI / ML model for inferring data that provides the O1 configuration required for performing the on / off switching of cells and / or carriers within the O-RAN among at least one of the retrained AI / MLs. The rApp monitors the O1-related data that provides the O1 configuration required for performing the on / off switching of cells and / or carriers via the R1 interface through the NRT-RIC framework and via the O1 interface through at least one SMO function within the SMO framework. The rApp evaluates the O1-related data that provides the O1 configuration required for performing the on / off switching of cells and / or carriers. The rApp determines to generate O1 configuration data for preparing and performing the on / off switching of cells and / or carriers. The rApp sends, via the R1 interface through the NRT-RIC framework and via the O1 interface through at least one SMO function within the SMO framework, the O1 configuration data for preparing and performing the on / off switching of cells and / or carriers to at least one E2 node. The E2 node and the O-RU are configured to execute instructions to implement the on / off switching of cells and / or carriers within the O-RAN.At least one processor is further configured such that, during implementation, the E2 node converts O1 configuration data for preparing and performing on / off switching of cells and / or carriers, and the E2 node instructs the O-RU to perform on / off switching of cells and / or carriers via the open FH M-Plane.

[0017] At least one processor is further configured such that, during retraining of at least one AI / ML model, the rApp selects one AI / ML model from a plurality of AI / ML models, the rApp sends a start request for retraining the AI / ML model to the NRT-RIC framework, the NRT-RIC framework retrains the AI / ML model, the rApp monitors the retrained AI / ML model parameters, determines the retrieval of the retrained AI / ML model from the NRT-RIC framework based on the retrained AI / ML model parameters, the rApp requests the retrained AI / ML model from the NRT-RIC framework, and the NRT-RIC framework sends the retrained AI / ML model to the rApp.

[0018] At least one processor may be further configured such that, during retraining of at least one AI / ML model, the rApp retrains one AI / ML model from a plurality of AI / ML models.

[0019] The O1-related data that provides the O1 configuration required to perform cell and / or carrier on / off switching may include at least one of configuration, performance indicators, and measurement reports provided by the O-RU. The measurement reports may include at least one of cell load-related information, traffic information, and energy efficiency / energy consumption (EE / EC) measurement reports. The energy efficiency / energy consumption (EE / EC) measurement reports may include at least one of the energy consumption of the E2 node, the energy consumption of the O-RU, and at least one of one or more performance-related KPIs (Key Performance Indicators) of the E2 node.

[0020] At least one processor sends an O1-related data collection request to the E2 node for collecting O1-related data that provides the O1 configuration required to perform cell and / or carrier on / off switching, by the rApp, through the NRT-RIC framework via the R1 interface, and through the SMO function in the SMO framework via the O1 interface. The E2 node receives the O1-related data collection request from the SMO function. The E2 node collects O1-related data that provides the O1 configuration required to perform cell and / or carrier on / off switching from the O-RU via the open front-haul management plane (FH M-Plane) interface between the E2 node and the open radio unit (O-RU). The E2 node sends the O1-related data that provides the O1 configuration required to perform cell and / or carrier on / off switching, collected via the SMO function in the SMO framework and the O1 interface through the NRT-RIC framework, to the rApp via the R1 interface. It may be configured as such.

[0021] At least one processor, while instructing the O-RU to perform on / off switching of cells and / or carriers via the open FH M-Plane, is further configured such that the O-RU notifies the E2 node of the completion of the implementation of the on / off switching of cells and / or carriers via the FH M-Plane interface between the E2 node and the O-RU, and the E2 node notifies the rApp of the completion of the implementation of the on / off switching of cells and / or carriers via the O1 interface through the SMO function and via the R1 interface through the NRT-RIC framework within the SMO framework.

[0022] At least one processor is further configured such that the NRT-RIC monitors the performance of the retrained AI / ML model, determines that a predetermined performance target is not achieved based on the collected O1-related data, and initiates a fallback mechanism and / or an update or retraining of the AI / ML model.

[0023] According to an embodiment, in an Open Radio Access Network (O-RAN), a method for implementing optimization of on / off switching of carriers and / or cells by a Service Management and Orchestration (SMO) framework is provided.The method includes: the rApp collecting O1-related data via the open front-haul management plane (FH M-Plane) interface between the E2 node and the open radio unit (O-RU), which is the O1 configuration required for the rApp to perform on / off switching of cells and / or carriers, and collecting the O1-related data from the E2 node via the R1 interface through the NRT-RIC framework and via the O1 interface through the SMO function in the SMO framework; the SMO retraining at least one artificial intelligence / machine learning (AI / ML) model based on the collected O1-related data; the rApp deploying and activating one retrained AI / ML model for inferring data that provides the O1 configuration required for performing on / off switching of cells and / or carriers within O-RAN among at least one retrained AI / ML; the rApp monitoring the O1-related data that provides the O1 configuration required for performing on / off switching of cells and / or carriers via the R1 interface through the NRT-RIC framework and via the O1 interface through at least one SMO function in the SMO framework; the rApp evaluating the O1-related data that provides the O1 configuration required for performing on / off switching of cells and / or carriers; the rApp determining to generate O1 configuration data for preparing and performing on / off switching of cells and / or carriers; the rApp sending the O1 configuration data for preparing and performing on / off switching of cells and / or carriers to at least one E2 node via the R1 interface through the NRT-RIC framework and via the O1 interface through at least one SMO function in the SMO framework; and the E2 node and the O-RU implementing on / off switching of cells and / or carriers within O-RAN.Implementing may include the E2 node converting O1 configuration data for preparing and performing on / off switching of cells and / or carriers, and the E2 node instructing the O-RU to perform on / off switching of cells and / or carriers via the open FH M-Plane.

[0024] Retraining at least one AI / ML model may include the rApp selecting one AI / ML model from a plurality of AI / ML models, the rApp sending a start request for retraining the AI / ML model to the NRT-RIC framework, the NRT-RIC framework retraining the AI / ML model, the rApp monitoring the retrained AI / ML model parameters and determining the retrieval of the retrained AI / ML model from the NRT-RIC framework based on the retrained AI / ML model parameters, the rApp requesting the retrained AI / ML model from the NRT-RIC framework, and the NRT-RIC framework sending the retrained AI / ML model to the rApp.

[0025] Retraining at least one AI / ML model may include the rApp retraining one AI / ML model from a plurality of AI / ML models.

[0026] The O1-related data providing the O1 configuration necessary to perform the on / off switching of cells and / or carriers may include at least one of configuration, performance indicators, and measurement reports provided by the O-RU. The measurement report may include at least one of cell load-related information, traffic information, and energy efficiency / energy consumption (EE / EC) measurement reports. The energy efficiency / energy consumption (EE / EC) measurement report may include at least one of the energy consumption of the E2 node, the energy consumption of the O-RU, and one or more performance-related KPIs (Key Performance Indicators) of the E2 node.

[0027] Collecting the O1-related data providing the O1 configuration necessary to perform the on / off switching of cells and / or carriers may involve the rApp sending an O1-related data collection request to the E2 node through the NRT-RIC framework via the R1 interface and via the O1 interface through the SMO function within the SMO framework, the E2 node receiving the O1-related data collection request from the SMO function, the E2 node collecting the O1-related data providing the O1 configuration necessary to perform the on / off switching of cells and / or carriers from the O-RU via the open front-haul management plane (FH M-Plane) interface between the E2 node and the open radio unit (O-RU), and the E2 node sending the O1-related data providing the O1 configuration necessary to perform the on / off switching of cells and / or carriers collected via the O1 interface through the SMO function within the SMO framework and the NRT-RIC framework to the rApp via the R1 interface.

[0028] Instructing the O-RU to perform on / off switching of cells and / or carriers may further include the O-RU notifying the E2 node of the completion of the implementation of the on / off switching of cells and / or carriers via the FH M-Plane interface between the E2 node and the O-RU, and the E2 node notifying the rApp of the completion of the implementation of the on / off switching of cells and / or carriers via the O1 interface through the SMO function and via the R1 interface through the NRT-RIC framework within the SMO framework.

[0029] The method may further include the NRT-RIC monitoring the performance of the retrained AI / ML model, determining that a predetermined performance target is not achieved based on the collected O1-related data, and initiating a fallback mechanism and / or an update or retraining of the AI / ML model.

[0030] According to an embodiment, in an Open Radio Access Network (O-RAN), a non-transitory computer-readable recording medium having recorded thereon instructions executable by at least one processor configured to implement a non-real-time RAN intelligent controller (NRT-RIC), an NRT-RIC framework, at least one SMO function, and an rApp hosted by the NRT-RIC is provided to implement optimization of on / off switching of carriers and / or cells by a service management and orchestration (SMO) framework.The method includes: the rApp collecting O1-related data for providing an O1 configuration necessary for performing on / off switching of cells and / or carriers by the rApp, the O1-related data being collected via an Open Front-Haul Management Plane (FH M-Plane) interface between an E2 node and an Open Radio Unit (O-RU), from the E2 node through the NRT-RIC framework via an R1 interface and through an SMO function within the SMO framework via an O1 interface; the SMO re-training at least one Artificial Intelligence / Machine Learning (AI / ML) model based on the collected O1-related data; the rApp deploying and activating one re-trained AI / ML model for inferring data for providing an O1 configuration necessary for performing on / off switching of cells and / or carriers within O-RAN among at least one of the re-trained AI / MLs; the rApp monitoring O1-related data for providing an O1 configuration necessary for performing on / off switching of cells and / or carriers; the rApp evaluating O1-related data for providing an O1 configuration necessary for performing on / off switching of cells and / or carriers; the rApp determining to generate O1 configuration data for preparing and performing on / off switching of cells and / or carriers; the rApp sending the O1 configuration data for preparing and performing on / off switching of cells and / or carriers to at least one E2 node through the NRT-RIC framework via an R1 interface and through at least one SMO function within the SMO framework via an O1 interface; and the E2 node and the O-RU implementing on / off switching of cells and / or carriers within O-RAN.Implementing may include the E2 node converting O1 configuration data for preparing and performing on / off switching of cells and / or carriers, and the E2 node instructing the O-RU to perform on / off switching of cells and / or carriers via the open FH M-Plane.

[0031] Retraining at least one AI / ML model may include the rApp retraining one AI / ML model from a plurality of AI / ML models.

[0032] O1-related data providing the O1 configuration necessary to perform on / off switching of cells and / or carriers may include at least one of configuration, performance indicators, and measurement reports provided by the O-RU. The measurement report may include at least one of cell load-related information, traffic information, and energy efficiency / energy consumption (EE / EC) measurement reports. The energy efficiency / energy consumption (EE / EC) measurement report may include at least one of the energy consumption of the E2 node, the energy consumption of the O-RU, and one or more performance-related KPIs (Key Performance Indicators) of the E2 node.

[0033] Collecting O1-related data required to perform on / off switching of cells and / or carriers may include the rApp sending an O1-related data collection request to the E2 node through the NRT-RIC framework via the R1 interface and through the SMO function within the SMO framework via the O1 interface, the E2 node receiving the O1-related data collection request from the SMO function, the E2 node collecting O1-related data providing the O1 configuration required to perform on / off switching of cells and / or carriers from the O-RU via the open front-haul management plane (FH M-Plane) interface between the E2 node and the open radio unit (O-RU), and the E2 node sending the O1-related data providing the O1 configuration required to perform on / off switching of cells and / or carriers collected via the O1 interface through the SMO function and the NRT-RIC framework within the SMO framework to the rApp via the R1 interface.

[0034] Instructing the O-RU to perform on / off switching of cells and / or carriers may further include the O-RU notifying the E2 node of the completion of the implementation of on / off switching of cells and / or carriers via the FH M-Plane interface between the E2 node and the O-RU, and the E2 node notifying the rApp of the completion of the implementation of on / off switching of cells and / or carriers via the O1 interface through the SMO function and via the R1 interface through the NRT-RIC framework within the SMO framework.

[0035] The method may further include monitoring, by the NRT-RIC, the performance of the retrained AI / ML model, determining that a predetermined performance target is not achieved based on the collected O1-related data, and initiating a fallback mechanism and / or an update or retraining of the AI / ML model.

[0036] Additional aspects may be partially presented in the following description, may be partially apparent from the description, or may be realized by the practice of the disclosed embodiments.

Brief Description of the Drawings

[0037] Features, aspects, and advantages of specific exemplary embodiments of the disclosure are described below with reference to the accompanying drawings in which like reference numerals represent like elements.

[0038] FIG. 1 illustrates an O-RAN architecture in the related art.

[0039] FIG. 2 is a diagram of an example environment in which the systems and / or methods described herein may be implemented.

[0040] FIG. 3 is a diagram of an example of components of a device according to an embodiment.

[0041] FIG. 4 illustrates an NRT-RIC framework within an O-RAN according to an embodiment.

[0042] FIG. 5 is a flowchart of a method for implementing optimization of on / off switching of carriers and / or cells according to an embodiment.

[0043] FIG. 6 illustrates a data collection flow according to an embodiment.

[0044] FIG. 7 illustrates a data analysis, training, and inference flow of an AI / ML model according to an embodiment.

[0045] FIG. 8 illustrates data analysis, AI / ML model training, and inference flows according to other embodiments.

[0046] FIG. 9 illustrates the generation and implementation of O1 configuration data for preparing and performing on / off switching of cells and / or carriers according to one embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0047] The following detailed description of the embodiments refers to the accompanying drawings. The foregoing disclosure provides illustration and description, but is not intended to be exhaustive or to limit the implementation to the exact forms disclosed. Changes and modifications are possible in light of the foregoing disclosure or may be obtained from practice of the implementation. Additionally, one or more features or components of one embodiment may be integrated with or combined with those of other embodiments (or one or more features of other embodiments). Further, in the flowcharts and operation descriptions provided below, one or more operations may be omitted, one or more operations may be added, one or more operations may be executed simultaneously (at least in part), and the order of one or more operations may be interchanged.

[0048] It will be apparent that the systems and / or methods described herein may be implemented in different forms of hardware, firmware, or a combination of hardware and software. The actual special control hardware or software code used to implement these systems and / or methods is not a limitation of the implementation. For this reason, the operations and behaviors of the systems and / or methods are described herein without reference to specific software code. It is understood that software and hardware may be designed based on the description herein to implement the systems and / or methods.

[0049] Even if a particular combination of features is recited in a claim and / or disclosed in the specification, these combinations are not intended to limit the disclosure of possible implementations. In fact, many of these features may be combined in ways different from those specifically recited in the claims and / or specifically disclosed in the specification. Each of the dependent claims listed below may depend directly on only one claim, but the disclosure of possible implementations includes each dependent claim in combination with all other claims in the claim set.

[0050] None of the elements, acts, or instructions used herein should be construed as important or essential unless explicitly described as such. Also, as used herein, the articles "a" and "an" are intended to include one or more items and may be used interchangeably with "one or more". Where only one item is intended, the term "one" or a similar term is used. Also, as used herein, the terms "has", "have", "having", "include", "including", etc. are intended to be open-ended terms. Further, the phrase "based on" is intended to mean "at least in part, based on" unless explicitly stated otherwise. Further, expressions such as "at least one of A and B" or "at least one of A or B" are understood to include only A, only B, or both A and B.

[0051] Embodiments of the present disclosure provide a system and method in which an NRT-RIC framework and / or an rApp constructs (i.e., at least one rApp hosted by the NRT-RIC and the NRT-RIC framework consumes and / or generates O1-related services that construct O1-related data providing the O1 configuration necessary to perform on / off switching of a cell and / or a carrier, i.e., the on / off switching parameters of a cell and / or a carrier). For example, the NRT-RIC framework and / or an rApp (e.g., at least one rApp hosted by the NRT-RIC and / or the NRT-RIC framework) enables flexible configuration of on / off switching parameters of a carrier and / or a cell in a cell or a cluster of cells by an A1 policy or an optimization trigger on the O1 interface for the nRT-RIC defined by the NRT-RIC (i.e., by at least one rApp hosted by the NRT-RIC and / or the NRT-RIC framework assisted by machine learning (ML) techniques). Actions of the nRT-RIC via the E2 interface may enable the deployment of O1 configuration data to prepare and perform on / off switching of a cell and / or a carrier for one or more E2 nodes. The implementation based on the O1 configuration data for preparing and performing on / off switching of a cell and a carrier in the O-RU is initiated by the E2 node via the open FH M-Plane interface between the E2 node and the O-RU.

[0052] For this purpose, before stopping (i.e., switching off) one or more carriers and / or cells, the E2 node needs to perform a preparation action for stopping (i.e., switching off) one or more carriers and / or cells (e.g., the E2 node may check for ongoing emergency calls and / or warning messages that should be handled (e.g., enabled, disabled, modified, etc.) in carrier aggregation and / or dual connectivity, such as triggering the migration of UEs from high occupancy (HO) data traffic and one or more cells and / or carriers to other cells or carriers, informing neighboring nodes via the X2 / Xn interface, etc.).

[0053] Furthermore, before switching on one or more carriers and / or cells, the E2 node needs to perform a preparation action for switching on (e.g., the E2 node may perform cell probing, notification to neighboring nodes via the X2 / Xn interface, etc.).

[0054] FIG. 2 is a diagram of an example of an environment 200 in which the systems and / or methods described herein may be implemented. As shown in FIG. 2, the environment 200 may include user devices 210, a platform 220, and a network 230. The devices of the environment 200 may be interconnected via a wired connection, a wireless connection, or a combination of wired and wireless connections. In embodiments, any of the functions and operations described above with reference to FIG. 1 may be performed by any combination of the elements illustrated in FIG. 2.

[0055] The user device 210 includes one or more devices capable of receiving, generating, storing, processing, and / or providing information related to the platform 220. For example, the user device 210 may include a computing device (e.g., a desktop computer, a laptop computer, a tablet computer, a handheld computer, a smart speaker, a server, etc.), a mobile phone (e.g., a smartphone, a wireless phone, etc.), a wearable device (e.g., smart glasses or a smartwatch), or a similar device. In some implementations, the user device 210 may receive information from the platform 220 and / or transmit information to the platform 220.

[0056] The platform 220 includes one or more devices capable of receiving, generating, storing, processing, and / or providing information. In some implementations, the platform 220 may include a cloud server or a group of cloud servers. In some implementations, the platform 220 may be designed to be modular such that specific software components may be swapped (in or out) according to specific needs. Thus, the platform 220 may be easily and / or quickly reconfigured for different uses.

[0057] In some implementations, as shown, the platform 220 may be hosted in a cloud computing environment 222. Note that the implementations described herein describe the platform 220 as being hosted in the cloud computing environment 222, but in some implementations, the platform 220 may not be cloud-based (i.e., may be implemented outside of a cloud computing environment) or may be partially cloud-based.

[0058] The cloud computing environment 222 includes an environment that hosts the platform 220. The cloud computing environment 222 may provide services that do not require the end user (e.g., user device 210) knowledge of the physical location and configuration of the system and / or device that hosts the platform 220, such as computing, software, data access, storage, etc. As shown, the cloud computing environment 222 may include a group of computing resources 224 (collectively referred to as "computing resources 224" and individually referred to as "computing resource 224").

[0059] The computing resources 224 include one or more personal computers, clusters of computing devices, workstation computers, server devices, or other types of computing and / or communication devices. In some implementations, the computing resources 224 may host the platform 220. Cloud resources may include computing instance running in the computing resources 224, storage device provided in the computing resources 224, data transfer device provided by the computing resources 224, etc. In some implementations, the computing resources 224 may communicate with other computing resources 224 via a wired connection, a wireless connection, or a combination of wired and wireless connections.

[0060] As further shown in FIG. 2, the computing resources 224 include a group of cloud resources such as one or more applications ("APP") 224-1, one or more virtual machines ("VM") 224-2, virtualized storage ("VS") 224-3, one or more hypervisors ("HYP") 224-4, etc.

[0061] Application 224-1 includes one or more software applications that may be provided to or accessed by user device 210. Application 224-1 may obviate the need to install and execute software applications on user device 210. For example, Application 224-1 may include software associated with platform 220 and / or any other software that may be provided via cloud computing environment 222. In some implementations, one Application 224-1 may communicate information with one or more other Applications 224-1 via virtual machine 224-2.

[0062] Virtual machine 224-2 includes a software implementation of a device (e.g., a computer) that executes programs like a physical device. Virtual machine 224-2 may be a system virtual machine or a process virtual machine, depending on its use by virtual machine 224-2 and its correspondence to any real device. A system virtual machine may provide a complete system platform that supports the execution of a full operating system ("OS"). A process virtual machine may execute a single program and may support a single process. In some implementations, virtual machine 224-2 may execute on behalf of a user (e.g., user device 210) and may manage the infrastructure of cloud computing environment 222, such as data management, synchronization, or long-duration data transfer.

[0063] The virtualized storage 224-3 includes one or more storage systems and / or devices of one or more devices or computing resources 224 that use virtualization technology within the storage system. In some implementations, within the context of the storage system, the types of virtualization may include block virtualization and file virtualization. Block virtualization may represent the abstraction (or separation) of logical storage from physical storage so that the storage system may be accessed without considering the physical storage or heterogeneous structure. The separation may provide flexibility to the storage system administrator when managing storage for end users. File virtualization may remove the dependency between the data accessed at the file level and the location where the files are physically stored. This may enable optimization of storage usage, server consolidation, and / or performance of non-disruptive file migration.

[0064] The hypervisor 224-4 may provide hardware virtualization technology that enables multiple operating systems (e.g., "guest operating systems") to run simultaneously on a host computer such as computing resources 224. The hypervisor 224-4 may present a virtual operating platform to the guest operating systems and may manage the execution of the guest operating systems. Multiple instances of various operating systems may share the virtualized hardware resources.

[0065] Network 230 includes one or more wired and / or wireless networks. For example, Network 230 may include a cellular network (e.g., a fifth-generation (5G) network, a long-term evolution (LTE) network, a third-generation (3G) network, a code division multiple access (CDMA) network, etc.), a public land mobile network (PLMN), a local area network (LAN), a wide area network (WAN), a metropolitan area network (MAN), a telephone network (e.g., a Public Switched Telephone Network (PSTN), a private network, an ad hoc network, an intranet, the Internet, a fiber-optic based network, etc.), and / or a combination of these or other types of networks.

[0066] The number and arrangement of the devices and networks shown in FIG. 2 are provided as an example. In fact, there may be additional devices and / or networks, fewer devices and / or networks, different devices and / or networks, or devices and / or networks with different arrangements compared to those shown in FIG. 2. Further, two or more devices shown in FIG. 2 may be implemented within a single device, and a single device shown in FIG. 2 may be implemented as a plurality of distributed devices. Additionally or alternatively, a set of devices in Environment 200 (e.g., one or more devices) may perform one or more functions described as being performed by another set of devices in Environment 200.

[0067] Figure 3 is a diagram of an example of components of device 300. Device 300 may correspond to user device 210 and / or platform 220. As shown in FIG. 3, device 300 may include bus 310, processor 320, memory 330, storage component 340, input component 350, output component 360, and communication interface 370.

[0068] Bus 310 includes components that enable communication between components of device 300. Processor 320 may be implemented in hardware, firmware, or a combination of hardware and software. Processor 320 may be a central processing unit (CPU), a graphics processing unit (GPU), an acceleration processing unit (APU), a microprocessor, a microcontroller, a digital signal processor (DSP), a field-programmable gate array (FPGA), an application-specific integrated circuit (ASIC), or other types of processing components. In some implementations, processor 320 includes one or more programmable processors for executing functions. Memory 330 includes random access memory (RAM), read-only memory (ROM), and / or other types of dynamic or static storage devices (e.g., flash memory, magnetic memory, and / or optical memory) for storing information and / or instructions for use by processor 320.

[0069] The storage component 340 stores information and / or software related to the operation and use of the device 300. For example, the storage component 340, together with the corresponding drive, may include a hard disk (e.g., magnetic disk, optical disk, magneto-optical disk, and / or solid state disk), a compact disc (CD), a digital versatile disc (DVD), a floppy disk, a cartridge, a magnetic tape, and / or other types of non-transitory computer-readable media. The input component 350 includes components that enable the device 300 to receive information via user input (e.g., touch screen display, keyboard, keypad, mouse, button, switch, and / or microphone), etc. Additionally or alternatively, the input component 350 may include sensors (e.g., global positioning system (GPS) component, accelerometer, gyroscope, and / or actuator) for measuring information. The output component 360 includes components that provide output information from the device 300 (e.g., display, speaker, and / or one or more light emitting diodes (LEDs)).

[0070] The communication interface 370 includes components such as a transceiver (e.g., transceiver and / or separate receiver and transmitter) that enable the device 300 to communicate with other devices via a wired connection, a wireless connection, or a combination of wired and wireless connections, etc. The communication interface 370 enables the device 300 to receive information from other devices and / or provide information to other devices. For example, the communication interface 370 may include an Ethernet interface, an optical interface, a coaxial interface, an infrared interface, a radio frequency (RF) interface, a universal serial bus (USB) interface, a Wi-Fi interface, a cellular network interface, etc.

[0071] Device 300 may execute one or more of the processes described herein. Device 300 may execute these processes in response to a processor 320 that executes software instructions stored by a non-transitory computer-readable medium such as a memory 330 and / or a storage component 340. The computer-readable medium is defined herein as a non-transitory memory device. The memory device includes a memory space within a single physical storage device or a memory space distributed across multiple physical storage devices.

[0072] The software instructions may be read into the memory 330 and / or the storage component 340 from another computer-readable medium or from another device via a communication interface 370. When executed, the software instructions stored in the memory 330 and / or the storage component 340 may cause the processor 320 to execute one or more of the processes described herein.

[0073] In addition or alternatively, a wired circuit may be used instead of or in combination with the software instructions to execute one or more of the processes described herein. Thus, the implementations described herein are not limited to a particular combination of hardware circuitry and software. The number and arrangement of components shown in FIG. 3 are provided as an example. In fact, device 300 may include additional components, fewer components, different components, or components in a different arrangement than those shown in FIG. 3. In addition or alternatively, a set of components of device 300 (e.g., one or more components) may execute one or more functions described as being performed by another set of components of device 300.

[0074] In an embodiment, any operation or process of FIGS. 4, 5, 6, 7, and 8 may be implemented by or using any element illustrated in FIGS. 1, 2, and 3. Other embodiments are not so limited and may be implemented in various different architectures (e.g., bare metal architecture, any cloud-based architecture, or deployment architectures such as Kubernetes, Docker, OpenStack, etc.).

[0075] FIG. 4 illustrates an NRT-RIC framework (or platform) within an SMO framework system architecture and an rApp for an R1 interface hosted by the NRT-RIC, and O1, O2, and A1 interfaces within O-RAN, according to one embodiment.

[0076] Referring to FIG. 4, the NRT-RIC represents a subset of the functions of the SMO framework. The NRT-RIC can access other SMO framework functions and can affect (i.e., control and / or execute) what is executed across the O1 and O2 interfaces (e.g., perform configuration management (CM) and / or performance management (PM)).

[0077] Generally, FCAPS management, software management, and file management are achieved by the O1 interface for operation and management between a management entity (network management system (NMS) / element management system (EMS) / management and orchestration of network function virtualization (MANO)) and an O-RAN management element.

[0078] The SMO framework system architecture includes SMO functions including O1 termination that enables communication between the SMO framework and an E2 node (i.e., O-CU, O-DU, etc.) via the O1 interface.

[0079] The NRT-RIC includes the NRT-RIC framework. The NRT-RIC framework includes an R1 service exposer function that handles R1 services provided according to embodiments, in addition to a plurality of other functions. Generally, the NRT-RIC functions within the NRT-RIC framework support, for rApps, authentication, authorization, registration, discovery, communication support, etc.

[0080] Generally, R1 services may include a set of services including, but not limited to, service registration and discovery services, authentication and authorization services, AI / ML workflow services, and A1, O1, and O2-interface related services.

[0081] An NRT-RIC application (rApp) is an application that utilizes functions available in the NRT-RIC framework and / or the SMO framework to provide value-added services related to RAN operations and optimization. The scope of rApps includes, but is not limited to, wireless resource management, data analytics, etc., and enrichment of information. Generally, an rApp represents an application designed to consume and / or generate R1 services.

[0082] For this purpose, the NRT-RIC framework generates and / or consumes R1 services according to embodiments via the R1 interface. The R1 interface terminates at the R1 termination of the NRT-RIC framework. The R1 termination enables the NRT-RIC framework and rApps to exchange messages / data (i.e., requests and responses with a data model) to connect to the NRT-RIC framework and rApps via the R1 interface and access R1 services.

[0083] Generally, the R1 interface is defined as an interface between the rApp and the NRT-RIC framework where R1 services can be generated and consumed.

[0084] Furthermore, the NRT-RIC framework comprises A1-related functions. The A1-related functions of the NRT-RIC framework support, for example, A1 logical termination, A1 policy coordination and catalog, A1-EI coordination and catalog, etc.

[0085] The data management and exposure services within the NRT-RIC framework deliver data generated or collected by the data generator to the data consumer according to needs (e.g., function management (FM) / consumption management (CM) / production management (PM) data for the rApp or CM changes from the rApp to O-RAN via the O1 interface).

[0086] The NRT-RIC framework further comprises external termination. The external termination supports, for example, the exchange of data between the NRT-RIC framework and external AI / ML functions, enrichment information (EI) sources, or external oversight.

[0087] Within the NRT-RIC framework, the AI / ML workflow service provides access to the AI / ML workflow. For example, the AI / ML workflow service may assist in model training, monitoring of the AI / ML models deployed in the NRT-RIC, etc.

[0088] Furthermore, the NRT-RIC framework comprises A2-related functions that support, for example, A2 logical termination, A2 policy coordination and catalog, etc.

[0089] Still referring to FIG. 4, within the NRT-RIC, the R1 interface is an open logical interface within the O-RAN architecture between the rApp and the NRT-RIC framework of the NRT-RIC. The R1 interface supports the exchange of control signaling information and the collection of data and the delivery between endpoints. The R1 interface enables, for example, multi-vendor rApps to consume and / or generate R1 services.

[0090] The R1 interface is independent of a particular implementation of the SMO and the NRT-RIC framework of the NRT-RIC. The R1 interface is defined in an extensible way that allows new services and data types to be added without the need to change the protocol or procedure.

[0091] In particular, the R1 interface enables the interconnection between rApps and NRT-RIC frameworks supplied by different vendors (i.e., enables interconnection in a multi-vendor environment). For this purpose, the R1 interface provides a level of abstraction between the rApp and the NRT-RIC framework and / or the SMO framework.

[0092] Referring to FIG. 4, for example, by an A1 policy or an optimization trigger on the O1 interface for the nRT-RIC defined by the NRT-RIC (i.e., by at least one rApp hosted by the NRT-RIC and / or by the NRT-RIC framework assisted by machine learning (ML) techniques), the NRT-RIC framework (e.g., at least one rApp hosted by the NRT-RIC and / or the NRT-RIC framework) enables flexible configuration of carrier and / or cell on / off switching parameters in a cell or a cluster of cells. Actions of the nRT-RIC via the E2 interface may enable the deployment of carrier and / or cell on / off switching parameters configured for one or more E2 nodes.

[0093] For this purpose, before stopping (i.e., switching off) one or more carriers and / or cells, the E2 node needs to perform preparatory actions for stopping (i.e., switching off) one or more carriers and / or cells (e.g., the E2 node may check ongoing emergency calls and / or warning messages that need to be addressed (e.g., enabled, disabled, modified, etc.) in carrier aggregation and / or dual connectivity, such as triggering the migration of UEs with high occupancy (HO) data traffic from one or more cells and / or carriers to other cells or carriers, informing neighboring nodes via the X2 / Xn interface, etc.).

[0094] Furthermore, before switching on one or more carriers and / or cells, the E2 node needs to perform preparatory actions for switching on (e.g., the E2 node may perform cell probing, notification to neighboring nodes via the X2 / Xn interface, etc.).

[0095] Referring to FIG. 4, the SMO and NRT-RIC frameworks are configured to collect configuration, performance indicators, and measurement reports (e.g., cell load related information and traffic information, measurement reports of energy efficiency (EE) and / or energy consumption (EC), geolocation information, etc.) from the E2 node and the O-RU (via the E2 node forwarded by the SMO) for the purpose of decision-making (e.g., the SMO and NRT-RIC frameworks may constitute R1 / O1 consumption and / or generation services). The decision-making may be based on, for example, the training and inference of AI / ML models that support such energy efficiency (EE) and / or energy consumption (EC) functions.

[0096] Generally, energy efficiency (EE) is defined as the relationship between useful output and energy / power consumption, and energy consumption (EC) is defined as the integral of power consumption over time.

[0097] Furthermore, the SMO and NRT-RIC frameworks transfer data collected for one or more rApps hosted by the NRT-RIC and signal (i.e., send) an updated configuration for energy efficiency (EE) / energy savings (ES) optimization to the E2 node (O-CU) via the R1 / O1 interface. They analyze the data received from the SMO (e.g., cell load-related information and traffic information, energy efficiency (EE) and / or energy consumption (EC) measurement reports, geolocation information, etc.) and are configured such that one or more E2 nodes and O-RUs determine EE / ES optimization (i.e., when one or more carriers and / or cells are recommended to be switched on / off). (For example, the SMO and NRT-RIC frameworks may configure R1 / O1 consumption and / or generation services.) The determination of EE / ES optimization (i.e., the recommendation of which one or more carriers and / or cells may be switched on / off) may be based on the use of an AI / ML model (i.e., may be assisted by the use of an AI / ML model).

[0098] In one embodiment, the SMO and NRT-RIC frameworks (e.g., at least one rApp hosted by the NRT-RIC and / or the NRT-RIC framework) may be configured to trigger the training and / or retraining of an EE / ES AI / ML model.

[0099] Furthermore, in one embodiment, the SMO and NRT-RIC frameworks (e.g., at least one rApp hosted by the NRT-RIC and / or the NRT-RIC framework) may be configured to deploy, update, configure, etc., an energy efficiency (EE) / energy savings (ES) AI / ML model in the NRT-RIC. (For example, the SMO and NRT-RIC frameworks may configure R1 / O1 consumption and / or generation services for deploying, updating, configuring, etc., the EE / ES AI / ML model.)

[0100] Referring to FIG. 4, one or more rApps hosted by the NRT-RIC are configured to retrieve, for the purpose of training and executing the associated AI / ML model (e.g., EE / ES AI / ML model), necessary configurations, performance indicators, and measurement reports (i.e., EE / EC measurement reports), etc. from the E2 node and the O-RU (transferred by the SMO) (e.g., may be equipped with R1 / O1 consumption and / or generation services).

[0101] Furthermore, the rApp hosted by the NRT-RIC is configured to infer, for EE / ES, an optimized configuration for the E2 node through the R1 / O1 interface of the SMO and the NRT-RIC framework, in order to report cell configuration parameters, performance indicators, and measurement reports (e.g., cell load-related information and traffic information, EE / EC measurement reports) to the SMO via the O1 interface (e.g., may be equipped with R1 / O1 consumption and / or generation services).

[0102] Furthermore, the rApp hosted by the NRT-RIC is configured to execute actions necessary for EE / ES optimization (i.e., consume and / or generate services via the R1 interface) (e.g., may configure R1 / O1 consumption and / or generation services). For example, the rApp may check ongoing emergency calls and warning messages via the R1 / O1 interface and execute some preparatory actions to stop (i.e., switch off) one or more carriers and / or cells. In one embodiment, the rApp may be configured to enable, disable, modify, etc. carrier aggregation and / or dual connectivity, for example, to notify neighboring nodes via the X2 / Xn interface that trigger the migration of UEs from one or more cells and / or carriers to other cells or carriers.

[0103] In other embodiments, one or more rApps hosted by the NRT-RIC are configured to perform the actions necessary for EE / ES optimization (i.e., consume and / or generate services via the R1 interface) to prepare to switch one or more carriers and / or cells on (e.g., may configure services for R1 / O1 consumption and / or generation). For example, one or more rApps hosted by the NRT-RIC may be configured to perform cell probing, notify neighboring nodes via the X2 / Xn interface, etc.

[0104] Furthermore, the rApp hosted by the NRT-RIC may be configured to make a final decision on the on / off switch and notify the SMO via O1 about the actions performed (i.e., notify one or more E2 nodes and O-RUs about the deployment of the configuration optimized for EE / ES through the R1 / O1 interface of the SMO and NRT-RIC framework) (e.g., may configure services for R1 / O1 consumption and / or generation).

[0105] For this purpose, one or more E2 nodes in FIG. 1 (i.e., O-DU, O-CU, etc.) are configured to report, for example, cell configuration, performance indicators, measurement reports (e.g., cell load-related information, traffic information, EE / EC measurement reports, etc.) to the SMO via the O1 interface. SMO functions such as O1 termination enable the SMO to communicate with the E2 nodes.

[0106] Furthermore, one or more E2 nodes in FIG. 1 (i.e., O-DU, O-CU, etc.) are configured to perform actions necessary for EE / ES optimization. These actions necessary for EE / ES optimization may include checking ongoing emergency calls and warning messages, performing some preparatory actions to stop (i.e., switch off) one or more carriers and / or cells, etc. In one embodiment, the rApp is configured to enable, disable, modify, etc., carrier aggregation and / or dual connectivity for the purpose of, for example, notifying neighboring nodes via the X2 / Xn interface that trigger HO traffic and the migration of UEs from one or more cells and / or carriers to other cells or carriers.

[0107] Furthermore, one or more E2 nodes in FIG. 1 (i.e., O-DU, O-CU, etc.) are configured to make a final decision regarding the on / off switching and notify the SMO via O1 about the actions performed, and to perform actions necessary to switch one or more carriers and / or cells on (for example, one or more E2 nodes may be configured to perform cell probing, notify neighboring nodes via the X2 / Xn interface, etc.).

[0108] One or more O-RUs in FIG. 1 are configured to report relevant information on energy consumption (EC) and energy efficiency (EE) to an E2 node (i.e., O-DU) via the open FH M-Plane interface.

[0109] In one embodiment, one or more O-RUs in FIG. 1 may be configured to report relevant information on energy consumption (EC) and energy efficiency (EE) directly to the SMO / NRT-RIC.

[0110] Furthermore, one or more O-RUs of FIG. 1 are configured to support actions necessary to perform EE / ES optimization and report an updated carrier configuration (e.g., report configuration status such as activation, deactivation, sleep, etc.).

[0111] FIG. 5 is a flowchart of a method for implementing optimization of on / off switching of carriers and / or cells according to one embodiment.

[0112] Referring to FIG. 5, a method for optimizing on / off switching of carriers and / or cells is implemented by a non-real-time RAN intelligent controller (NRT-RIC), an NRT-RIC framework, at least one SMO function (e.g., O1 termination), and a service management and orchestration (SMO) framework that hosts an rApp by the NRT-RIC. The SMO framework may function as an intermediary between the rApp and the E2 node and the O-RU (via the E2 node) within the O-RAN.

[0113] In step 501, the rApp collects O1-related data that provides the O1 configuration necessary to perform on / off switching of cells and / or carriers from an E2 node (i.e., O-CU, O-DU, etc.) via the R1 interface through the NRT-RIC framework and via the O1 interface through an SMO function within the SMO framework (i.e., an SMO function configured as the O1 termination of the O1 interface in the SMO). The O1-related data is collected via the open front-haul management plane (FH M-Plane) interface between the E2 node and the open radio unit (O-RU).

[0114] In one embodiment, collecting O1-related data that provides the O1 configuration necessary to perform on / off switching of cells and / or carriers may include the rApp sending an O1-related data collection request to the E2 node via the R1 interface through the NRT-RIC framework and via the O1 interface (i.e., O1 termination) through the SMO function within the SMO framework. The E2 node may receive the O1-related data collection request from the SMO function, and may collect O1-related data that provides the O1 configuration necessary to perform on / off switching of cells and / or carriers from the O-RU via the open front-haul management plane (FH M-Plane) interface between the E2 node and the open radio unit (O-RU).

[0115] In one embodiment, regarding the collection of O1-related data that provides the O1 configuration necessary to perform on / off switching of cells and / or carriers, the E2 node may activate a measurement report (i.e., an EE / EC measurement report) for the O-RU, and the O-RU may provide measurement data (i.e., input data) for the measurement report.

[0116] When collecting O1-related data from the O-RU, the E2 node may send, via the R1 interface, the O1-related data that provides the O1 configuration necessary to perform on / off switching of cells and / or carriers, which has been collected via the O1 interface through the SMO function within the SMO framework and through the NRT-RIC framework, to the rApp.

[0117] In step 502, the SMO retrains at least one artificial intelligence / machine learning (AI / ML) model based on the collected O1-related data, and the rApp deploys and activates one retrained AI / ML model for inferring data necessary to perform on / off switching of cells and / or carriers within the O-RAN from among at least one of the retrained AI / MLs to provide the O1 configuration.

[0118] In one embodiment, the O1-related data that provides the O1 configuration necessary to perform on / off switching of cells and / or carriers may be input data (e.g., measurement data) used in the training and inference of an AI / ML model. The O1-related data that provides the O1 configuration necessary to perform on / off switching of cells and / or carriers may, in addition to other O1-related data, include the following measurement data for monitoring the energy consumption and energy efficiency (EC / EE) of one or more E2 nodes and one or more O-RUs: for each interface, for each PLMN (public land mobile network), for each service quality (QoS) level, for each slice, for the F1-U interface, for the Xn-U interface, for the X2-U interface, the downlink packet data convergence protocol service data unit (DL PDCP SDU) data volume (data volume in the DL delivered from O-CU-UP to O-DU), for each interface, for each PLMN (public land mobile network), for each service quality (QoS) level, for each slice, for the F1-U interface, for the Xn-U interface, for the X2-U interface, the uplink packet data convergence protocol service data unit (UP PDCP SDU) data volume (data volume in the UL delivered from O-CU-UP to O-DU), for each cell for each synchronization signal block (SSB), the reference signal received quality (RSRQ) measurement result, for each cell for each SSB, the reference signal received power (RSRP) measurement result, for each cell for each SSB, the signal-to-interference plus noise ratio (SINR) measurement result, energy consumption, power consumed by hardware components, transmission power.

[0119] In one embodiment, the NRT-RIC framework may retrain at least one AI / ML model. According to this embodiment, the rApp selects one AI / ML model from a plurality of AI / ML models and sends a start request for retraining the AI / ML model to the NRT-RIC framework. The NRT-RIC framework retrains the AI / ML model. At this time, the rApp monitors the retrained AI / ML model parameters and determines the recovery of the retrained AI / ML model from the NRT-RIC framework based on the retrained AI / ML model parameters. The rApp requests the retrained AI / ML model from the NRT-RIC framework based on the determination of the recovery. When receiving the request, the NRT-RIC framework sends the retrained AI / ML model to the rApp.

[0120] In other embodiments, the rApp hosts a plurality of AI / ML models and retrains one of the plurality of AI / ML models.

[0121] Still referring to FIG. 5, in step 503, the rApp monitors O1-related data that provides the O1 configuration necessary to perform on / off switching of cells and / or carriers through the R1 interface via the NRT-RIC framework and through the O1 interface via the SMO function within the SMO framework.

[0122] In one embodiment, the rApp may constantly monitor, for example, the performance and energy consumption of the E2 node and the energy consumption of the O-RU.

[0123] Furthermore, in one embodiment, the rApp monitors performance and energy consumption parameters for the evaluation of the O1 configuration required to perform the shutdown of cells and carriers. These performance and energy consumption parameters may include configuration, performance indicators, measurement reports (e.g., cell load related information, traffic information, EE / EC measurement reports, etc.).

[0124] For example, the O1-related data (i.e., input data) providing the O1 configuration required to perform the on / off switching of cells and / or carriers may include at least one of configuration, performance indicators, and measurement reports provided by the O-RU. The measurement reports may include at least one of cell load related information, measurement reports, traffic information measurement reports, energy efficiency / energy consumption (EE / EC) measurement reports. The energy efficiency / energy consumption (EE / EC) measurement reports may include at least one of the energy consumption of the E2 node and the energy consumption of the O-RU and one or more performance-related KPIs of the E2 node.

[0125] In step 504, the rApp evaluates the O1-related data providing the configuration required to perform the on / off switching of cells and / or carriers within the O-RAN and determines to generate the O1 configuration data for preparing and performing the on / off switching of cells and / or carriers.

[0126] Furthermore, in step 504, when generating the O1 configuration data for preparing and performing the on / off switching of cells and / or carriers, the rApp sends the O1 configuration data for preparing and performing the on / off switching of cells and / or carriers to the E2 node via the R1 interface through the NRT-RIC framework and via the O1 interface through the SMO function.

[0127] In one embodiment, if a predetermined performance target (e.g., an EE / ES performance target) is not achieved based on O1-related data (i.e., input data) that provides the O1 configuration necessary to perform the on / off switching of cells and / or carriers, the rApp determines to generate O1 configuration data (i.e., output data) for preparing and performing the on / off switching of cells and / or carriers. In this case, the rApp generates O1 configuration data for preparing and performing the on / off switching of cells and / or carriers.

[0128] For example, the EE / ES performance target may be an A1 policy in the NRT-RIC or a target set by the network operator for the energy saving (ES) function in the NRT-RIC (i.e., a predetermined performance parameter for EE / ES within O-RAN, e.g., one or more predetermined performance targets for EE / EC within O-RAN).

[0129] In one embodiment, the O1 configuration data (i.e., output data) for preparing and executing the on / off switching of the generated cells and / or carriers enables the rApp for shutting down the cells and carriers that save energy to reconfigure resources via the O1 interface. For example, it may include the NRCellCU Information Object Class IOC, NRCellDU IOC, GNBDUFunction IOC, GNBCUCPFunction IOC, GNBCUUPFunction IOC, etc., as defined in 3GPP TS 28.541: “3rd Generation Partnership Project; Technical Specification Group Services and System Aspects; Management and Orchestration; 5G Network Resource Model (NRM); Stage 2 and stage 3”, Release 16, December 2020.

[0130] In step 505, when the E2 node receives the O1 configuration data for preparing and executing the on / off switching of the cells and / or carriers at the E2 node, it converts the O1 configuration data for preparing and executing the on / off switching of the cells and / or carriers and instructs the O-RU to execute the on / off switching of the cells and / or carriers via the open FH M-Plane.

[0131] In one embodiment, the implementation of the O1 configuration data for preparing and performing the on / off switching of cells and / or carriers may further include the O-RU notifying the E2 node of the completion of the implementation of the on / off switching of cells and / or carriers. When receiving the notification from the O-RU, the E2 node notifies the rApp through the O1 interface via the SMO function and through the R1 interface via the NRT-RIC framework. According to an embodiment, the O-RU may notify the E2 node via the open front-haul management plane (FH M-Plane) interface between the E2 node and the O-RU.

[0132] In a further embodiment, after the implementation in step 505, the NRT-RIC may monitor the performance of the retrained AI / ML model and may determine that a predetermined performance target is not achieved. In this case, the NRT-RIC may initiate a fallback mechanism and / or initiate an update or retraining of the AI / ML model.

[0133] FIG. 6 illustrates a data collection flow according to one embodiment. Referring to FIG. 6, the data collection aims to enable the on / off switching energy saving function of carriers and cells in the O-RAN by actions that enable the implementation of configuration parameter changes (i.e., the implementation of the O1 configuration data for preparing and performing the on / off switching of cells and / or carriers) and an AI / ML-based solution for optimizing the on / off switching of cells and / or carriers in the EE / ES within the O-RAN controlled by the NRT-RIC.

[0134] For this purpose, the SMO function may be an O1 termination point for the O1 interface. The non-RT-RIC framework and / or rApp may perform AI / ML-based optimization of cell and / or carrier on / off switching. At least one O-RAN E2 node and O-RU may enable (i.e., implement) a carrier and cell on / off switching optimization configuration.

[0135] Referring to FIG. 6, when an open FH M-Plane interface is established between the R1 interface and O1 interface connections and the E2 node and O-RU, a communication path is established within the O-RAN between the rApp and the E2 node and at least one O-RAN -RU.

[0136] According to the O-RAN system architecture, the NRT-RIC has knowledge about overlapping carriers / cells and the coverage of those carriers / cells (e.g., which carriers / cells are in the coverage layer and which are in the capacity layer).

[0137] To optimize EE / ES within the O-RAN, the network operator may set a target (i.e., one or more predetermined performance parameters for EE / ES within the O-RAN, e.g., one or more predetermined performance goals for EE / EC within the O-RAN) for the energy savings (ES) function in the NRT-RIC.

[0138] As a result, a method for optimizing cell and / or carrier on / off switching may start when the network operator enables an optimized rApp with an initial AI / ML model for the carrier and cell on / off switching ES function and the E2 node and O-RU come into an operating state.

[0139] In Operation 1, the rApp requests the NRT-RIC framework to collect O1-related data such as the necessary configuration, performance indicators, measurement data (e.g., cell load-related information and traffic information, EE / EC measurement reports, cell-level configuration), etc. via the R1 interface.

[0140] In Operation 2, the NRT-RIC framework requests the SMO framework to collect O1-related data from the E2 node.

[0141] In Operation 3, the SMO framework function (i.e., the SMO function) requests data collection from the E2 node and the O-RU (via the E2 node).

[0142] In Operation 4, when the E2 node (i.e., O-CU, O-DU, etc.) receives a request from the SMO, it requests and collects the O1-related data (i.e., configuration data, configured measurement data) necessary to optimize the on / off switching of carriers and / or cells from the O-RU via the open FH M-Plane interface.

[0143] In Operation 5, one or more E2 nodes (i.e., O-CU, O-DU, etc.) send O1-related data such as configuration data and configured measurement data to the SMO periodically and / or based on events.

[0144] In Operation 6, the NRT-RIC retrieves O1-related data such as configuration data and configured measurement data for processing (e.g., for consuming and / or producing R1 services related to EE / ES).

[0145] In Operation 7, the rApp retrieves O1-related data such as configuration data and configured measurement data for processing (e.g., for consuming and / or producing R1 services related to EE / ES).

[0146] Figure 7 illustrates a data analysis, AI / ML model training, and inference flow according to an embodiment. Referring to Figure 7, in operation 8, at least one of the plurality of AI / ML models can be retrained on the NRT-RIC framework or rApp. In one example, when the NRT-RIC framework hosts the retraining of at least one of the plurality of AI / ML models, the rApp selects one of the plurality of AI / ML models and initiates the retraining of the selected AI / ML model on the Non-RT-RIC framework. In one example, the retraining and selection of the AI / ML model may be performed by a flexible AI / ML workflow within the SMO.

[0147] In operation 9, upon receiving a retraining request from the rApp, the NRT-RIC framework initiates the retraining of the AI / ML model.

[0148] In operation 10, the rApp monitors the retrained AIML model and retrieves the retrained AI / ML model from the NRT-RIC. In one example, the AI / ML model retrieval procedure on the R1 interface may be performed by a flexible AI / ML workflow within the SMO based on the R1 service.

[0149] In operation 11, upon receiving a retrieval request from the rApp, the NRT-RIC framework transfers the AI / ML model (i.e., the retrained AI / ML model) to the rApp.

[0150] In one example, the AI / ML model transfer procedure on the R1 interface may be performed by a flexible AI / ML workflow within the SMO based on the R1 service.

[0151] In operation 13, when the retraining of the AI / ML model is executed, at least one AI / ML model (including the retrained AI / ML model) is deployed and activated for inference (i.e., to infer data necessary to provide the O1 configuration for performing on / off switching of cells and / or carriers within O-RAN).

[0152] FIG. 8 illustrates a data analysis, AI / ML model training, and inference flow according to one embodiment. Referring to FIG. 8, data analysis, AI / ML training, and inference may be performed by the rApp.

[0153] For this purpose, in operation 12, the retraining of the AI / ML model is hosted by the rApp, and the AI / ML model is retrained on top of the rApp itself.

[0154] In operation 13 of FIG. 8, when the retraining of the AI / ML model is executed, at least one AI / ML model (including the retrained AI / ML model) is deployed and activated for inference (i.e., to infer data necessary to provide the O1 configuration for performing on / off switching of cells and / or carriers within O-RAN).

[0155] Referring to FIGS. 7 and 8, in operation 13, the rApp constantly monitors the performance and energy consumption of the E2 node, the energy consumption of the O-RU, etc. For example, the rApp monitors performance and energy consumption for the evaluation of the O1 configuration necessary to perform shutdown of cells and carriers.

[0156] In one embodiment, the O1-related data may be measurement input data used in the training and inference of the AI / ML model. The O1-related data may include the following measurement data, in addition to other O1-related data, for monitoring the energy consumption and energy efficiency (EC / EE) of one or more E2 nodes and one or more O-RUs: for each interface, for each public land mobile network (PLMN), for each quality of service (QoS) level, for each slice, for the F1-U interface, Xn-U interface, X2-U interface, the downlink packet data convergence protocol service data unit (DL PDCP SDU) data volume (data volume in the DL delivered from O-CU-UP to O-DU), for each interface, for each public land mobile network (PLMN), for each quality of service (QoS) level, for each slice, for the F1-U interface, Xn-U interface, X2-U interface, the uplink packet data convergence protocol service data unit (UP PDCP SDU) data volume (data volume in the UP delivered from O-CU-UP to O-DU), for each cell for each synchronization signal block (SSB), the reference signal received quality (RSRQ) measurement result, for each cell for each SSB, the reference signal received power (RSRP) measurement result, for each cell for each SSB, the signal-to-interference plus noise ratio (SINR) measurement result, energy consumption, power consumed by the hardware components, transmission power, etc.

[0157] FIG. 9 illustrates the generation and implementation of an O1 configuration for preparing and performing on / off switching of a cell and / or carrier according to one embodiment.

[0158] Referring to FIG. 9, in operation 14, the rApp generates an O1 configuration for preparing and performing on / off switching of one or more cells and / or one or more carriers and sends the O1 configuration to the SMO via the R1 interface through the NRT-RIC framework.

[0159] In one embodiment, the O1 configuration data for preparing and executing the on / off switching of the generated cells and / or carriers enables cell and carrier shutdown to save energy and reconfigure resources via the O1 interface. For example, it may include output data such as NRCellCU Information Object Class IOC, NRCellDU IOC, GNBDUFunction IOC, GNBCUCPFunction IOC, GNBCUUPFunction IOC, as defined in 3GPP TS 28.541: “3rd Generation Partnership Project; Technical Specification Group Services and System Aspects; Management and orchestration; 5G Network Resource Model (NRM); Stage 2 and stage 3”, Release 16, December 2020.

[0160] In operation 15, the NRT-RIC requests, via the O1 interface, that the SMO framework function (i.e., the SMO function) configure the E2 node to prepare and execute the on / off switching of a cell or carrier.

[0161] In operation 16, the SMO instructs the E2 node, via the O1 interface, to execute the request received from the rApp.

[0162] In operation 17, the E2 node notifies the O-RU of the updated O-RU configuration via the open FH M-Plane. In one embodiment, the O-RU may notify the E2 node when the O-RU configuration is implemented.

[0163] In operation 18, when the on / off switching of a cell or carrier is completed, the E2 node notifies the SMO.

[0164] In Operation 19, the SMO framework function (i.e., the SMO function) notifies the NRT-RIC framework of the completion of the on / off switching of a cell or carrier.

[0165] In Operation 20, the NRT-RIC notifies the rApp of the completion of the on / off switching of a cell or carrier on the R1 interface.

[0166] In Operation 21, the NRT-RIC continuously analyzes the performance of the AI / ML model. In one example, if the energy saving target is not achieved, the NRT-RIC may decide to initiate a fallback mechanism and / or an update or retraining of the AI / ML model.

[0167] In one example, a method for optimizing the on / off switching of a cell and / or carrier may end when the E2 node becomes inactive or when the operator deactivates an optimization function or an AI / ML model for energy saving (i.e., the AI / ML model for EE / ES).

[0168] In other examples, the rApp continues the closed-loop monitoring of the energy saving function in the E2 node and the O-RU (via the E2 node).

[0169] According to Operation 21, the E2 node and the O-RU operate using the newly deployed parameters (i.e., O1 configuration data) / models (i.e., the retrained AI / ML model) and states (i.e., the on / off states of carriers and cells).

[0170] According to an embodiment, in an Open Radio Access Network (O-RAN), a system is provided for implementing optimization of on / off switching of carriers and / or cells by a Service Management and Orchestration (SMO) framework. The system includes a memory for storing instructions, and at least one processor configured to implement a Non-Real-Time RAN Intelligent Controller (NRT-RIC), an NRT-RIC framework, at least one SMO function, and an rApp hosted by the NRT-RIC.At least one processor collects, via the R1 interface through the NRT-RIC framework and via the O1 interface through the SMO function within the SMO framework, O1-related data provided by the rApp for performing the on / off switching of cells and / or carriers, the O1-related data being collected via the open front-haul management plane (FH M-Plane) interface between the E2 node and the open radio unit (O-RU). The SMO retrains at least one artificial intelligence / machine learning (AI / ML) model based on the collected O1-related data. The rApp deploys and activates one retrained AI / ML model for inferring data for providing the O1 configuration required to perform the on / off switching of cells and / or carriers within the O-RAN among the at least one retrained AI / ML. The rApp monitors the O1-related data for providing the O1 configuration required to perform the on / off switching of cells and / or carriers, evaluates the O1-related data for providing the O1 configuration required to perform the on / off switching of cells and / or carriers, determines to generate O1 configuration data for preparing and performing the on / off switching of cells and / or carriers, and sends, via the R1 interface through the NRT-RIC framework and via the O1 interface through at least one SMO function within the SMO framework, the O1 configuration data for preparing and performing the on / off switching of cells and / or carriers to at least one E2 node. The E2 node and the O-RU are configured to execute instructions to implement the on / off switching of cells and / or carriers within the O-RAN.During implementation, at least one processor is further configured to convert O1 configuration data for preparing and performing on / off switching of cells and / or carriers by an E2 node, and to instruct the O-RU to perform on / off switching of cells and / or carriers via an open FH M-Plane by the E2 node.

[0171] During retraining of at least one AI / ML model, at least one processor is further configured to select one AI / ML model from a plurality of AI / ML models by an rApp, send a start request for retraining the AI / ML model to an NRT-RIC framework by the rApp, retrain the AI / ML model by the NRT-RIC framework, monitor the retrained AI / ML model parameters by the rApp, determine the retrieval of the retrained AI / ML model from the NRT-RIC framework based on the retrained AI / ML model parameters, request the retrained AI / ML model from the NRT-RIC framework by the rApp, and send the retrained AI / ML model to the rApp by the NRT-RIC framework.

[0172] During retraining of at least one AI / ML model, at least one processor may be further configured to retrain one AI / ML model from a plurality of AI / ML models by an rApp.

[0173] The O1-related data that provides the O1 configuration necessary to perform the on / off switching of cells and / or carriers may include at least one of configuration, performance indicators, and measurement reports provided by the O-RU. The measurement report may include at least one of cell load-related information, traffic information, and energy efficiency / energy consumption (EE / EC) measurement reports. The energy efficiency / energy consumption (EE / EC) measurement report may include at least one of the energy consumption of the E2 node, the energy consumption of the O-RU, and one or more performance-related KPIs (Key Performance Indicators) of the E2 node.

[0174] At least one processor sends an O1-related data collection request to the E2 node for collecting O1-related data that provides the O1 configuration necessary to perform the on / off switching of cells and / or carriers, by the rApp, through the NRT-RIC framework via the R1 interface, and via the O1 interface through the SMO function within the SMO framework. The E2 node receives the O1-related data collection request from the SMO function. The E2 node collects O1-related data that provides the O1 configuration necessary to perform the on / off switching of cells and / or carriers from the O-RU via the open front-haul management plane (FH M-Plane) interface between the E2 node and the open radio unit (O-RU). The E2 node sends the O1-related data that provides the O1 configuration necessary to perform the on / off switching of cells and / or carriers, collected via the SMO function within the SMO framework and the O1 interface through the NRT-RIC framework, to the rApp via the R1 interface. It may be configured as such.

[0175] At least one processor is further configured such that, while instructing the O-RU to perform on / off switching of cells and / or carriers via the open FH M-Plane, the O-RU notifies the completion of the implementation of the on / off switching of cells and / or carriers to the E2 node via the FH M-Plane interface between the E2 node and the O-RU, and the E2 node notifies the rApp of the completion of the implementation of the on / off switching of cells and / or carriers via the O1 interface through the SMO function and via the R1 interface through the NRT-RIC framework within the SMO framework.

[0176] At least one processor is further configured such that the NRT-RIC monitors the performance of the retrained AI / ML model, determines that a predetermined performance target is not achieved based on the collected O1-related data, and initiates a fallback mechanism and / or an update or retraining of the AI / ML model.

[0177] According to an embodiment, in an Open Radio Access Network (O-RAN), a method for implementing optimization of on / off switching of carriers and / or cells by a Service Management and Orchestration (SMO) framework is provided.The method is O1-related data provided by the rApp to provide the O1 configuration required for the on / off switching of cells and / or carriers, and the O1-related data collected via the open front-haul management plane (FH M-Plane) interface between the E2 node and the open radio unit (O-RU) is collected from the E2 node through the R1 interface via the NRT-RIC framework and through the O1 interface via the SMO function within the SMO framework; the SMO retrains at least one artificial intelligence / machine learning (AI / ML) model based on the collected O1-related data; the rApp deploys and activates one retrained AI / ML model for inferring the data that provides the O1 configuration required for the on / off switching of cells and / or carriers within O-RAN among at least one retrained AI / ML; the rApp monitors the O1-related data that provides the O1 configuration required for the on / off switching of cells and / or carriers through the R1 interface via the NRT-RIC framework and through the O1 interface via at least one SMO function within the SMO framework; the rApp evaluates the O1-related data that provides the O1 configuration required for the on / off switching of cells and / or carriers; the rApp determines to generate the O1 configuration data for preparing and executing the on / off switching of cells and / or carriers; the rApp sends the O1 configuration data for preparing and executing the on / off switching of cells and / or carriers to at least one E2 node through the R1 interface via the NRT-RIC framework and through the O1 interface via at least one SMO function within the SMO framework; and the E2 node and the O-RU implement the on / off switching of cells and / or carriers within O-RAN.Implementing may include the E2 node converting O1 configuration data for preparing and performing on / off switching of cells and / or carriers, and the E2 node instructing the O-RU to perform on / off switching of cells and / or carriers via the open FH M-Plane.

[0178] Retraining at least one AI / ML model may include the rApp selecting one AI / ML model from a plurality of AI / ML models, the rApp sending a start request for retraining the AI / ML model to the NRT-RIC framework, the NRT-RIC framework retraining the AI / ML model, the rApp monitoring the retrained AI / ML model parameters and determining the retrieval of the retrained AI / ML model from the NRT-RIC framework based on the retrained AI / ML model parameters, the rApp requesting the retrained AI / ML model from the NRT-RIC framework, and the NRT-RIC framework sending the retrained AI / ML model to the rApp.

[0179] Retraining at least one AI / ML model may include the rApp retraining one AI / ML model from a plurality of AI / ML models.

[0180] The O1-related data that provides the O1 configuration necessary to perform on / off switching of cells and / or carriers may include at least one of configuration, performance indicators, and measurement reports provided by the O-RU. The measurement report may include at least one of cell load-related information, traffic information, and energy efficiency / energy consumption (EE / EC) measurement reports. The energy efficiency / energy consumption (EE / EC) measurement report may include at least one of the energy consumption of the E2 node, the energy consumption of the O-RU, and one or more performance-related KPIs (Key Performance Indicators) of the E2 node.

[0181] Collecting the O1-related data that provides the O1 configuration necessary to perform on / off switching of cells and / or carriers involves the rApp sending an O1-related data collection request to the E2 node through the NRT-RIC framework via the R1 interface and via the O1 interface through the SMO function within the SMO framework, the E2 node receiving the O1-related data collection request from the SMO function, the E2 node collecting the O1-related data that provides the O1 configuration necessary to perform on / off switching of cells and / or carriers from the O-RU via the open front-haul management plane (FH M-Plane) interface between the E2 node and the open radio unit (O-RU), and the E2 node sending the O1-related data that provides the O1 configuration necessary to perform on / off switching of cells and / or carriers collected via the O1 interface through the SMO function within the SMO framework and the NRT-RIC framework to the rApp via the R1 interface. This may be included.

[0182] Instructing the O-RU to perform on / off switching of cells and / or carriers may further include the O-RU notifying the E2 node of the completion of the implementation of the on / off switching of cells and / or carriers via the FH M-Plane interface between the E2 node and the O-RU, and the E2 node notifying the rApp of the completion of the implementation of the on / off switching of cells and / or carriers via the O1 interface through the SMO function and via the R1 interface through the NRT-RIC framework within the SMO framework.

[0183] The method may further include the NRT-RIC monitoring the performance of the retrained AI / ML model, determining that a predetermined performance target is not achieved based on the collected O1-related data, and initiating a fallback mechanism and / or updating or retraining the AI / ML model.

[0184] According to an embodiment, in an Open Radio Access Network (O-RAN), a non-transitory computer-readable recording medium having recorded thereon instructions executable by at least one processor configured to implement a non-real-time RAN intelligent controller (NRT-RIC), an NRT-RIC framework, at least one SMO function, and an rApp hosted by the NRT-RIC is provided to execute a method for implementing optimization of on / off switching of carriers and / or cells by a service management and orchestration (SMO) framework.The method is O1-related data provided by the rApp to provide the O1 configuration required to perform cell and / or carrier on / off switching, and the O1-related data collected via the open fronthaul management plane (FH M-Plane) interface between the E2 node and the open radio unit (O-RU) is collected from the E2 node through the R1 interface via the NRT-RIC framework and via the O1 interface through the SMO function within the SMO framework; re-training at least one artificial intelligence / machine learning (AI / ML) model by the SMO based on the collected O1-related data; deploying and activating one re-trained AI / ML model by the rApp for inferring data that provides the O1 configuration required to perform cell and / or carrier on / off switching within the O-RAN among at least one re-trained AI / ML; monitoring by the rApp the O1-related data that provides the O1 configuration required to perform cell and / or carrier on / off switching through the R1 interface via the NRT-RIC framework and via the O1 interface through at least one SMO function within the SMO framework; evaluating by the rApp the O1-related data that provides the O1 configuration required to perform cell and / or carrier on / off switching; determining by the rApp to generate O1 configuration data for preparing and performing cell and / or carrier on / off switching; sending by the rApp the O1 configuration data for preparing and performing cell and / or carrier on / off switching to at least one E2 node through the R1 interface via the NRT-RIC framework and via the O1 interface through at least one SMO function within the SMO framework; and implementing cell and / or carrier on / off switching within the O-RAN by the E2 node and the O-RU.Implementing may include the E2 node converting O1 configuration data for preparing and performing on / off switching of cells and / or carriers, and the E2 node instructing the O-RU to perform on / off switching of cells and / or carriers via the open FH M-Plane.

[0185] Retraining at least one AI / ML model may include the rApp retraining one AI / ML model from a plurality of AI / ML models.

[0186] The O1-related data providing the O1 configuration necessary to perform on / off switching of cells and / or carriers may include at least one of configuration, performance indicators, and measurement reports provided by the O-RU. The measurement reports may include at least one of cell load-related information, traffic information, and energy efficiency / energy consumption (EE / EC) measurement reports. The energy efficiency / energy consumption (EE / EC) measurement reports may include at least one of the energy consumption of the E2 node, the energy consumption of the O-RU, and one or more performance-related KPIs (Key Performance Indicators) of the E2 node.

[0187] Collecting O1-related data required to perform on / off switching of cells and / or carriers may include the rApp sending an O1-related data collection request to the E2 node via the R1 interface through the NRT-RIC framework and via the O1 interface through the SMO function within the SMO framework; the E2 node receiving the O1-related data collection request from the SMO function; the E2 node collecting O1-related data providing the O1 configuration required to perform on / off switching of cells and / or carriers from the O-RU via the open front-haul management plane (FH M-Plane) interface between the E2 node and the open radio unit (O-RU); and the E2 node sending the O1-related data providing the O1 configuration required to perform on / off switching of cells and / or carriers collected via the O1 interface through the SMO function and the NRT-RIC framework within the SMO framework to the rApp via the R1 interface.

[0188] Instructing the O-RU to perform on / off switching of cells and / or carriers may further include the O-RU notifying the E2 node of the completion of the implementation of on / off switching of cells and / or carriers via the FH M-Plane interface between the E2 node and the O-RU; and the E2 node notifying the rApp of the completion of the implementation of on / off switching of cells and / or carriers via the O1 interface through the SMO function and via the R1 interface through the NRT-RIC framework within the SMO framework.

[0189] The method may further include the NRT-RIC monitoring the performance of the retrained AI / ML model, determining that a predetermined performance goal is not achieved based on the collected O1-related data, and initiating a fallback mechanism and / or an update or retraining of the AI / ML model.

[0190] According to an embodiment, the system and method implement carrier and / or cell on / off switching control that takes into account the energy efficiency of the entire network instead of local optimization in O-RAN. For example, the function of the AI / ML model may include predicting future traffic, user mobility, and resource usage, and predicting the expected energy efficiency improvement, resource usage, and network performance for different energy-saving optimization states.

[0191] As a result, the system and method implement an NRT-RIC framework that allows network operators to flexibly configure the on / off switching parameters of carriers and / or cells in a cell or a cluster of cells to optimize the energy efficiency of the entire network instead of local optimization in O-RAN.

[0192] The above disclosure provides illustration and description, but is not intended to be exhaustive or to limit implementation to the exact form disclosed. Changes and modifications are possible in light of the above disclosure or may be obtained from practice of the implementation.

[0193] Some embodiments may relate to systems, methods, and / or computer-readable media at any possible level of technical detail of integration. Furthermore, one or more of the above-described components may be implemented as instructions stored on a computer-readable medium and executable by at least one processor (and / or may include at least one processor). The computer-readable medium may include a computer-readable non-transitory storage medium (or medium) having computer-readable program instructions stored thereon for causing a processor to perform operations.

[0194] A computer-readable storage medium may be a tangible device that can hold and store instructions for use by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination thereof. A non-exhaustive list of more specific examples of computer-readable storage media includes the following: portable computer diskettes, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROMs or flash memories), static random access memories (SRAMs), portable compact disk read-only memories (CD-ROMs), digital versatile disks (DVDs), memory sticks, floppy disks, punch cards or mechanically encoded devices such as raised structures in grooves with instructions recorded therein, and any suitable combination thereof. As used herein, a computer-readable storage medium is not to be understood as a transitory signal per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., light pulses passing through a fiber optic cable), or electrical signals transmitted over wires.

[0195] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to respective computing / processing devices, or can be downloaded from an external computer or an external storage device via a network such as the Internet, a local area network, a wide area network, and / or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and transfers the computer-readable program instructions for storage in a computer-readable storage medium within each respective computing / processing device.

[0196] The computer-readable program code / instructions for performing the operation may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, configuration data for an integrated circuit, or source code or object code written in any combination of one or more programming languages including object-oriented programming languages such as Smalltalk, C++, and procedural programming languages such as the "C" programming language or similar programming languages. The computer-readable program instructions may be executed entirely on the user's computer as a stand-alone software package, partially on the user's computer, partially on the user's computer, partially on a remote computer, or entirely on a remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (e.g., through the Internet using an Internet service provider). In some embodiments, for example, an electronic circuit including a programmable logic circuit, a field-programmable gate array (FPGA), or a programmable logic array (PLA) may execute the computer-readable program instructions by utilizing the state information of the computer-readable program instructions to personalize the electronic circuit for performing the aspect or operation.

[0197] These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, implement the functions / acts specified in the flowchart and / or block diagram (one or more blocks). These computer-readable program instructions may be stored in a computer-readable storage medium that, when containing instructions that implement aspects of the functions / acts specified in the flowchart and / or block diagram (one or more blocks), causes a computer, programmable data processing apparatus, and / or other device to function in a particular manner.

[0198] The computer-readable program instructions may be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus, or other device to produce a computer-implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions / acts specified in the flowchart and / or block diagram (one or more blocks).

[0199] The flowchart and block diagrams shown illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer-readable media according to various embodiments. Here, each block in the flowchart or block diagram may represent a micro-service, module, segment, or portion of instructions that includes one or more executable instructions for implementing a particular logical function. The methods, computer systems, and computer-readable media may include additional blocks, fewer blocks, different blocks, or differently arranged blocks than those shown in the figures. In some alternative implementations, the functions shown in the blocks may occur out of the order shown in the figures. For example, two blocks shown in succession may, in fact, be executed simultaneously or substantially simultaneously, depending on the functions involved, or the blocks may be executed in the reverse order. Note that each block of the illustrations of the block diagrams and / or flowcharts, and combinations of blocks in the illustrations of the block diagrams and / or flowcharts, can be implemented by a system based on dedicated hardware for performing a particular function or action, or by a combination of dedicated hardware and computer instructions.

[0200] It is apparent that the systems and / or methods described herein may be implemented in different forms of hardware, firmware, or a combination of hardware and software. The actual dedicated control hardware or software code used to implement these systems and / or methods does not limit the implementation. Thus, the operation and behavior of the systems and / or methods are described herein without reference to a particular software code. It is understood that software and hardware may be designed to implement the systems and / or methods based on the description herein.

Claims

1. In an Open Radio Access Network (O-RAN), a system for implementing optimization of on / off switching of carriers and / or cells by a Service Management and Orchestration (SMO) framework, comprising: a memory for storing instructions; at least one processor configured to implement a Non-Real-Time RAN Intelligent Controller (NRT-RIC), an NRT-RIC framework, at least one SMO function, and an rApp hosted by the NRT-RIC; wherein, the at least one processor: collects, via an Open Front-Haul Management Plane (FH M-Plane) interface between an E2 node and an Open Radio Unit (O-RU), O1-related data, which is O1-related data provided by an rApp for providing an O1 configuration necessary for performing on / off switching of the cell and / or carrier, and collects the O1-related data from the E2 node via an R1 interface through the NRT-RIC framework and via an O1 interface through the SMO function within the SMO framework; retrains, by the SMO, at least one Artificial Intelligence / Machine Learning (AI / ML) model based on the collected O1-related data; deploys and activates, by the rApp, one retrained AI / ML model for inferring data for providing an O1 configuration necessary for performing on / off switching of the cell and / or carrier within the O-RAN from among the at least one retrained AI / ML; monitors, by the rApp, the O1-related data for providing an O1 configuration necessary for performing on / off switching of the cell and / or carrier via the R1 interface through the NRT-RIC framework and via the O1 interface through the SMO function within the SMO framework; evaluates, by the rApp, the O1-related data for providing an O1 configuration necessary for performing on / off switching of the cell and / or carrier; determines, by the rApp, to generate O1 configuration data for preparing and performing on / off switching of the cell and / or carrier. The rApp sends the O1 configuration data for preparing and executing the on / off switching of the cell and / or carrier through the NRT-RIC framework via the R1 interface and via the at least one SMO function in the SMO framework to the at least one E2 node. The E2 node and the O-RU implement the on / off switching of the cell and / or carrier in the O-RAN. configured to execute the instructions for During implementation, the at least one processor The E2 node converts the O1 configuration data for preparing and executing the on / off switching of the cell and / or carrier. The E2 node instructs the O-RU to execute the on / off switching of the cell and / or carrier via the open FH M-Plane. A system further configured as such. [

2. ] During the retraining of at least one AI / ML model, the at least one processor The rApp selects one AI / ML model from a plurality of AI / ML models. The rApp sends a start request for retraining the AI / ML model to the NRT-RIC framework. The NRT-RIC framework retrains the AI / ML model. The rApp monitors the retrained AI / ML model parameters and determines the retrieval of the retrained AI / ML model from the NRT-RIC framework based on the retrained AI / ML model parameters. The rApp requests the retrained AI / ML model from the NRT-RIC framework. The NRT-RIC framework sends the retrained AI / ML model to the rApp. The system according to claim 1, further configured as such. [

3. ] The system according to claim 1, wherein during the retraining of at least one AI / ML model, the at least one processor is further configured by the rApp to retrain one AI / ML model from the plurality of AI / ML models.

4. The O1-related data providing the O1 configuration necessary to perform the on / off switching of the cell and / or carrier includes at least one of configuration, performance indicator, and measurement report provided by the O-RU, The measurement report includes at least one of cell load-related information, traffic information, and energy efficiency / energy consumption (EE / EC) measurement report, The energy efficiency / energy consumption (EE / EC) measurement report includes at least one of the energy consumption of the E2 node, the energy consumption of the O-RU, and one or more performance-related KPIs (Key Performance Indicator) of the E2 node, The system according to claim 1.

5. During the collection of the O1-related data providing the O1 configuration necessary to perform the on / off switching of the cell and / or carrier by the at least one processor, the rApp sends an O1-related data collection request to the E2 node through the NRT-RIC framework via the R1 interface and through the SMO function within the SMO framework via the O1 interface, the E2 node receives the O1-related data collection request from the SMO function, the E2 node collects the O1-related data providing the O1 configuration necessary to perform the on / off switching of the cell and / or carrier from the O-RU via the open front-haul management plane (FH M-Plane) interface between the E2 node and the open radio unit (O-RU), the E2 node sends the O1-related data providing the O1 configuration necessary to perform the on / off switching of the cell and / or carrier collected via the O1 interface through the SMO function within the SMO framework and the NRT-RIC framework to the rApp via the R1 interface, The system according to claim 1, configured as such.

6. During the at least one processor instructing the O-RU to perform the on / off switching of the cell and / or carrier via the open FH M-Plane, The O-RU notifies the completion of the implementation of the on / off switching of the cell and / or carrier to the E2 node via the FH M-Plane interface between the E2 node and the O-RU. The E2 node notifies the rApp of the completion of the implementation of the on / off switching of the cell and / or carrier via the O1 interface through the SMO function and via the R1 interface through the NRT-RIC framework within the SMO framework. The system according to claim 1, further configured as described above.

7. The at least one processor is The NRT-RIC monitors the performance of the retrained AI / ML model. Based on the collected O1-related data, it is determined that a predetermined performance target is not achieved. It starts a fallback mechanism and / or updates or retrains the AI / ML model. The system according to claim 1, further configured as described above.

8. In an Open Radio Access Network (O-RAN), a method for implementing optimization of on / off switching of carriers and / or cells by a Service Management and Orchestration (SMO) framework, comprising: The rApp collects O1-related data, which is O1 configuration necessary for performing the on / off switching of the cell and / or carrier, from the E2 node via the R1 interface through the NRT-RIC framework and via the O1 interface through the SMO function within the SMO framework, the O1-related data being collected via the open front-haul management plane (FH M-Plane) interface between the E2 node and the Open Radio Unit (O-RU). The SMO retrains at least one Artificial Intelligence / Machine Learning (AI / ML) model based on the collected O1-related data. The rApp deploys and activates one retrained AI / ML model for inferring data for providing an O1 configuration necessary for performing on / off switching of the cell and / or carrier within the O-RAN among the at least one retrained AI / MLs. The rApp monitors the O1-related data for providing an O1 configuration necessary for performing on / off switching of the cell and / or carrier via the R1 interface through the NRT-RIC framework and via the O1 interface through the SMO function within the SMO framework. The rApp evaluates the O1-related data for providing an O1 configuration necessary for performing on / off switching of the cell and / or carrier. The rApp determines to generate O1 configuration data for preparing and performing on / off switching of the cell and / or carrier. The rApp sends the O1 configuration data for preparing and performing on / off switching of the cell and / or carrier to the at least one E2 node via the R1 interface through the NRT-RIC framework and via the O1 interface through at least one SMO function within the SMO framework. The E2 node and the O-RU implement on / off switching of the cell and / or carrier within the O-RAN. comprising The implementing The E2 node converts the O1 configuration data for preparing and performing on / off switching of the cell and / or carrier. The E2 node instructs the O-RU to perform on / off switching of the cell and / or carrier via the open FH M-Plane. A method comprising. Claim 9 The retraining of the at least one AI / ML model The rApp selects one AI / ML model from a plurality of AI / ML models. The rApp sends a start request for retraining the AI / ML model to the NRT-RIC framework. Retraining the AI / ML model by the NRT-RIC framework, and monitoring, by the rApp, the retrained AI / ML model parameters and determining, based on the retrained AI / ML model parameters, the retrieval of the retrained AI / ML model from the NRT-RIC framework; and requesting, by the rApp, the retrained AI / ML model from the NRT-RIC framework; and sending, by the NRT-RIC framework, the retrained AI / ML model to the rApp; The method according to claim 8, comprising the above.

10. The method according to claim 8, wherein the retraining of the at least one AI / ML model comprises retraining, by the rApp, one AI / ML model from the plurality of AI / ML models.

11. The O1-related data providing the O1 configuration required to perform the on / off switching of the cell and / or carrier comprises at least one of configuration, performance indicator, and measurement report provided by the O-RU. The measurement report comprises at least one of cell load related information, traffic information, and energy efficiency / energy consumption (EE / EC) measurement report. The energy efficiency / energy consumption (EE / EC) measurement report comprises at least one of the energy consumption of the E2 node, the energy consumption of the O-RU, and one or more performance related KPIs (Key Performance Indicator) of the E2 node. The method according to claim 8.

12. Collecting the O1-related data providing the O1 configuration required to perform the on / off switching of the cell and / or carrier comprises: sending, by the rApp, an O1-related data collection request to the E2 node through the R1 interface via the NRT-RIC framework and through the O1 interface via the SMO function within the SMO framework; and receiving, by the E2 node, the O1-related data collection request from the SMO function. The E2 node collects, from the O-RU, the O1-related data that provides the O1 configuration necessary to perform on / off switching of the cell and / or carrier via an open front-haul management plane (FH M-Plane) interface between the E2 node and the open radio unit (O-RU). The E2 node sends, via the R1 interface, to the rApp, the O1-related data that provides the O1 configuration necessary to perform on / off switching of the cell and / or carrier, which is collected via the O1 interface through the SMO function in the SMO framework and through the NRT-RIC framework in the SMO framework. The method according to claim 8, comprising the above.

13. Instructing the O-RU to perform on / off switching of the cell and / or carrier means The O-RU notifies the E2 node, via the FH M-Plane interface between the E2 node and the O-RU, of the completion of the implementation of the on / off switching of the cell and / or carrier. The E2 node notifies the rApp, via the O1 interface through the SMO function and via the R1 interface through the NRT-RIC framework in the SMO framework, of the completion of the implementation of the on / off switching of the cell and / or carrier. The method according to claim 8, further comprising the above.

14. The NRT-RIC monitors the performance of the retrained AI / ML model. Based on the collected O1-related data, it is determined that a predetermined performance target is not achieved. Initiating a fallback mechanism and / or an update or retraining of the AI / ML model The method according to claim 8, further comprising the above.

15. In an Open Radio Access Network (O-RAN), a non-transitory computer-readable recording medium having instructions recorded thereon that are executable by at least one processor configured to implement a non-real-time RAN intelligent controller (NRT-RIC), an NRT-RIC framework, at least one SMO function, and an rApp hosted by the NRT-RIC, for executing a method for implementing optimization of on / off switching of carriers and / or cells by a service management and orchestration (SMO) framework, The method includes: collecting, by the rApp, O1-related data that provides an O1 configuration necessary for performing on / off switching of the cell and / or carrier, the O1-related data collected via an open front-haul management plane (FH M-Plane) interface between an E2 node and an open radio unit (O-RU), from the E2 node via an R1 interface through the NRT-RIC framework and via an O1 interface through the SMO function within the SMO framework; retraining, by the SMO, at least one artificial intelligence / machine learning (AI / ML) model based on the collected O1-related data; deploying and activating, by the rApp, one of the at least one retrained AI / ML models for inferring data that provides an O1 configuration necessary for performing on / off switching of the cell and / or carrier within the O-RAN; monitoring, by the rApp, the O1-related data that provides an O1 configuration necessary for performing on / off switching of the cell and / or carrier via the R1 interface through the NRT-RIC framework and via the O1 interface through the SMO function within the SMO framework; evaluating, by the rApp, the O1-related data that provides an O1 configuration necessary for performing on / off switching of the cell and / or carrier. The rApp determines to generate O1 configuration data for preparing and executing on / off switching of the cell and / or carrier; The rApp sends the O1 configuration data for preparing and executing on / off switching of the cell and / or carrier to the at least one E2 node via the R1 interface through the NRT-RIC framework and via the O1 interface through the at least one SMO function within the SMO framework; The E2 node and the O-RU implement on / off switching of the cell and / or carrier within the O-RAN; comprising; The implementing comprises: The E2 node converts the O1 configuration data for preparing and executing on / off switching of the cell and / or carrier; The E2 node instructs the O-RU to execute on / off switching of the cell and / or carrier via the open FH M-Plane; A non-transitory computer-readable recording medium comprising.

16. The re-training of the at least one AI / ML model comprises: The rApp selects one AI / ML model from a plurality of AI / ML models; The rApp sends a start request for re-training the AI / ML model to the NRT-RIC framework; The NRT-RIC framework re-trains the AI / ML model; The rApp monitors the re-trained AI / ML model parameters and determines the recovery of the re-trained AI / ML model from the NRT-RIC framework based on the re-trained AI / ML model parameters; The rApp requests the re-trained AI / ML model from the NRT-RIC framework; The NRT-RIC framework sends the re-trained AI / ML model to the rApp; The non-transitory computer-readable recording medium according to claim 15, comprising.

17. The retraining of the at least one AI / ML model comprises retraining one AI / ML model from the plurality of AI / ML models by the rApp, the non-transitory computer-readable recording medium according to claim 15.

18. The O1-related data providing the O1 configuration required to perform the on / off switching of the cell and / or carrier comprises at least one of a configuration, a performance indicator, and a measurement report provided by the O-RU, The measurement report comprises at least one of cell load-related information, traffic information, and an energy efficiency / energy consumption (EE / EC) measurement report, The energy efficiency / energy consumption (EE / EC) measurement report comprises at least one of the energy consumption of the E2 node, the energy consumption of the O-RU, and one or more performance-related KPIs (Key Performance Indicators) of the E2 node. The non-transitory computer-readable recording medium according to claim 15.

19. Collecting the O1-related data providing the O1 configuration required to perform the on / off switching of the cell and / or carrier comprises sending, by the rApp, an O1-related data collection request to the E2 node via the R1 interface through the NRT-RIC framework and via the O1 interface through the SMO function within the SMO framework; receiving, by the E2 node, the O1-related data collection request from the SMO function; collecting, by the E2 node, the O1-related data providing the O1 configuration required to perform the on / off switching of the cell and / or carrier from the O-RU via an open front-haul management plane (FH M-Plane) interface between the E2 node and the open radio unit (O-RU). The E2 node sends, via the R1 interface, to the rApp the O1-related data that provides the O1 configuration necessary to perform the on / off switching of the cells and / or carriers collected via the O1 interface through the SMO function and the NRT-RIC framework within the SMO framework, The non-transitory computer-readable recording medium according to claim 15, comprising [

20. ] Instructing the O-RU to perform the on / off switching of the cells and / or carriers is The O-RU notifies the E2 node, via the FH M-Plane interface between the E2 node and the O-RU, of the completion of the implementation of the on / off switching of the cells and / or carriers, The E2 node notifies the rApp, via the O1 interface through the SMO function and via the R1 interface through the NRT-RIC framework within the SMO framework, of the completion of the implementation of the on / off switching of the cells and / or carriers, The non-transitory computer-readable recording medium according to claim 15, further comprising

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