A system and method for optimizing the on / off switching of carriers and / or cells in a communication network.

Abstract

This invention provides a system, method, and recording medium for optimizing the on / off switching of carriers and / or cells in an open radio access network (O-RAN) using a service management and orchestration (SMO) framework. [Solution] The method collects O1-related data from an E2 node that provides the O1 configuration necessary to perform on / off switching of cells and / or carriers, retrains an AI / ML model based on the O1-related data, deploys and activates a retrained AI / ML model to infer the data that provides the O1 configuration, monitors the O1-related data, evaluates the O1-related data, decides to generate O1 configuration data for preparing and performing on / off switching of cells and / or carriers and sends it to the E2 node, and implements on / off switching of cells and / or carriers in O-RAN.

Description

Technical Field

[0001] Embodiments consistent with 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] The emergence of open RAN (O-RAN) technology has enabled multiple vendors to provide hardware and / or software for communication systems. 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 sublayers 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 sublayers 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] Figure 1 illustrates an O-RAN architecture in related technologies. Referring to Figure 1, the RAN functionality in the O-RAN architecture is controlled and optimized by the RIC. The RIC is a software-defined component that implements modular applications to achieve the multi-vendor operability required in the O-RAN system and to automate and optimize RAN operations. RICs are divided into two types: non-real-time RICs (NRT-RICs) and near-real-time RICs (nRT-RICs).

[0005] The NRT-RIC is the control point of the non-real-time control loop and operates on a timescale longer than one second within the Service Management and Orchestration (SMO) framework. Its functions are implemented through modular applications called rApps (rApp 1, ..., rApp N) and include providing policy-based guidance and enrichment across the A1 interface, which is an interface enabling communication between the NRT-RIC and 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 connecting the SMO to RAN management elements (e.g., nRT-RIC, O-RAN Aggregation Unit (O-CU), O-RAN Distributed Unit (O-DU), etc.).

[0006] The nRT-RIC operates on timescales between 10 milliseconds and 1 second and connects to the O-DU, O-CU (which is broken down into the O-CU Control Plane (O-CU-CP) and O-CU User Plane (O-CU-UP)), and open evolved NodeB (O-eNB) via the E2 interface. The nRT-RIC uses the E2 interface to control the underlying RAN elements (E2 Node / Network Function (NF)) 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 activated functions of the E2 nodes. Furthermore, the nRT-RIC hosts xApps to implement functions such as Quality of Service (QoS) optimization, mobility optimization, slicing optimization, interference mitigation, load balancing, and security. The two types of RICs work together to optimize the O-RAN. For example, NRT-RIC provides policies, data, and artificial intelligence / machine learning (AI / ML) models enabled and used by nRT-RIC for RAN optimization via the A1 interface, and nRT-RIC returns policy feedback (i.e., how the policies set by NRT-RIC work).

[0007] The SMO framework on which NRT-RIC resides manages and coordinates RAN elements. Specifically, the SMO manages and coordinates what is referred to as the O-RAN Cloud (O-Cloud). The O-Cloud is a collection of RICs, O-CUs, and O-DUs, supporting software components (e.g., operating systems and runtime environments), and physical RAN nodes that host 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 on which it resides. Through the O2 interface, the SMO provides Infrastructure Management Services (IMS) and Deployment Management Services (DMS).

[0008] On the other hand, O-Cloud is a cloud computing platform that comprises a collection of physical infrastructure nodes that meet O-RAN specifications to host relevant O-RAN functions (e.g., nRT-RIC, O-CU-CP, O-CU-UP, O-DU, etc.), supporting software components (operating systems, virtual machine monitors, container runtimes, etc.), and appropriate management and orchestration functions.

[0009] The SMO framework, in which the NRT-RIC resides, manages and coordinates RAN elements. The SMO performs management and orchestration of 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 O-RAN network functions; the open fronthaul M-plane interface for FCAPS support between the SMO and O-RU in the case of a hybrid model; and the O2 interface for platform resource and workload management between the SMO and O-Cloud.

[0010] In related technologies, O-RAN utilizes multiple frequency layers (carriers) to cover its service area. When network traffic load is low (for example, when the expected traffic volume is below a fixed threshold), energy savings (i.e., high energy efficiency and / or low energy consumption) can be achieved by shutting down (i.e., switching off) one or more carriers or entire cells without compromising the O-RAN user experience. When shutting down (i.e., switching off) one or more carriers or entire cells, O-RAN users (i.e., user equipment (UEs) within O-RAN) that were previously served by one or more carriers or cells are offloaded by the E2 node to one or more new target carriers or cells before the shutdown (i.e., switching off). [Overview of the Initiative] [Problems that the invention aims to solve]

[0011] In terms of the local or network-wide impact of a 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, which changes over time. Furthermore, E2 nodes may have to support several technologies that affect energy consumption and may depend on the load (e.g., network traffic or number of users).

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

[0013] According to the embodiment, a system and method for implementing a service management and orchestration (SMO) framework are provided. The SMO framework comprises 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 performing cell and / or carrier on / off switching by one or more E2 nodes. With the assistance of artificial intelligence / machine learning (AI / ML) techniques, O1-related data from the E2 nodes (O-RUs) is used for retraining, deploying, and activating AI / ML models to infer data that provides the O1 configuration necessary to perform cell and / or carrier on / off switching within the O-RAN. Carrier and cell on / off switching control (i.e., monitoring at least one O1-related data that provides the necessary O1 configuration to perform cell and / or carrier on / off switching via the R1 interface through the NRT-RIC framework and via the O1 interface through at least one SMO function in the SMO framework) considers network-wide energy efficiency instead of local optimization in O-RAN.

[0014] For example, the capabilities of the AI / ML model may include predicting future traffic, user mobility, and resource usage, and may also predict expected energy efficiency improvements, resource usage, and network performance under different energy-saving optimization states.

[0015] As a result, the system and method implement an NRT-RIC framework that allows network operators to flexibly configure carrier and / or cell on / off switching parameters in cells or clusters of cells to optimize the energy efficiency of the entire network, instead of local optimization in O-RAN.

[0016] According to one embodiment, a system is provided for implementing optimization of carrier and / or cell on / off switching by a service management and orchestration (SMO) framework in an open radio access network (O-RAN). The system includes 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, via rApp, collects O1-related data that provides the O1 configuration necessary to perform cell and / or carrier on / off switching, collected 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, and SMO retrains at least one artificial intelligence / machine learning (AI / ML) model based on the collected O1-related data, and rApp deploys and activates one of the retrained AI / ML models from the at least one retrained AI / ML to infer data that provides the O1 configuration necessary to perform cell and / or carrier on / off switching within the O-RAN, and rApp retrains at least one artificial intelligence / machine learning (AI / ML) model based on the collected O1-related data and SMO deploys and activates one of the retrained AI / ML models to infer data that provides the O1 configuration necessary to perform cell and / or carrier on / off switching within the O-RAN, and rApp retrains at least one artificial intelligence / machine learning (AI / ML) model based on the collected O1-related data and SMO deploys and activates one of the retrained AI / ML models to infer data that provides the O1 configuration necessary to perform cell and / or carrier on / off switching The rApp monitors O1-related data that provides the O1 configuration necessary to perform carrier on / off switching, evaluates the O1-related data that provides the O1 configuration necessary to perform cell and / or carrier on / off switching, decides to generate O1 configuration data for preparing and performing cell and / or carrier on / off switching, sends the O1 configuration data for preparing and performing cell and / or carrier on / off switching 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 is configured to execute instructions to implement cell and / or carrier on / off switching in the O-RAN by the E2 node and O-RU.At least one processor is further configured during implementation to translate O1 configuration data by the E2 node to prepare and perform cell and / or carrier on / off switching, and to instruct the O-RU via open FH M-Plane to perform cell and / or carrier on / off switching.

[0017] At least one processor may be further configured to, during the retraining of at least one AI / ML model, select one AI / ML model from multiple AI / ML models via rApp, send a start request to the NRT-RIC framework to retrain the AI / ML model via rApp, retrain the AI / ML model via the NRT-RIC framework, monitor the parameters of the retrained AI / ML model via rApp, decide to retrieve the retrained AI / ML model from the NRT-RIC framework based on the parameters of the retrained AI / ML model, request the retrained AI / ML model from the NRT-RIC framework via rApp, and send the retrained AI / ML model to rApp via the NRT-RIC framework.

[0018] At least one processor may be further configured to retrain one AI / ML model from multiple AI / ML models via rApp while at least one AI / ML model is being retrained.

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

[0020] At least one processor may be configured such that, during the collection of O1-related data that provides the O1 configuration necessary to perform cell and / or carrier on / off switching, the rApp sends 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 in 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 cell and / or carrier on / off switching from the O-RU via the open fronthaul management plane (FH M-Plane) interface between the E2 node and the open radio unit (O-RU); and the E2 node sends the O1-related data that provides the O1 configuration necessary to perform cell and / or carrier on / off switching, collected via the O1 interface through the SMO function in the SMO framework and the NRT-RIC framework, to the rApp via the R1 interface.

[0021] At least one processor may be further configured to instruct the O-RU via open FH M-Plane to perform cell and / or carrier on / off switching, while 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 cell and / or carrier on / off switching implementation, and 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 cell and / or carrier on / off switching implementation.

[0022] At least one processor may be further configured to monitor the performance of the retrained AI / ML model by the NRT-RIC, determine that a predetermined performance target is not being met based on the collected O1-related data, and initiate a fallback mechanism and / or update or retrain the AI / ML model.

[0023] According to one embodiment, a method is provided for implementing optimization of carrier and / or cell on / off switching by a service management and orchestration (SMO) framework in an open radio access network (O-RAN).The method involves collecting O1-related data, which provides the O1 configuration necessary to perform cell and / or carrier on / off switching, via an open fronthaul management plane (FH M-Plane) interface between the E2 node and the open radio unit (O-RU) using an rApp, via the R1 interface through the NRT-RIC framework, and via the O1 interface through the SMO function within the SMO framework; retraining at least one artificial intelligence / machine learning (AI / ML) model based on the collected O1-related data using an SMO; deploying and activating one of the retrained AI / ML models, from at least one retrained AI / ML, to infer data providing the O1 configuration necessary to perform cell and / or carrier on / off switching within the O-RAN using an rApp; and retraining at least one artificial intelligence / machine learning (AI / ML) model based on the collected O1-related data using an SMO using an rApp, and deploying and activating one of the retrained AI / ML models to infer data providing the O1 configuration necessary to perform cell and / or carrier on / off switching within the O-RAN using an rApp, and This includes monitoring O1-related data that provides the O1 configuration necessary to perform on / off switching of / or carriers, evaluating the O1-related data by rApp that provides the O1 configuration necessary to perform on / off switching of cells and / or carriers, deciding by rApp to generate O1 configuration data for preparing and performing on / off switching of cells and / or carriers, sending the O1 configuration data for preparing and performing on / off switching of cells and / or carriers to at least one E2 node by rApp 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 implementing on / off switching of cells and / or carriers in O-RAN by the E2 node and O-RU.The implementation may include the E2 node converting O1 configuration data for preparing and performing cell and / or carrier on / off switching, and the E2 node instructing the O-RU via open FH M-Plane to perform cell and / or carrier on / off switching.

[0024] Retraining at least one AI / ML model may include: selecting one AI / ML model from multiple AI / ML models using an rApp; sending a start request to the NRT-RIC framework for retraining the AI / ML model using an rApp; retraining the AI / ML model using the NRT-RIC framework; monitoring the parameters of the retrained AI / ML model using an rApp and deciding to retrieve the retrained AI / ML model from the NRT-RIC framework based on the parameters of the retrained AI / ML model using an rApp; requesting the retrained AI / ML model from the NRT-RIC framework using an rApp; and sending the retrained AI / ML model to the rApp using the NRT-RIC framework.

[0025] Retraining at least one AI / ML model may include retraining one AI / ML model from multiple AI / ML models using rApp.

[0026] 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 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.

[0027] Collecting the O1-related data that provides the O1 configuration necessary to perform 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 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.

[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 one embodiment, a non-real-time RAN intelligent controller (NRT-RIC), an NRT-RIC framework, at least one SMO function, and a non-temporary computer-readable recording medium on which instructions executable by at least one processor configured to implement an rApp hosted by the NRT-RIC are recorded are provided for performing a method for implementing the optimization of carrier and / or cell on / off switching by a service management and orchestration (SMO) framework in an open radio access network (O-RAN).The method involves collecting O1-related data, which provides the O1 configuration necessary to perform cell and / or carrier on / off switching, via an open fronthaul management plane (FH M-Plane) interface between the E2 node and the open radio unit (O-RU) using an rApp, via the R1 interface through the NRT-RIC framework and via the O1 interface through the SMO function within the SMO framework; retraining at least one artificial intelligence / machine learning (AI / ML) model based on the collected O1-related data using an SMO; deploying and activating one of the retrained AI / ML models from at least one retrained AI / ML to infer data providing the O1 configuration necessary to perform cell and / or carrier on / off switching within the O-RAN using an rApp; and retraining at least one artificial intelligence / machine learning (AI / ML) model based on the collected O1-related data using an SMO using an rApp, via the R1 interface through the NRT-RIC framework and via the O1 interface through the SMO function within the SMO framework. This may also include monitoring O1-related data that provides the O1 configuration necessary to perform on / off switching of carriers, evaluating the O1-related data that provides the O1 configuration necessary to perform on / off switching of cells and / or carriers by rApp, deciding by rApp to generate O1 configuration data for preparing and performing on / off switching of cells and / or carriers, sending the O1 configuration data for preparing and performing on / off switching of cells and / or carriers to at least one E2 node by rApp 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 implementing on / off switching of cells and / or carriers in O-RAN by the E2 node and O-RU.The implementation may include the E2 node converting O1 configuration data for preparing and performing cell and / or carrier on / off switching, and the E2 node instructing the O-RU via open FH M-Plane to perform cell and / or carrier on / off switching.

[0031] Retraining at least one AI / ML model may include retraining one AI / ML model from multiple AI / ML models using rApp.

[0032] O1-related data that provides the O1 configuration necessary to perform cell and / or carrier on / off switching may include configuration, performance indicators, and measurement reports provided by O-RUs. Measurement reports may include cell load-related information, traffic information, and at least one energy efficiency / energy consumption (EE / EC) measurement report. Energy efficiency / energy consumption (EE / EC) measurement reports may include energy consumption of E2 nodes, energy consumption of O-RUs, and at least one of one or more performance-related KPIs (Key Performance Indicators) of E2 nodes.

[0033] Collecting O1-related data that provides the O1 configuration necessary to perform cell and / or carrier on / off switching 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 in the SMO framework; the E2 node receiving the O1-related data collection request from the SMO function; the E2 node collecting O1-related data from the O-RU that provides the O1 configuration necessary to perform cell and / or carrier on / off switching via the open fronthaul 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 cell and / or carrier on / off switching, collected via the O1 interface through the SMO function in the SMO framework and the NRT-RIC framework, to the rApp via the R1 interface.

[0034] Instructing the O-RU to perform cell and / or carrier on / off switching may further include the O-RU notifying the E2 node of the completion of the cell and / or carrier on / off switching implementation 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 cell and / or carrier on / off switching implementation 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 the performance of the retrained AI / ML model by NRT-RIC, determining that a predetermined performance target is not being met based on the collected O1-related data, and initiating a fallback mechanism and / or updating or retraining the AI / ML model.

[0036] Additional aspects may be partially presented in the following description, partially revealed from the description, or realized by the implementation of the embodiments presented in the disclosure. [Brief explanation of the drawing]

[0037] Features, aspects, and advantages of certain exemplary embodiments of the disclosure are described below with reference to the accompanying drawings, where similar reference numerals represent similar elements.

[0038] Figure 1 illustrates an O-RAN architecture in related technologies.

[0039] Figure 2 is a diagram illustrating an example environment in which the system and / or methods described herein may be implemented.

[0040] Figure 3 shows an example of a device component according to one embodiment.

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

[0042] Figure 5 is a flowchart of a method for implementing the optimization of carrier and / or cell on / off switching according to one embodiment.

[0043] Figure 6 illustrates a data collection flow according to one embodiment.

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

[0045] Figure 8 illustrates data analysis, AI / ML model training, and inference flows according to another embodiment.

[0046] Figure 9 illustrates the generation and implementation of O1 configuration data for preparing and executing on / off switching of cells and / or carriers according to one embodiment. [Modes for carrying out the invention]

[0047] The following detailed descriptions of embodiments refer to the accompanying drawings. The prior disclosures provide examples and descriptions, but are not intended to be exhaustive or to limit implementations to the exact forms disclosed. Modifications and alterations are possible in light of the prior disclosures or may be obtained from the implementation. Furthermore, one or more features or components of one embodiment may be integrated with or combined with other embodiments (or one or more features of other embodiments). In addition, in the flowcharts and descriptions of operations provided below, it is understood that one or more operations may be omitted, one or more operations may be added, one or more operations may be performed simultaneously (at least partially), and the order of one or more operations may be changed.

[0048] It will become clear that the systems and / or methods described herein may be implemented in different forms of hardware, firmware, or combinations of hardware and software. The actual specific control hardware or software code used to implement these systems and / or methods is not an implementation limitation. For this reason, the operation and behavior 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 to implement the systems and / or methods based on the descriptions herein.

[0049] Even if certain combinations of features are described in the claims 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 described in the claims and / or 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 group of claims.

[0050] Any element, action, or command used herein should not be interpreted as important or essential unless explicitly stated otherwise. 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." When only one item is intended, the term "one" or similar is used. Also, as used herein, the terms "has," "have," "having," "include," "including," etc., are intended to be open-ended terms. Furthermore, the phrase "based on" means "at least partially based on" unless explicitly stated otherwise. Furthermore, 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 this disclosure provide a system and method in which the NRT-RIC framework and / or rApp configure carrier and / or cell on / off switching parameters (i.e., carrier and / or cell on / off switching parameters) (i.e., NRT-RIC and at least one rApp hosted by the NRT-RIC framework consume and / or generate O1-related services that constitute O1-related data providing the O1 configuration necessary to perform cell and / or carrier on / off switching). For example, by an A1 policy or optimization trigger on the O1 interface to nRT-RIC defined by NRT-RIC (i.e., by at least one rApp hosted by NRT-RIC and / or the NRT-RIC framework supported by machine learning (ML) techniques), the NRT-RIC framework and / or rApp (e.g., at least one rApp hosted by NRT-RIC and / or the NRT-RIC framework) enable flexible configuration of carrier and / or cell on / off switching parameters in a cell or cluster of cells. Actions of the nRT-RIC via the E2 interface may enable the deployment of O1 configuration data to prepare and execute cell and / or carrier on / off switching for one or more E2 nodes. The implementation based on the O1 configuration data for preparing and executing cell and carrier on / off switching 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 shutting down (i.e., switching off) one or more carriers and / or cells, the E2 node must perform preparatory actions for shutting down (i.e., switching off) one or more carriers and / or cells (for example, the E2 node may check for ongoing emergency calls and / or warning messages that need to be addressed (e.g., enabled, disabled, corrected, etc.) in carrier aggregation and / or dual connectivity, for example, to notify neighboring nodes via the X2 / Xn interface, triggering high-occupancy (HO) data traffic and the migration of UEs from one or more cells and / or carriers to other cells or carriers).

[0053] Furthermore, before switching one or more carriers and / or cells on, the E2 node must perform preparatory actions for the switch (for example, the E2 node may perform cell probing, notify neighboring nodes via the X2 / Xn interface, etc.).

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

[0055] User device 210 includes one or more devices capable of receiving, generating, storing, processing, and / or providing information related to platform 220. For example, user device 210 may include computing devices (e.g., desktop computers, laptop computers, tablet computers, handheld computers, smart speakers, servers, etc.), mobile phones (e.g., smartphones, wireless phones, etc.), wearable devices (e.g., smart glasses or smartwatches), or similar devices. In some implementations, user device 210 may receive information from and / or transmit information to platform 220.

[0056] Platform 220 includes one or more devices capable of receiving, generating, storing, processing, and / or providing information. In some implementations, Platform 220 may include a cloud server or a group of cloud servers. In some implementations, Platform 220 may be designed to be modular so that certain software components can be swapped (in or out) depending on specific needs. Thus, Platform 220 may be easily and / or quickly reconfigured for different applications.

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

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

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

[0060] As further shown in Figure 2, the computing resource 224 includes 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, and one or more hypervisors ("HYP") 224-4.

[0061] Application 224-1 includes one or more software applications that may be provided to or accessed by the user device 210. Application 224-1 may eliminate the need to install and run software applications on the user device 210. For example, Application 224-1 may include any other software that can be provided via the platform 220 and its associated software and / or the cloud computing environment 222. In some implementations, one application 224-1 may send and receive information to and from one or more other applications 224-1 via a virtual machine 224-2.

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

[0063] 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 systems. 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 an abstraction (or isolation) of logical storage from physical storage so that the storage system may be accessed without considering the physical storage or heterogeneous structure. Isolation can provide administrators of the storage system with flexibility in managing storage for end users. File virtualization may remove the dependency between data accessed at the file level and the location where the files are physically stored. This may enable optimized storage usage, server consolidation, and / or performance of non-destructive 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 resource 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 virtualized hardware resources.

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

[0066] The number and arrangement of devices and networks shown in Figure 2 are provided as an example. In practice, there may be additional devices and / or networks, fewer devices and / or networks, different devices and / or networks, or devices and / or networks in different arrangements than those shown in Figure 2. Furthermore, two or more devices shown in Figure 2 may be implemented within a single device, and a single device shown in Figure 2 may be implemented as multiple distributed devices. In addition or alternatively, a set of devices in environment 200 (e.g., one or more devices) may perform one or more functions that are described as being performed by other sets of devices in environment 200.

[0067] Figure 3 shows an example of the components of device 300. Device 300 may correspond to user device 210 and / or platform 220. As shown in Figure 3, device 300 may include a bus 310, a processor 320, memory 330, a storage component 340, an input component 350, an output component 360, and a 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), graphics processing unit (GPU), acceleration unit (APU), microprocessor, microcontroller, digital signal processor (DSP), FPGA (field-programmable gate array), ASIC (application-specific integrated circuit), or other types of processing components. In some implementations, processor 320 includes one or more processors that are programmable to perform 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) that store 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 device 300. For example, the storage component 340 may include, along with a corresponding drive, a hard disk (e.g., magnetic disk, optical disk, magneto-optical disk, and / or solid-state disk), a compact disk (CD), a digital versatile disk (DVD), a floppy disk, a cartridge, magnetic tape, and / or other types of non-temporary computer-readable media. The input component 350 includes components that enable device 300 to receive information via user input (e.g., a touchscreen display, keyboard, keypad, mouse, buttons, switches, and / or microphone), etc. In addition or alternatively, the input component 350 may include sensors for measuring information (e.g., a global positioning system (GPS) component, an accelerometer, a gyroscope, and / or actuators). The output component 360 includes components that provide output information from device 300 (e.g., a display, a speaker, and / or one or more light-emitting diodes (LEDs)).

[0070] The communication interface 370 includes transceiver-like components (e.g., a transceiver and / or separate receiver and transmitter) that enable device 300 to communicate with other devices via wired connections, wireless connections, or a combination of wired and wireless connections. The communication interface 370 enables device 300 to receive information from 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, and the like.

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

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

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

[0074] In the embodiments, any operation or process in Figures 4, 5, 6, 7, and 8 may be implemented by or using any element illustrated in Figures 1, 2, and 3. Other embodiments are understood to be, but are not limited thereto, and may be implemented in a variety of different architectures (e.g., bare metal architecture, any cloud-based architecture, or deployment architectures such as Kubernetes, Docker, or OpenStack).

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

[0076] Referring to Figure 4, NRT-RIC represents a subset of the SMO framework's functions. NRT-RIC can access other SMO framework functions and influence (i.e., control and / or perform) actions performed across the O1 and O2 interfaces (e.g., performing configuration management (CM) and / or performance management (PM)).

[0077] Generally, FCAPS management, software management, and file management are achieved through O1 interfaces for operation and management between management entities (Network Management System (NMS) / Element Management System (EMS) / Management and Organization of Network Function Virtualization (MANO)) and O-RAN management elements.

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

[0079] NRT-RIC includes the NRT-RIC framework. The NRT-RIC framework, in addition to several other functions, includes R1 service exposure functionality that handles R1 services provided according to the embodiment. Generally, the NRT-RIC functions within the NRT-RIC framework support authorization, authentication, registration, discovery, communication support, etc., for rApps.

[0080] Generally, R1 services include, but are not limited to, a set of services including, 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 leverages the capabilities available within the NRT-RIC framework and / or SMO framework to provide value-added services related to RAN operation and optimization. The scope of an rApp includes, but is not limited to, wireless resource management, data analytics, and information enrichment. Generally speaking, 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 the embodiment via the R1 interface. The R1 interface terminates at the R1 termination of the NRT-RIC framework. The R1 termination connects to the NRT-RIC framework and rApp via the R1 interface, enabling the NRT-RIC framework and rApp to exchange messages / data (i.e., requests and responses with data models) in order to access R1 services via the R1 interface.

[0083] In general, the R1 interface is defined as the interface between rApp and the NRT-RIC framework through which R1 services can be generated and consumed.

[0084] Furthermore, the NRT-RIC framework includes A1-related functions. These A1-related functions support, for example, A1 logical termination, A1 policy coordination and cataloging, A1-EI coordination and cataloging, and more.

[0085] The data management and exposure services within the NRT-RIC framework deliver data generated or collected by data generators to data consumers as needed (e.g., function management (FM) / consumption management (CM) / production management (PM) data to rApps or CM changes from rApps to O-RANs via the O1 interface).

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

[0087] Within the NRT-RIC framework, the AI / ML workflow service provides access to AI / ML workflows. For example, the AI / ML workflow service may assist with model training, monitoring of AI / ML models deployed within NRT-RIC, and other similar tasks.

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

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

[0090] The R1 interface is independent of specific implementations of the NRT-RIC framework by SMO and NRT-RIC. The R1 interface is defined in an extensible manner, allowing new services and data types to be added without requiring changes to the protocol or procedures.

[0091] In particular, the R1 interface enables interconnection between rApp 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 rApp and NRT-RIC frameworks and / or SMO frameworks.

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

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

[0094] Furthermore, before switching one or more carriers and / or cells on, the E2 node must perform preparatory actions for the switch (for example, the E2 node may perform cell probing, notify neighboring nodes via the X2 / Xn interface, etc.).

[0095] Referring to Figure 4, the SMO and NRT-RIC framework is configured to collect configuration, performance indicators, and measurement reports (e.g., cell load-related information and traffic information, energy efficiency (EE) and / or energy consumption (EC) measurement reports, geolocation information, etc.) from E2 nodes and O-RUs (via E2 nodes forwarded by the SMO) for decision-making purposes (for example, the SMO and NRT-RIC framework may constitute R1 / O1 consumption and / or generation services). Decision-making may be based, for example, on the use of training and inference of AI / ML models to 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, while energy consumption (EC) is defined as the integral of power consumption over time.

[0097] Furthermore, the SMO and NRT-RIC framework forwards collected data to one or more rApps hosted by NRT-RIC, signals (i.e., sends) updated configurations for energy efficiency (EE) / energy saving (ES) optimization to E2 nodes (O-CUs) via the R1 / O1 interface, analyzes 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 configures one or more E2 nodes and O-RUs to determine EE / ES optimization (i.e., whether one or more carriers and / or cells are recommended to be switched on / off) (for example, the SMO and NRT-RIC framework may configure R1 / O1 consumption and / or generation services). The EE / ES optimization decision (i.e., recommendation on which of one or more carriers and / or cells may be switched on / off) may be based on (i.e., assisted by) the use of AI / ML models.

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

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

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

[0101] Furthermore, the rApp hosted by NRT-RIC is configured to infer optimized configurations for EE / ES to the E2 node via the R1 / O1 interface of the SMO and 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 (for example, it may include R1 / O1 consumption and / or generation services).

[0102] Furthermore, the rApp hosted by NRT-RIC is configured to perform actions necessary for EE / ES optimization (i.e., consume and / or generate services via the R1 interface) (for example, it may configure services to consume and / or generate R1 / O1). For example, the rApp may check for ongoing emergency calls and warning messages via the R1 / O1 interface and perform several preparatory actions to shut down (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 to trigger HO traffic and UE migrations from one or more cells and / or carriers to other cells or carriers, notify neighboring nodes via the X2 / Xn interface, etc.

[0103] In other embodiments, one or more rApps hosted by the NRT-RIC are configured to perform actions necessary for EE / ES optimization (i.e., consume and / or generate services via the R1 interface) in preparation for switching one or more carriers and / or cells on (for example, they may be configured to consume and / or generate services via the R1 / O1 interface). 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 NRT-RIC may be configured to make the final decision about on / off switching and notify the SMO via O1 of the action taken (i.e., notify one or more E2 nodes and O-RUs of the deployment of a configuration optimized for EE / ES through the R1 / O1 interface of the SMO and the NRT-RIC framework) (for example, it may configure R1 / O1 consumption and / or generation services).

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

[0106] Furthermore, one or more E2 nodes in Figure 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 several preparatory actions to shut down (i.e., switch off) one or more carriers and / or cells, etc. In one embodiment, rApp may be configured to enable, disable, modify, etc., carrier aggregation and / or dual connectivity to trigger HO traffic and UE migrations from one or more cells and / or carriers to other cells or carriers, notify neighboring nodes via the X2 / Xn interface, etc.

[0107] Furthermore, one or more E2 nodes in Figure 1 (i.e., O-DU, O-CU, etc.) are configured to perform the necessary actions to switch one or more carriers and / or cells on in order to make the final decision about on / off switching and to notify the SMO via O1 about the actions performed (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 Figure 1 are configured to report relevant energy consumption (EC) and energy efficiency (EE) information to an E2 node (i.e., an O-DU) via the open FH M-Plane interface.

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

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

[0111] Figure 5 is a flowchart of a method for implementing optimization of carrier and / or cell on / off switching according to one embodiment.

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

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

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

[0115] In one embodiment, with respect to 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) to the O-RU, and the O-RU provides measurement data (i.e., input data) for the measurement report.

[0116] When collecting O1-related data from the O-RU, the E2 node may also send the O1-related data to the rApp via the R1 interface, 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 in the SMO framework and the NRT-RIC framework.

[0117] In step 502, SMO retrains at least one artificial intelligence / machine learning (AI / ML) model based on the collected O1-related data, and rApp deploys and activates one of the retrained AI / ML models to infer data that provides the O1 configuration necessary to perform cell and / or carrier on / off switching within O-RAN.

[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 training and inference of an AI / ML model. O1-related data that provides the O1 configuration necessary to perform cell and / or carrier on / off switching may include, in addition to other O1-related data, 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: Downlink Packet Data Convergence Protocol Service Data Unit (DL PDCP SDU) data volume per interface, per PLMN (public land mobile network), per Quality of Service (QoS) level, per slice, per F1-U interface, Xn-U interface, and per X2-U interface (data volume in DL delivered from O-CU-UP to O-DU); Uplink Packet Data Convergence Protocol Service Data Unit (UP PDCP) per interface, per PLMN (public land mobile network), per Quality of Service (QoS) level, per slice, per F1-U interface, Xn-U interface, and per X2-U interface SDU) Data volume (data volume in UL delivered from O-CU-UP to O-DU), reference signal reception quality (RSRQ) measurement results for each cell per synchronous signal block (SSB), reference signal reception power (RSRP) measurement results for each cell per SSB, signal-to-interference plus noise ratio (SINR) measurement results for each cell per SSB, energy consumption, power consumed by hardware components, and transmit power.

[0119] In one embodiment, the NRT-RIC framework may retrain at least one AI / ML model. According to this embodiment, rApp selects one AI / ML model from multiple AI / ML models and sends a start request to the NRT-RIC framework to retrain the AI / ML model. The NRT-RIC framework retrains the AI / ML model. During this process, rApp monitors the parameters of the retrained AI / ML model and decides to retrieve the retrained AI / ML model from the NRT-RIC framework based on the parameters. Based on the retrieval decision, rApp requests the retrained AI / ML model from the NRT-RIC framework. Upon receiving the request, the NRT-RIC framework sends the retrained AI / ML model to rApp.

[0120] In another embodiment, rApp hosts multiple AI / ML models and retrains one of them.

[0121] Still referring to Figure 5, in step 503, the rApp monitors O1-related data to provide the O1 configuration necessary to perform cell and / or carrier on / off switching via the R1 interface through the NRT-RIC framework and via the O1 interface through the SMO function within the SMO framework.

[0122] In one embodiment, rApp may continuously 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, rApp monitors performance and energy consumption parameters to evaluate the O1 configuration necessary to perform cell and carrier shutdowns. These performance and energy consumption parameters may include configuration, performance indicators, and measurement reports (e.g., cell load-related information, traffic information, EE / EC measurement reports, etc.).

[0124] For example, O1-related data (i.e., input data) that provides the O1 configuration necessary to perform cell and / or carrier on / off switching may include configuration, performance indicators, and at least one of the 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, 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 and the energy consumption of the O-RU and one or more performance-related KPIs of the E2 node.

[0125] In step 504, rApp evaluates O1-related data that provides the configuration necessary to perform cell and / or carrier on / off switching within O-RAN, and decides to generate O1 configuration data for preparing and performing cell and / or carrier on / off switching.

[0126] Furthermore, in step 504, when rApp generates O1 configuration data for preparing and executing the on / off switching of cells and / or carriers, it sends the O1 configuration data for preparing and executing 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 objective (e.g., an EE / ES performance objective) is not achieved based on O1-related data (i.e., input data) that provides the O1 configuration necessary to perform cell and / or carrier on / off switching, rApp decides to generate O1 configuration data (i.e., output data) for preparing and performing cell and / or carrier on / off switching. In this case, rApp generates O1 configuration data for preparing and performing cell and / or carrier on / off switching.

[0128] For example, the EE / ES performance objective may be an A1 policy in the NRT-RIC, or it may be based on 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 in the O-RAN, e.g., one or more predetermined performance objectives for EE / EC in the O-RAN).

[0129] In one embodiment, the O1 configuration data (i.e., output data) for preparing and performing the on / off switching of the generated cells and / or carriers may include, for example, NRCellCU Information Object Class IOCs, NRCellDU IOCs, GNBDUFunction IOCs, GNBCUCPFunction IOCs, GNBCUUPFunction IOCs, 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, in order to enable resource reconfiguration via the O1 interface for energy-saving cell and carrier shutdown rApps.

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

[0131] In one embodiment, the implementation of O1 configuration data for preparing and performing cell and / or carrier on / off switching may further include the O-RU notifying the E2 node of the completion of the cell and / or carrier on / off switching implementation. Upon receiving notification from the O-RU, the E2 node notifies rApp via the O1 interface through the SMO function and via the R1 interface through the NRT-RIC framework. According to the embodiment, the O-RU may also notify the E2 node via an open fronthaul 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 determine that a predetermined performance target is not being met. In this case, the NRT-RIC may initiate a fallback mechanism and / or initiate an update or retraining of the AI / ML model.

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

[0134] For this purpose, the SMO function may also be an O1 termination point for the O1 interface. Non-RT-RIC frameworks and / or rApps 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) carrier and cell on / off switching optimization configurations.

[0135] Referring to Figure 6, once the R1 interface and O1 interface connection and the open FH M-Plane interface are established between the E2 node and the O-RU, a communication path is established within the O-RAN between the rApp and the E2 node and the O-RU of at least one O-RAN.

[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 targets (i.e., predetermined performance parameters for EE / ES within the O-RAN, e.g., one or more predetermined performance targets for EE / EC within the O-RAN) for the energy saving (ES) function in the NRT-RIC.

[0138] As a result, a method for optimizing cell and / or carrier on / off switching may be initiated by the network operator enabling an optimized rApp along with an initial AI / ML model for carrier and cell on / off switching ES functionality, when the E2 node and O-RU become operational.

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

[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 O-RU (via the E2 node).

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

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

[0144] In Operation 6, the NRT-RIC collects 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, 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 one embodiment. Referring to Figure 7, in Operation 8, at least one AI / ML model from among multiple AI / ML models may be retrained on the NRT-RIC framework or rApp. In one embodiment, if the NRT-RIC framework hosts the retraining of at least one AI / ML model from among multiple AI / ML models, the rApp selects one AI / ML model from among multiple AI / ML models and starts retraining the selected AI / ML model on the Non-RT-RIC framework. In one embodiment, the retraining and selection of AI / ML models may be performed by a flexible AI / ML workflow within the SMO.

[0147] In Operation 9, when a retraining request is received from rApp, the NRT-RIC framework begins retraining 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 embodiment, 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 rApp, the NRT-RIC framework transfers the AI / ML model (i.e., the retrained AI / ML model) to rApp.

[0150] In one embodiment, 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, once the AI / ML models are retrained, at least one AI / ML model (including the retrained AI / ML model) is deployed and activated for inference (i.e., to infer data that provides the O1 configuration necessary to perform cell and / or carrier on / off switching within O-RAN).

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

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

[0154] In Operation 13 of Figure 8, when the AI / ML model is retrained, at least one AI / ML model (including the retrained AI / ML model) is deployed and activated for inference (i.e., to infer data that provides the O1 configuration necessary to perform on / off switching of cells and / or carriers in O-RAN).

[0155] Referring to Figures 7 and 8, in Operation 13, rApp continuously monitors the performance and energy consumption of the E2 node, the energy consumption of the O-RU, etc. For example, rApp monitors performance and energy consumption to evaluate the O1 configuration necessary to perform cell and carrier shutdowns.

[0156] In one embodiment, O1-related data may also be measurement input data used in training and inference of AI / ML models. In addition to other O1-related data, O1-related data may 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: Downlink Packet Data Convergence Protocol Service Data Unit (DL PDCP SDU) data volume per interface, per PLMN (public land mobile network), per Quality of Service (QoS) level, per slice, per F1-U interface, Xn-U interface, and per X2-U interface (data volume in DL delivered from O-CU-UP to O-DU); Uplink Packet Data Convergence Protocol Service Data Unit (UP PDCP) per interface, per PLMN (public land mobile network), per Quality of Service (QoS) level, per slice, per F1-U interface, Xn-U interface, and per X2-U interface SDU) Data volume (data volume in UP delivered from O-CU-UP to O-DU), reference signal reception quality (RSRQ) measurement results for each cell of the synchronous signal block (SSB), reference signal reception power (RSRP) measurement results for each cell of the SSB, signal-to-interference plus noise ratio (SINR) measurement results for each cell of the SSB, energy consumption, power consumed by hardware components, transmit power, etc.

[0157] Figure 9 illustrates the generation and implementation of an O1 configuration for preparing and performing on / off switching of cells and / or carriers according to one embodiment.

[0158] Referring to Figure 9, in Operation 14, rApp generates an O1 configuration for preparing and executing the 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 may include output data such as 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, to enable energy-saving cell and carrier shutdowns and reconfigure resources via the O1 interface.

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

[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 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, once the cell or carrier has been switched on or off, 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 switch of a cell or carrier.

[0165] In Operation 20, the NRT-RIC notifies the rApp via the R1 interface that the cell or carrier on / off switchover is complete.

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

[0167] In one embodiment, the method for optimizing the on / off switching of cells and / or carriers may terminate when the E2 node becomes inactive or when the operator disables an AI / ML model for optimization or energy saving (i.e., an AI / ML model for EE / ES).

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

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

[0170] According to one embodiment, a system is provided for implementing optimization of carrier and / or cell on / off switching by a service management and orchestration (SMO) framework in an open radio access network (O-RAN). The system includes 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, via rApp, collects O1-related data that provides the O1 configuration necessary to perform cell and / or carrier on / off switching, collected 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, and SMO retrains at least one artificial intelligence / machine learning (AI / ML) model based on the collected O1-related data, and rApp deploys and activates one of the retrained AI / ML models from the at least one retrained AI / ML to infer data that provides the O1 configuration necessary to perform cell and / or carrier on / off switching within the O-RAN, and rApp retrains at least one artificial intelligence / machine learning (AI / ML) model based on the collected O1-related data and SMO deploys and activates one of the retrained AI / ML models to infer data that provides the O1 configuration necessary to perform cell and / or carrier on / off switching within the O-RAN, and rApp retrains at least one artificial intelligence / machine learning (AI / ML) model based on the collected O1-related data and SMO deploys and activates one of the retrained AI / ML models to infer data that provides the O1 configuration necessary to perform cell and / or carrier on / off switching The rApp monitors O1-related data that provides the O1 configuration necessary to perform carrier on / off switching, evaluates the O1-related data that provides the O1 configuration necessary to perform cell and / or carrier on / off switching, decides to generate O1 configuration data for preparing and performing cell and / or carrier on / off switching, sends the O1 configuration data for preparing and performing cell and / or carrier on / off switching 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 is configured to execute instructions to implement cell and / or carrier on / off switching in the O-RAN by the E2 node and O-RU.At least one processor is further configured during implementation to translate O1 configuration data by the E2 node to prepare and perform cell and / or carrier on / off switching, and to instruct the O-RU via open FH M-Plane to perform cell and / or carrier on / off switching.

[0171] At least one processor may be further configured to, during the retraining of at least one AI / ML model, select one AI / ML model from multiple AI / ML models via rApp, send a start request to the NRT-RIC framework to retrain the AI / ML model via rApp, retrain the AI / ML model via the NRT-RIC framework, monitor the parameters of the retrained AI / ML model via rApp, decide to retrieve the retrained AI / ML model from the NRT-RIC framework based on the parameters of the retrained AI / ML model, request the retrained AI / ML model from the NRT-RIC framework via rApp, and send the retrained AI / ML model to rApp via the NRT-RIC framework.

[0172] At least one processor may be further configured to retrain one AI / ML model from multiple AI / ML models via rApp while at least one AI / ML model is being retrained.

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

[0174] At least one processor may be configured such that, during the collection of O1-related data that provides the O1 configuration necessary to perform cell and / or carrier on / off switching, the rApp sends 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 in 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 cell and / or carrier on / off switching from the O-RU via the open fronthaul management plane (FH M-Plane) interface between the E2 node and the open radio unit (O-RU); and the E2 node sends the O1-related data that provides the O1 configuration necessary to perform cell and / or carrier on / off switching, collected via the O1 interface through the SMO function in the SMO framework and the NRT-RIC framework, to the rApp via the R1 interface.

[0175] At least one processor may be further configured to instruct the O-RU via open FH M-Plane to perform cell and / or carrier on / off switching, while 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 cell and / or carrier on / off switching implementation, and 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 cell and / or carrier on / off switching implementation.

[0176] At least one processor may be further configured to monitor the performance of the retrained AI / ML model by the NRT-RIC, determine that a predetermined performance target is not being met based on the collected O1-related data, and initiate a fallback mechanism and / or update or retrain the AI / ML model.

[0177] According to one embodiment, a method is provided for implementing optimization of carrier and / or cell on / off switching by a service management and orchestration (SMO) framework in an open radio access network (O-RAN).The method involves collecting O1-related data, which provides the O1 configuration necessary to perform cell and / or carrier on / off switching, via an open fronthaul management plane (FH M-Plane) interface between the E2 node and the open radio unit (O-RU) using an rApp, via the R1 interface through the NRT-RIC framework, and via the O1 interface through the SMO function within the SMO framework; retraining at least one artificial intelligence / machine learning (AI / ML) model based on the collected O1-related data using an SMO; deploying and activating one of the retrained AI / ML models, from at least one retrained AI / ML, to infer data providing the O1 configuration necessary to perform cell and / or carrier on / off switching within the O-RAN using an rApp; and retraining at least one artificial intelligence / machine learning (AI / ML) model based on the collected O1-related data using an SMO using an rApp, and deploying and activating one of the retrained AI / ML models to infer data providing the O1 configuration necessary to perform cell and / or carrier on / off switching within the O-RAN using an rApp, and This includes monitoring O1-related data that provides the O1 configuration necessary to perform on / off switching of / or carriers, evaluating the O1-related data by rApp that provides the O1 configuration necessary to perform on / off switching of cells and / or carriers, deciding by rApp to generate O1 configuration data for preparing and performing on / off switching of cells and / or carriers, sending the O1 configuration data for preparing and performing on / off switching of cells and / or carriers to at least one E2 node by rApp 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 implementing on / off switching of cells and / or carriers in O-RAN by the E2 node and O-RU.The implementation may include the E2 node converting O1 configuration data for preparing and performing cell and / or carrier on / off switching, and the E2 node instructing the O-RU via open FH M-Plane to perform cell and / or carrier on / off switching.

[0178] Retraining at least one AI / ML model may include: selecting one AI / ML model from multiple AI / ML models using an rApp; sending a start request to the NRT-RIC framework for retraining the AI / ML model using an rApp; retraining the AI / ML model using the NRT-RIC framework; monitoring the parameters of the retrained AI / ML model using an rApp and deciding to retrieve the retrained AI / ML model from the NRT-RIC framework based on the parameters of the retrained AI / ML model using an rApp; requesting the retrained AI / ML model from the NRT-RIC framework using an rApp; and sending the retrained AI / ML model to the rApp using the NRT-RIC framework.

[0179] Retraining at least one AI / ML model may include retraining one AI / ML model from multiple AI / ML models using rApp.

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

[0181] Collecting O1-related data that provides the O1 configuration necessary to perform cell and / or carrier on / off switching 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 in the SMO framework; the E2 node receiving the O1-related data collection request from the SMO function; the E2 node collecting O1-related data from the O-RU that provides the O1 configuration necessary to perform cell and / or carrier on / off switching via the open fronthaul 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 cell and / or carrier on / off switching, collected via the O1 interface through the SMO function in the SMO framework and the NRT-RIC framework, to the rApp via the R1 interface.

[0182] Instructing the O-RU to perform cell and / or carrier on / off switching may further include the O-RU notifying the E2 node of the completion of the cell and / or carrier on / off switching implementation 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 cell and / or carrier on / off switching implementation 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 monitoring the performance of the retrained AI / ML model by NRT-RIC, determining that a predetermined performance target is not being met based on the collected O1-related data, and initiating a fallback mechanism and / or updating or retraining the AI / ML model.

[0184] According to one embodiment, a non-real-time RAN intelligent controller (NRT-RIC), an NRT-RIC framework, at least one SMO function, and a non-temporary computer-readable recording medium on which instructions executable by at least one processor configured to implement an rApp hosted by the NRT-RIC are recorded are provided for performing a method for implementing the optimization of carrier and / or cell on / off switching by a service management and orchestration (SMO) framework in an open radio access network (O-RAN).The method involves collecting O1-related data, which provides the O1 configuration necessary to perform cell and / or carrier on / off switching, via an open fronthaul management plane (FH M-Plane) interface between the E2 node and the open radio unit (O-RU) using an rApp, via the R1 interface through the NRT-RIC framework and via the O1 interface through the SMO function within the SMO framework; retraining at least one artificial intelligence / machine learning (AI / ML) model based on the collected O1-related data using an SMO; deploying and activating one of the retrained AI / ML models from at least one retrained AI / ML to infer data providing the O1 configuration necessary to perform cell and / or carrier on / off switching within the O-RAN using an rApp; and retraining at least one artificial intelligence / machine learning (AI / ML) model based on the collected O1-related data using an SMO using an rApp, via the R1 interface through the NRT-RIC framework and via the O1 interface through the SMO function within the SMO framework. This may also include monitoring O1-related data that provides the O1 configuration necessary to perform on / off switching of carriers, evaluating the O1-related data that provides the O1 configuration necessary to perform on / off switching of cells and / or carriers by rApp, deciding by rApp to generate O1 configuration data for preparing and performing on / off switching of cells and / or carriers, sending the O1 configuration data for preparing and performing on / off switching of cells and / or carriers to at least one E2 node by rApp 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 implementing on / off switching of cells and / or carriers in O-RAN by the E2 node and O-RU.The implementation may include the E2 node converting O1 configuration data for preparing and performing cell and / or carrier on / off switching, and the E2 node instructing the O-RU via open FH M-Plane to perform cell and / or carrier on / off switching.

[0185] Retraining at least one AI / ML model may include retraining one AI / ML model from multiple AI / ML models using rApp.

[0186] O1-related data that provides the O1 configuration necessary to perform cell and / or carrier on / off switching may include configuration, performance indicators, and measurement reports provided by O-RUs. Measurement reports may include cell load-related information, traffic information, and at least one energy efficiency / energy consumption (EE / EC) measurement report. Energy efficiency / energy consumption (EE / EC) measurement reports may include energy consumption of E2 nodes, energy consumption of O-RUs, and at least one of one or more performance-related KPIs (Key Performance Indicators) of E2 nodes.

[0187] Collecting O1-related data that provides the O1 configuration necessary to perform cell and / or carrier on / off switching 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 in the SMO framework; the E2 node receiving the O1-related data collection request from the SMO function; the E2 node collecting O1-related data from the O-RU that provides the O1 configuration necessary to perform cell and / or carrier on / off switching via the open fronthaul 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 cell and / or carrier on / off switching, collected via the O1 interface through the SMO function in the SMO framework and the NRT-RIC framework, to the rApp via the R1 interface.

[0188] Instructing the O-RU to perform cell and / or carrier on / off switching may further include the O-RU notifying the E2 node of the completion of the cell and / or carrier on / off switching implementation 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 cell and / or carrier on / off switching implementation 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 monitoring the performance of the retrained AI / ML model by NRT-RIC, determining that a predetermined performance target is not being met based on the collected O1-related data, and initiating a fallback mechanism and / or updating or retraining the AI / ML model.

[0190] According to the embodiment, the system and method implement carrier and / or cell on / off switching control that considers the energy efficiency of the entire network instead of local optimization in the O-RAN. For example, the functionality of the AI / ML model may include predicting future traffic, user mobility, and resource usage, and may predict network performance for expected energy efficiency improvements, resource usage, and 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 carrier and / or cell on / off switching parameters in cells or clusters of cells to optimize the energy efficiency of the entire network, instead of local optimization in O-RAN.

[0192] The foregoing disclosures are illustrative and descriptive, but are not intended to be exhaustive or to limit implementations to the exact forms disclosed. Modifications and variations are possible in light of the foregoing disclosures or may be derived from the execution of the implementations.

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

[0194] A computer-readable storage medium may be a tangible device capable of holding and storing instructions for use by an instruction execution device. A computer-readable storage medium may, but is 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: portable computer diskettes, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disk read-only memory (CD-ROM), digital multipurpose disks (DVDs), memory sticks, floppy disks, mechanically encoded devices such as punch cards or grooves on which instructions are recorded, and any suitable combination thereof. The computer-readable storage medium used herein is not to be interpreted as a transient signal itself, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmitting media (e.g., light pulses passing through fiber optic cables), or electrical signals transmitted through wires.

[0195] The computer-readable program instructions described herein may be downloaded from computer-readable storage media to each computing / processing device, or downloaded to an external computer or 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 include copper transmission cables, optical transmission fibers, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives computer-readable program instructions from the network and transfers them to storage in the computer-readable storage media within each computing / processing device.

[0196] The computer-readable program code / instructions for performing the operation may be assembler instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, configuration data for integrated circuits, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk and C++, and procedural programming languages ​​such as the C programming language, or similar programming languages. The computer-readable program instructions may be executed as a standalone software package, either entirely 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 wide area network (WAN), and the connection may be to an external computer (for example, via the Internet using an Internet Service Provider). In some embodiments, for example, an electronic circuit including a programmable logic circuit, an FPGA (field-programmable gate array), or a programmable logic array (PLA) may execute computer-readable program instructions by utilizing state information of computer-readable program instructions to personalize the electronic circuit in order to perform a side or operation.

[0197] These computer-readable program instructions may be provided to a general-purpose computer, a dedicated computer, or a processor of another programmable data processing device to generate a device such that instructions executed via the processor of a computer or other programmable data processing device generate means for implementing functions / actions described in flowcharts and / or block diagrams (one or more blocks). These computer-readable program instructions may be stored on a computer-readable storage medium on which the instructions are stored, which can be instructed to cause a computer, a programmable data processing device, and / or other device to function in a particular manner such that the storage medium containing the instructions has a workpiece containing instructions that implement aspects of functions / actions described in flowcharts and / or block diagrams (one or more blocks).

[0198] Computer-readable program instructions may be loaded onto a computer, other programmable device, or other device so that a series of operational steps are executed on the computer, other programmable device, or other device to generate a computer-implemented process in which instructions executed on the computer, other programmable device, or other device implement a function / action described in a flowchart and / or block diagram (one or more blocks).

[0199] The illustrated flowcharts and block diagrams 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 microservice, module, segment, or portion of instructions comprising one or more executable instructions to implement a particular logical function. The methods, computer systems, and computer-readable media may include additional blocks, fewer blocks, different blocks, or different arrangements of blocks than those shown in the diagrams. In some alternative implementations, the functions shown in the blocks may occur outside the order shown in the diagrams. For example, two blocks shown consecutively may actually be executed simultaneously or substantially simultaneously, depending on the functions involved, or the blocks may be executed in reverse order. Note that each block in the illustrated block diagrams and / or flowcharts, and combinations of blocks in the illustrated block diagrams and / or flowcharts, may be implemented by a system based on dedicated hardware that performs a particular function or action, or by executing a combination of dedicated hardware and computer instructions.

[0200] It is evident that the systems and / or methods described herein may be implemented in different forms of hardware, firmware, or combinations of hardware and software. The actual dedicated control hardware or software code used to implement these systems and / or methods is not limited to the implementation. Thus, the operation and behavior 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 to implement the systems and / or methods based on the descriptions herein.

Claims

1. A system for implementing optimization of carrier and / or cell on / off switching using a service management and orchestration (SMO) framework in an open radio access network (O-RAN), Memory for storing instructions, A non-real-time RAN intelligent controller (NRT-RIC), an NRT-RIC framework, at least one processor configured to implement at least one SMO function and an rApp hosted by the NRT-RIC, Equipped with, The at least one processor is O1-related data, which provides the O1 configuration necessary to perform the on / off switching of the cell and / or carrier by rApp, is collected 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, and is collected via the open fronthaul management plane (FH M-Plane) interface between the E2 node and the open radio unit (O-RU), and is collected 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 aforementioned 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 of the at least one retrained AI / ML models for inferring data that provides the O1 configuration necessary to perform the on / off switching of the cells and / or carriers within the O-RAN. The rApp monitors the O1-related data that provides the O1 configuration necessary to perform the 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 in the SMO framework. The rApp evaluates the O1-related data that provides the O1 configuration necessary to perform the on / off switching of the cell and / or carrier, The rApp determines to generate O1 configuration data for preparing and executing the on / off switching of the cell and / or carrier. The rApp sends the O1 configuration data to the at least E2 node for preparing and performing the 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 at least one SMO function in the SMO framework. The E2 node and the O-RU implement the on / off switching of the cells and / or carriers within the O-RAN. It is configured to execute the aforementioned instructions, The aforementioned at least one processor, during implementation, The E2 node converts the O1 configuration data to prepare and perform the on / off switching of the cell and / or carrier. The E2 node instructs the O-RU via the open FH M-Plane to perform the on / off switching of the cell and / or carrier. A system further configured in this way.

2. The aforementioned at least one processor, during the retraining of at least one AI / ML model, The aforementioned rApp selects one AI / ML model from multiple AI / ML models. The rApp sends a start request to the NRT-RIC framework to retrain the AI / ML model. The aforementioned NRT-RIC framework is used to retrain the AI / ML model. The rApp monitors the parameters of the retrained AI / ML model and, based on the parameters of the retrained AI / ML model, decides whether to retrieve the retrained AI / ML model from the NRT-RIC framework. 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 follows.

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

4. The O1-related data, which provides the O1 configuration necessary to perform the on / off switching of the cell and / or carrier, comprises at least one of the configuration, performance indicators, and measurement reports provided from the O-RU. The measurement report comprises at least one of the following: cell load-related information, traffic information, and energy efficiency / energy consumption (EE / EC) measurement report. The aforementioned energy efficiency / energy consumption (EE / EC) measurement report includes the energy consumption of the E2 node, the energy consumption of the O-RU, and at least one of the performance-related KPIs (Key Performance Indicators) of the E2 node. The system according to claim 1.

5. The at least one processor, during the collection of the O1-related data that provides the O1 configuration necessary to perform the on / off switching of the cell and / or carrier, The rApp sends O1-related data collection requests 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 receives the O1-related data collection request from the SMO function, The E2 node collects the O1-related data from the O-RU via an open fronthaul management plane (FH M-Plane) interface between the E2 node and the open radio unit (O-RU) to provide the O1 configuration necessary to perform the on / off switching of the cell and / or carrier. The E2 node sends the O1-related data, which 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 in the SMO framework and the NRT-RIC framework, to the rApp via the R1 interface. The system according to claim 1, configured as follows.

6. The at least one processor instructs the O-RU via the open FH M-Plane to perform the on / off switching of the cell and / or carrier, The O-RU notifies the E2 node of the completion of the implementation of switching the cell and / or carrier on and off 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 switching the cell and / or carrier on and off 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 follows.

7. The at least one processor is The NRT-RIC monitors the performance of the retrained AI / ML model, Based on the O1-related data collected, it is determined that the predetermined performance targets will not be achieved. Initiate the fallback mechanism and / or update or retrain the AI / ML model. The system according to claim 1, further configured as follows.

8. A method for implementing optimization of carrier and / or cell on / off switching by a service management and orchestration (SMO) framework in an open radio access network (O-RAN), The rApp provides O1-related data that provides the O1 configuration necessary to perform the on / off switching of the cell and / or carrier, 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 via the R1 interface through the NRT-RIC framework and via the O1 interface through the SMO function in the SMO framework. The aforementioned SMO retrains at least one artificial intelligence / machine learning (AI / ML) model based on the O1-related data collected, The rApp deploys and activates one of the at least one retrained AI / ML models for inferring data that provides the O1 configuration necessary to perform the on / off switching of the cells and / or carriers within the O-RAN, The rApp monitors the O1-related data that provides the O1 configuration necessary to perform the 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 in the SMO framework, The rApp evaluates the O1-related data that provides the O1 configuration necessary to perform the on / off switching of the cell and / or carrier, The rApp determines to generate O1 configuration data for preparing and executing the 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 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 in the SMO framework, The E2 node and the O-RU implement the on / off switching of the cells and / or carriers within the O-RAN, Equipped with, The aforementioned implementation means The E2 node converts the O1 configuration data for preparing and performing the on / off switching of the cell and / or carrier, The E2 node instructs the O-RU via the open FH M-Plane to perform the on / off switching of the cell and / or carrier, A method for providing this.

9. The retraining of at least one AI / ML model is performed as follows: The aforementioned rApp allows for the selection of one AI / ML model from multiple AI / ML models, The rApp sends a request to the NRT-RIC framework to start retraining the AI / ML model. The aforementioned NRT-RIC framework is used to retrain the AI / ML model, The rApp monitors the parameters of the retrained AI / ML model and, based on the parameters of the retrained AI / ML model, determines the retrieval of the retrained AI / ML model from the NRT-RIC framework. 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 method according to claim 8, comprising:

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

11. The O1-related data, which provides the O1 configuration necessary to perform the on / off switching of the cell and / or carrier, comprises at least one of the configuration, performance indicators, and measurement reports provided from the O-RU. The measurement report comprises at least one of the following: cell load-related information, traffic information, and energy efficiency / energy consumption (EE / EC) measurement report. The aforementioned energy efficiency / energy consumption (EE / EC) measurement report includes the energy consumption of the E2 node, the energy consumption of the O-RU, and at least one of the performance-related KPIs (Key Performance Indicators) of the E2 node. The method according to claim 8.

12. Collecting the O1-related data that provides the O1 configuration necessary to perform the on / off switching of the cell and / or carrier is, The rApp sends O1-related data collection requests 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 receives the O1-related data collection request from the SMO function, The E2 node collects the O1-related data 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) to provide the O1 configuration necessary to perform the on / off switching of the cell and / or carrier, The E2 node sends the O1-related data, which 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 in the SMO framework and the NRT-RIC framework, to the rApp via the R1 interface, The method according to claim 8, comprising:

13. Instructing the O-RU to perform the on / off switching of the cell and / or carrier is: The O-RU notifies the E2 node of the completion of the implementation of switching the cell and / or carrier on and off 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 switching the cell and / or carrier on and off via the O1 interface through the SMO function and via the R1 interface through the NRT-RIC framework within the SMO framework. The method according to claim 8, further comprising:

14. The NRT-RIC is used to monitor the performance of the retrained AI / ML model, Based on the O1-related data collected, it is determined that the predetermined performance targets will not be achieved. This involves initiating updates or retraining of the fallback mechanism and / or AI / ML model, The method according to claim 8, further comprising:

15. A non-temporary computer-readable recording medium on which 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 are recorded, for the purpose of implementing a method for optimizing carrier and / or cell on / off switching by a service management and orchestration (SMO) framework in an open radio access network (O-RAN), The aforementioned method, The rApp provides O1-related data that provides the O1 configuration necessary to perform the on / off switching of the cell and / or carrier, 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 via the R1 interface through the NRT-RIC framework and via the O1 interface through the SMO function in the SMO framework. The aforementioned SMO retrains at least one artificial intelligence / machine learning (AI / ML) model based on the O1-related data collected, The rApp deploys and activates one of the at least one retrained AI / ML models for inferring data that provides the O1 configuration necessary to perform the on / off switching of the cells and / or carriers within the O-RAN, The rApp monitors the O1-related data that provides the O1 configuration necessary to perform the 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 in the SMO framework, The rApp evaluates the O1-related data that provides the O1 configuration necessary to perform the on / off switching of the cell and / or carrier, The rApp determines to generate O1 configuration data for preparing and executing the 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 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 in the SMO framework, The E2 node and the O-RU implement the on / off switching of the cells and / or carriers within the O-RAN, Equipped with, The aforementioned implementation means The E2 node converts the O1 configuration data for preparing and performing the on / off switching of the cell and / or carrier, The E2 node instructs the O-RU via the open FH M-Plane to perform the on / off switching of the cell and / or carrier, A non-temporary computer-readable recording medium equipped with [a specific feature].

16. The retraining of at least one AI / ML model is performed as follows: The aforementioned rApp allows for the selection of one AI / ML model from multiple AI / ML models, The rApp sends a request to the NRT-RIC framework to start retraining the AI / ML model. The aforementioned NRT-RIC framework is used to retrain the AI / ML model, The rApp monitors the parameters of the retrained AI / ML model and, based on the parameters of the retrained AI / ML model, determines the retrieval of the retrained AI / ML model from the NRT-RIC framework. 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, A non-temporary computer-readable recording medium according to claim 15, comprising:

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

18. The O1-related data, which provides the O1 configuration necessary to perform the on / off switching of the cell and / or carrier, comprises at least one of the configuration, performance indicators, and measurement reports provided from the O-RU. The measurement report comprises at least one of the following: cell load-related information, traffic information, and energy efficiency / energy consumption (EE / EC) measurement report. The aforementioned energy efficiency / energy consumption (EE / EC) measurement report includes the energy consumption of the E2 node, the energy consumption of the O-RU, and at least one of the performance-related KPIs (Key Performance Indicators) of the E2 node. The non-temporary computer-readable recording medium according to claim 15.

19. Collecting the O1-related data that provides the O1 configuration necessary to perform the on / off switching of the cell and / or carrier is, The rApp sends O1-related data collection requests 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 receives the O1-related data collection request from the SMO function, The E2 node collects the O1-related data 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) to provide the O1 configuration necessary to perform the on / off switching of the cell and / or carrier, The E2 node sends the O1-related data, which 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 in the SMO framework and the NRT-RIC framework, to the rApp via the R1 interface, A non-temporary computer-readable recording medium according to claim 15, comprising:

20. Instructing the O-RU to perform the on / off switching of the cell and / or carrier is: The O-RU notifies the E2 node of the completion of the implementation of switching the cell and / or carrier on and off 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 switching the cell and / or carrier on and off via the O1 interface through the SMO function and via the R1 interface through the NRT-RIC framework within the SMO framework. A non-temporary computer-readable recording medium according to claim 15, further comprising the above.

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