SYSTEM AND METHOD FOR OPTIMIZING CARRIER AND / OR CELL ON / OFF SWITCHING IN A COMMUNICATION NETWORK - Patent application

Abstract

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

Description

[Technical Field]

[0001] Systems and methods consistent with embodiments of the present disclosure relate to generating and deploying optimizations for shutting down and starting up carriers and / or cells to conserve energy in communication networks. [Background technology]

[0002] The Radio Access Network (RAN) is a critical component in a communication system that connects end-user devices (or user equipment) to the rest of the network. The RAN includes a combination of various network elements (NEs) that connect end-user devices to the core network. Traditionally, the hardware and / or software of a particular RAN has been vendor-specific.

[0003] The emergence of Open RAN (O-RAN) technology allows multiple vendors to provide hardware and / or software for communication systems. To this end, O-RAN decomposes RAN functions into a centralized unit (CU), a distributed unit (DU), and a radio unit (RU). The CU is a logical node for hosting the 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 the RAN sublayers of Radio Link Control (RLC), Medium Access Control (MAC), and Physical (PHY). The RU is a physical node that converts radio signals from the antenna into digital signals that can be transmitted over the fronthaul to the DU. These entities can be developed by different vendors because they have open protocols and interfaces between them.

[0004] Figure 1 illustrates an O-RAN architecture in the related art. Referring to Figure 1, RAN functions in the O-RAN architecture are controlled and optimized by RICs. RICs are software-defined components that implement modular applications to realize the multi-vendor operability required in O-RAN systems and 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 for non-real-time control loops and operates on sub-second timescales within a 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 over the A1 interface, which is the 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 over the O1 interface, which is the 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 a time scale between 10 milliseconds and 1 second and connects to the O-DU, O-CU (decomposed into the O-CU control plane (O-CU-CP) and the 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 nodes / network functions (NFs)) in 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) through policies. For example, the nRT-RIC sets policy parameters on the activated functions of the E2 nodes. Furthermore, the nRT-RIC hosts xApps for implementing functions such as quality of service (QoS) optimization, mobility optimization, slicing optimization, interference mitigation, load balancing, and security. The two types of RICs work together to optimize the O-RAN. For example, the NRT-RIC provides policies, data, and artificial intelligence / machine learning (AI / ML) models over the A1 interface that are enabled and used by the nRT-RIC for RAN optimization, and the nRT-RIC returns policy feedback (i.e., how the policies set by the NRT-RIC are working).

[0007] The SMO framework in which the 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 physical RAN nodes that host the RIC, O-CU, and O-DU, supporting software components (e.g., operating systems and runtime environments), and the SMO itself. In other words, the SMO manages the O-Cloud from within. The O2 interface is the interface between the SMO and the O-Cloud in which it resides. The SMO provides Infrastructure Management Services (IMS) and Deployment Management Services (DMS) through the O2 interface.

[0008] On the other hand, the O-Cloud is a cloud computing platform that comprises a collection of physical infrastructure nodes that meet the O-RAN specifications for hosting the 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, where the NRT-RIC is located, manages and orchestrates 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 hybrid model; and the O2 interface for platform resource and workload management between the SMO and O-Cloud.

[0010] In related technology, O-RAN utilizes multiple frequency layers (carriers) to cover a service area. When network traffic load is low (e.g., when the expected traffic volume is lower than a fixed threshold), energy savings (i.e., high energy efficiency and / or low energy consumption) can be achieved by shutting down (i.e., switching off) one or more carriers or entire cells without impairing 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 (UE) in the O-RAN) 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 shutting down (i.e., switching off). Summary of the Invention [Problem to be solved by the invention]

[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, where network traffic varies 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 on / off carriers and / or cells may lead 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 embodiments, a system and method are provided for implementing a service management and orchestration (SMO) framework. The SMO framework includes a non-real-time RAN intelligent controller (NRT-RIC), an NRT-RIC framework, at least one SMO function, and an rApp hosted by the NRT-RIC. The rApp generates and implements O1 configuration data for preparing and executing 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 to retrain, deploy, and activate AI / ML models for inferring data that provides the O1 configuration necessary to execute 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 providing 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 at least one SMO function in the SMO framework) considers network-wide energy efficiency instead of local optimization in the O-RAN.

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

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

[0016] According to an embodiment, a system for implementing carrier and / or cell on / off switching optimization via a Service Management and Orchestration (SMO) framework in an Open Radio Access Network (O-RAN) is provided, the system including: a memory storing instructions; and at least one processor configured to implement a Non-Real-Time RAN Intelligent Controller (NRT-RIC), the NRT-RIC framework, at least one SMO function, and an rApp hosted by the NRT-RIC.The at least one processor, via an rApp, collects O1-related data from the E2 node via an R1 interface through the NRT-RIC framework and via an O1 interface through an SMO function in the SMO framework, the O1-related data providing O1 configurations necessary to perform cell and / or carrier on / off switching, the O1-related data being collected via an open fronthaul management plane (FH M-Plane) interface between the E2 node and the open radio unit (O-RU); retrains, via the SMO, at least one artificial intelligence / machine learning (AI / ML) model based on the collected O1-related data; deploys and activates, via the rApp, one retrained AI / ML model from the at least one retrained AI / ML for inferring data providing O1 configurations necessary to perform cell and / or carrier on / off switching within the O-RAN; and deploys, via the rApp, one retrained AI / ML model for inferring data providing O1 configurations necessary to perform cell and / or carrier on / off switching within the O-RAN. and executing instructions to: monitor O1-related data providing the O1 configuration required to perform carrier on / off switching; evaluate, by an rApp, the O1-related data providing the O1 configuration required to perform cell and / or carrier on / off switching; determine, by the rApp, to generate O1 configuration data for preparing and performing cell and / or carrier on / off switching; send, by the rApp, the O1 configuration data for preparing and performing cell and / or carrier on / off switching to at least one E2 node via an R1 interface through the NRT-RIC framework and via an O1 interface through at least one SMO function in the SMO framework; and implement, by the E2 node and the O-RU, the cell and / or carrier on / off switching in the O-RAN.The at least one processor is further configured, during implementation, to convert, by the E2 node, O1 configuration data for preparing and performing on / off switching of cells and / or carriers, and to instruct, by the E2 node, via the open FH M-Plane, the O-RU to perform on / off switching of cells and / or carriers.

[0017] The at least one processor may be further configured to, during retraining of the at least one AI / ML model, select, by the rApp, an AI / ML model from the plurality of AI / ML models; send, by the rApp, an initiation request to retrain the AI / ML model to the NRT-RIC framework; retrain, by the NRT-RIC framework, the AI / ML model; monitor, by the rApp, the retrained AI / ML model parameters; determine, based on the retrained AI / ML model parameters, to retrieve the retrained AI / ML model from the NRT-RIC framework; request, by the rApp, the retrained AI / ML model from the NRT-RIC framework; and send, by the NRT-RIC framework, the retrained AI / ML model to the rApp.

[0018] The at least one processor may be further configured to, during retraining of the at least one AI / ML model, retrain one AI / ML model from the plurality of AI / ML models by the rApp.

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

[0020] The at least one processor may be configured to: during collection of O1-related data providing O1 configurations necessary to perform cell and / or carrier on / off switching, send, by the rApp, an O1-related data collection request to the E2 node via the R1 interface through the NRT-RIC framework and via the O1 interface through an SMO function in the SMO framework; receive, by the E2 node, the O1-related data collection request from the SMO function; collect, by the E2 node, O1-related data providing O1 configurations necessary to perform cell and / or carrier on / off switching from the O-RU via an open fronthaul management plane (FH M-Plane) interface between the E2 node and the O-RU; and send, by the E2 node, the O1-related data providing O1 configurations 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] The at least one processor may be further configured to: notify, by the O-RU, the E2 node via an FH M-Plane interface between the E2 node and the O-RU of completion of implementation of the cell and / or carrier on / off switching, while instructing the O-RU via the open FH M-Plane to perform cell and / or carrier on / off switching; and notify, by the E2 node, the rApp via an O1 interface through an SMO function and via an R1 interface through an NRT-RIC framework within the SMO framework of completion of implementation of the cell and / or carrier on / off switching.

[0022] The at least one processor may be further configured to monitor, by the NRT-RIC, the performance of the retrained AI / ML model, determine that predetermined performance goals are not achieved based on the collected O1-related data, and initiate a fallback mechanism and / or an update or retraining of the AI / ML model.

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

[0024] Retraining at least one AI / ML model may include selecting, by the rApp, an AI / ML model from the plurality of AI / ML models; sending, by the rApp, an initiation request to the NRT-RIC framework to retrain the AI / ML model; retraining, by the NRT-RIC framework, the AI / ML model; monitoring, by the rApp, retrained AI / ML model parameters and determining, based on the retrained AI / ML model parameters, to retrieve the retrained AI / ML model from the NRT-RIC framework; requesting, by the rApp, the retrained AI / ML model from the NRT-RIC framework; and sending, by the NRT-RIC framework, the retrained AI / ML model to the rApp.

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

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

[0027] Collecting O1-related data that provides the O1 configuration required to perform cell and / or carrier on / off switching may include sending, by the rApp, an O1-related data collection request to the E2 node via the R1 interface through the NRT-RIC framework and via the O1 interface through an SMO function in the SMO framework; receiving, by the E2 node, the O1-related data collection request from the SMO function; collecting, by the E2 node, O1-related data from an open radio unit (O-RU) via an open fronthaul management plane (FH M-Plane) interface between the E2 node and the O-RU that provides the O1 configuration required to perform cell and / or carrier on / off switching; and sending, by the E2 node, the O1-related data that provides the O1 configuration required to perform cell and / or carrier on / off switching that has been 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.

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

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

[0030] According to an embodiment, there is provided a non-transitory computer-readable storage medium having stored thereon instructions executable by at least one processor configured to implement a Non-Real-Time RAN Intelligent Controller (NRT-RIC), an NRT-RIC framework, at least one SMO function, and an rApp hosted by the NRT-RIC to perform a method for implementing carrier and / or cell on / off switching optimization via a Service Management and Orchestration (SMO) framework in an Open Radio Access Network (O-RAN).The method includes collecting, by an rApp, O1-related data from an E2 node via an R1 interface through an NRT-RIC framework and via an O1 interface through an SMO function in an SMO framework, the O1-related data providing O1 configurations necessary to perform cell and / or carrier on / off switching, the O1-related data being collected via an open fronthaul management plane (FH M-Plane) interface between an E2 node and an open radio unit (O-RU); retraining, by the SMO, at least one artificial intelligence / machine learning (AI / ML) model based on the collected O1-related data; deploying and activating, by the rApp, one of the at least one retrained AI / ML model for inferring data providing O1 configurations necessary to perform cell and / or carrier on / off switching in an O-RAN; and transmitting, by the rApp, O1-related data through the NRT-RIC framework via the R1 interface and via the O1 interface through an SMO function in the SMO framework. and monitoring O1-related data providing the O1 configuration required to perform cell and / or carrier on / off switching; evaluating, by the rApp, the O1-related data providing the O1 configuration required to perform cell and / or carrier on / off switching; determining, by the rApp, to generate O1 configuration data for preparing and performing cell and / or carrier on / off switching; sending, by the rApp, the O1 configuration data for preparing and performing cell and / or carrier on / off switching to at least one E2 node via an R1 interface through the NRT-RIC framework and via an O1 interface through at least one SMO function in the SMO framework; and implementing, by the E2 node and the O-RU, the cell and / or carrier on / off switching in the O-RAN.The implementing may include: converting, by the E2 node, O1 configuration data for preparing and performing on / off switching of the cell and / or carrier; and instructing, by the E2 node, via an open FH M-Plane, the O-RU to perform on / off switching of the cell and / or carrier.

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

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

[0033] Collecting O1-related data that provides the O1 configuration required to perform cell and / or carrier on / off switching may include sending, by the rApp, an O1-related data collection request to the E2 node via the R1 interface through the NRT-RIC framework and via the O1 interface through an SMO function in the SMO framework; receiving, by the E2 node, the O1-related data collection request from the SMO function; collecting, by the E2 node, O1-related data from an open radio unit (O-RU) via an open fronthaul management plane (FH M-Plane) interface between the E2 node and the O-RU that provides the O1 configuration required to perform cell and / or carrier on / off switching; and sending, by the E2 node, the O1-related data that provides the O1 configuration required to perform cell and / or carrier on / off switching that has been 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 completion of implementation of the cell and / or carrier on / off switching via an FH M-Plane interface between the E2 node and the O-RU; and the E2 node notifying the rApp of completion of implementation of the cell and / or carrier on / off switching via an O1 interface through an SMO function and via an R1 interface through an NRT-RIC framework within the SMO framework.

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

[0036] Additional aspects will be set forth in part in the description that follows, and in part will be obvious from the description, or may be realized by practice of presented embodiments of the disclosure. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

[0047] The following detailed description of the embodiments refers to the accompanying drawings. The foregoing disclosure provides illustration and description, but is not intended to be exhaustive or to limit implementations to the precise form disclosed. Modifications and variations are possible in light of the foregoing disclosure or may be acquired from practice of the implementations. Furthermore, one or more features or components of one embodiment may be combined or combined with other embodiments (or one or more features of other embodiments). Additionally, in the flowcharts and operational descriptions 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 concurrently (at least in part), and the order of one or more operations may be rearranged.

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

[0049] Although particular feature combinations are recited in the claims and / or disclosed in the specification, these combinations are not intended to limit the disclosure of possible implementations. Indeed, many of these features may be combined in ways other than those specifically recited in the claims and / or specifically disclosed in the specification. Although each dependent claim listed below may depend directly on only one claim, the disclosure of possible implementations includes each dependent claim in combination with all other claims in the claim group.

[0050] No element, act, or instruction used herein should be construed as critical or required 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." Where only one item is intended, the term "one" or similar words are 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" is intended to mean "based, at least in part, on," unless expressly stated otherwise. Furthermore, phrases such as "at least one of A and B" or "at least one of A or B" are understood to include A only, B only, or both A and B.

[0051] Embodiments of the present disclosure provide systems and methods in which an NRT-RIC framework and / or rApp configures carrier and / or cell on / off switching parameters (i.e., carrier and / or cell on / off switching parameters) (i.e., an NRT-RIC and at least one rApp hosted by the NRT-RIC framework consumes and / or produces O1-related services that configure O1-related data that provides the O1 configuration necessary to perform cell and / or carrier on / off switching). For example, via an A1 policy or optimization trigger on the O1 interface to the nRT-RIC defined by the NRT-RIC (i.e., by at least one rApp hosted by the NRT-RIC and / or NRT-RIC framework assisted by machine learning (ML) techniques), the NRT-RIC framework and / or rApp (e.g., at least one rApp hosted by the NRT-RIC and / or NRT-RIC framework) enables 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 deployment of O1 configuration data to prepare and perform cell and / or carrier on / off switching for one or more E2 nodes. Implementation based on said O1 configuration data to prepare and perform 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 stopping (i.e., switching off) one or more carriers and / or cells, the E2 node needs to perform preparatory actions for stopping (i.e., switching off) one or more carriers and / or cells (e.g., the E2 node may check for ongoing emergency calls and / or warning messages that should be addressed (e.g., enabled, disabled, modified, etc.) in carrier aggregation and / or dual connectivity, for example, to trigger high occupancy (HO) data traffic and UE transition from one or more cells and / or carriers to other cells or carriers, to inform neighboring nodes via the X2 / Xn interface, etc.).

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

[0054] 2 is a diagram of an example environment 200 in which the systems and / or methods described herein may be implemented. As shown in FIG. 2, environment 200 may include a user device 210, a platform 220, and a network 230. The devices of environment 200 may be interconnected via wired connections, wireless connections, or a combination of wired and wireless connections. In an embodiment, any of the functions and operations described above with reference to FIG. 1 may be performed by any combination of elements illustrated in FIG. 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 a computing device (e.g., a desktop computer, a laptop computer, a tablet computer, a handheld computer, a smart speaker, a server, etc.), a mobile phone (e.g., a smartphone, a wireless phone, etc.), a wearable device (e.g., smart glasses or a smart watch), or similar device. In some implementations, user device 210 may receive information from and / or send 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, such that particular software components may be swapped in or out depending on particular needs. In this manner, platform 220 may be easily and / or quickly reconfigured for different uses.

[0057] In some implementations, as shown, platform 220 may be hosted in a cloud computing environment 222. Note that 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., may be implemented outside of a cloud computing environment) or may be partially cloud-based.

[0058] Cloud computing environment 222 includes an environment that hosts platform 220. Cloud computing environment 222 may provide services such as computation, software, data access, storage, etc., without requiring end-user (e.g., user device 210) knowledge of the physical location and configuration of the systems and / or devices that host platform 220. As shown, cloud computing environment 222 may include a group of computing resources 224 (collectively referred to as “computing resources 224” and individually referred to as “computing resource 224”).

[0059] Computing resources 224 include one or more personal computers, clusters of computing devices, workstation computers, server devices, or other types of computation and / or communication devices. In some implementations, computing resources 224 may host platform 220. Cloud resources may include compute instances executing on computing resources 224, storage devices provided on computing resources 224, data transfer devices provided by computing resources 224, etc. In some implementations, computing resources 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 FIG. 2, computing resources 224 include a group of cloud resources such as one or more applications (“APP”) 224-1, one or more virtual machines (“VM”) 224-2, virtualized storage (“VS”) 224-3, 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 user device 210. Application 224-1 may obviate the need to install and run a software application on user device 210. For example, application 224-1 may include software associated with platform 220 and / or any other software that may be provided via cloud computing environment 222. In some implementations, one application 224-1 may send or receive information to or from one or more other applications 224-1 via virtual machine 224-2.

[0062] Virtual machine 224-2 includes a software implementation of a device (e.g., a computer) that executes programs like a physical device. Virtual machine 224-2 may be a system virtual machine or a process virtual machine, depending on the use by virtual machine 224-2 and the degree of correspondence with any real-world device. 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 execute a single program or support a single process. In some implementations, virtual machine 224-2 may execute on behalf of a user (e.g., user device 210) and manage the infrastructure of cloud computing environment 222, such as data management, synchronization, or long-term data transfer.

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

[0064] Hypervisor 224-4 may provide hardware virtualization technology that allows multiple operating systems (e.g., "guest operating systems") to run simultaneously on a host computer, such as computing resource 224. 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 different operating systems may share virtualized hardware resources.

[0065] Network 230 may include one or more wired and / or wireless networks. For example, network 230 may include a cellular network (e.g., a fifth-generation (5G) network, a long-term evolution (LTE) network, a third-generation (3G) network, a code division multiple access (CDMA) network, etc.), a public land mobile network (PLMN), a local area network (LAN), a wide area network (WAN), a metropolitan area network (MAN), a telephone network (e.g., a public switched telephone network (PSTN), a private network, an ad hoc network, an intranet, the Internet, an optical fiber-based network, etc.), and / or a combination 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, fewer, different, or differently arranged devices and / or networks 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. Additionally 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] 3 is a diagram of example components of a device 300. The device 300 may correspond to the user device 210 and / or the platform 220. As shown in FIG. 3, the device 300 may include a bus 310, a processor 320, a memory 330, a storage component 340, an input component 350, an output component 360, and a communication interface 370.

[0068] The bus 310 includes components that enable communication between the components of the device 300. The processor 320 may be implemented in hardware, firmware, or a combination of hardware and software. The processor 320 may be a central processing unit (CPU), a graphics processing unit (GPU), an accelerated processing unit (APU), a microprocessor, a microcontroller, a digital signal processor (DSP), a field-programmable gate array (FPGA), an application-specific integrated circuit (ASIC), or other types of processing components. In some implementations, the processor 320 includes one or more processors that are programmable to perform functions. The 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 the processor 320.

[0069] Storage component 340 stores information and / or software related to the operation and use of device 300. For example, storage component 340 may include a hard disk (e.g., a 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 type of non-transitory computer-readable medium, along with a corresponding drive. Input component 350 includes components that enable device 300 to receive information, such as via user input (e.g., a touchscreen display, a keyboard, a keypad, a mouse, buttons, switches, and / or a microphone). Additionally or alternatively, input component 350 may include sensors for measuring information (e.g., a global positioning system (GPS) component, an accelerometer, a gyroscope, and / or an actuator). 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] Communications interface 370 includes transceiver-like components (e.g., a transceiver and / or a separate receiver and transmitter) that allow device 300 to communicate with other devices, such as via a wired connection, a wireless connection, or a combination of wired and wireless connections. Communications interface 370 allows device 300 to receive information from and / or provide information to other devices. For example, communications interface 370 may include an Ethernet interface, an optical interface, a coaxial interface, an infrared interface, a radio frequency (RF) interface, a universal serial bus (USB) interface, a Wi-Fi interface, a cellular network interface, etc.

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

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

[0073] Additionally or alternatively, hardwired circuitry may be used in place of or in combination with software instructions to perform one or more processes described herein. Thus, implementations described herein are not limited to any specific combination of hardware circuitry and software. The number and arrangement of components shown in FIG. 3 are provided as an example. In practice, device 300 may include additional, fewer, different, or differently arranged components than those shown in FIG. 3. Additionally or alternatively, a set of components of device 300 (e.g., one or more components) may perform one or more functions described as being performed by other sets of components of device 300.

[0074] In embodiments, any of the operations or processes of Figures 4, 5, 6, 7, and 8 may be implemented by or using any of the elements illustrated in Figures 1, 2, and 3. It is understood that other embodiments are not so limited and may be implemented in a variety of different architectures (e.g., bare metal architectures, any cloud-based architectures or deployment architectures such as Kubernetes, Docker, OpenStack, etc.).

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

[0076] 4, the NRT-RIC represents a subset of the functionality of the SMO framework. The NRT-RIC has access to other SMO framework functions and can affect (i.e., control and / or perform) what is done 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 by the O1 interface for operations and management between management entities (Network Management System (NMS) / Element Management System (EMS) / Management and Orchestration of Network Functions Virtualization (MANO)) and O-RAN management elements.

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

[0079] The NRT-RIC includes an NRT-RIC framework. The NRT-RIC framework includes, among other functions, an R1 service exposure function that handles the R1 services provided in accordance with an 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 may include a collection of services including, but not limited to, service registration and discovery services, authentication and authorization services, AI / ML workflow services, and A1, O1 and O2-interface related services.

[0081] An NRT-RIC application (rApp) is an application that utilizes the functionality available in 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, radio resource management, data analytics, etc., and information enrichment. Generally, an rApp represents an application designed to consume and / or produce R1 services.

[0082] To this end, the NRT-RIC framework produces and / or consumes an R1 service according to an embodiment via an R1 interface, which terminates at an R1 termination in the NRT-RIC framework, which connects to the NRT-RIC framework and rApps via the R1 interface and enables the NRT-RIC framework and rApps to exchange messages / data (i.e., requests and responses comprising a data model) to access the R1 service via the R1 interface.

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

[0084] Furthermore, the NRT-RIC framework includes A1-related functions, such as supporting A1 logical termination, A1 policy coordination and catalog, and A1-EI coordination and catalog.

[0085] Data management and exposure services within the NRT-RIC framework deliver data generated or collected by data generators to data consumers according to their needs (e.g., Function Management (FM) / Consumption Management (CM) / Production Management (PM) data to rApps or CM changes via the O1 interface from rApps to O-RAN).

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

[0087] Within the NRT-RIC framework, AI / ML workflow services provide access to AI / ML workflows. For example, the AI / ML workflow services may assist in training models, monitoring AI / ML models deployed in the NRT-RIC, etc.

[0088] Additionally, the NRT-RIC framework provides A2 related functions that support, for example, A2 logical termination, A2 policy coordination and catalogs.

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

[0090] The R1 interface is independent of the specific implementation of the NRT-RIC framework of the SMO and NRT-RIC. The R1 interface is defined in an extensible manner that allows new services and data types to be added without having to change protocols or procedures.

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

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

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

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

[0095] 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 the E2 node and O-RU (via the E2 node forwarded by the SMO) for decision-making purposes (e.g., the SMO and NRT-RIC framework may configure R1 / O1 consuming and / or generating services). Decision-making may be based, for example, on using training and inference of AI / ML models supporting such energy efficiency (EE) and / or energy consumption (EC) functions.

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

[0097] Furthermore, the SMO and NRT-RIC framework may be configured to forward collected data to one or more rApps hosted by the NRT-RIC, signal (i.e., send) updated configurations for energy efficiency (EE) / energy savings (ES) optimization to the E2 node (O-CU) via the R1 / O1 interface, analyze 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 enable one or more E2 nodes and O-RUs to make EE / ES optimization decisions (i.e., if one or more carriers and / or cells are recommended to be switched on / off) (e.g., the SMO and NRT-RIC framework may configure R1 / O1 consuming and / or generating services). The EE / ES optimization decisions (i.e., recommendations on which 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 hosted by the NRT-RIC and / or the NRT-RIC framework) may be configured to trigger training and / or retraining of the EE / ES AI / ML model.

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

[0100] Referring to FIG. 4, one or more rApps hosted by the NRT-RIC are configured to collect (e.g., may comprise R1 / O1 consumption and / or production services) necessary configurations, performance indicators, and measurement reports (i.e., EE / EC measurement reports) etc. from E2 nodes and O-RUs (transferred by SMOs) for the purpose of training and execution of associated AI / ML models (e.g., EE / ES AI / ML models).

[0101] Furthermore, the rApp hosted by the NRT-RIC is configured to infer an optimized configuration for EE / ES to the E2 node through the R1 / O1 interface of the SMO and NRT-RIC framework (e.g., may comprise R1 / O1 consumption and / or production services) 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.

[0102] Additionally, an rApp hosted by the NRT-RIC may be configured (e.g., configure R1 / O1 consuming and / or producing services) to perform actions necessary for EE / ES optimization (i.e., consume and / or produce services via the R1 interface). For example, the rApp may check for ongoing emergency calls and alert messages via the R1 / O1 interface, and perform some preparatory actions to stop (i.e., switch off) one or more carriers and / or cells. In one embodiment, the rApp may be configured to enable, disable, modify, etc. carrier aggregation and / or dual connectivity for HO traffic and trigger UE transitions from one or more cells and / or carriers to other cells or carriers, inform neighboring nodes via the X2 / Xn interface, etc.

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

[0104] Additionally, the rApp hosted by the NRT-RIC may be configured to make the final decision about on / off switching and notify the SMO via O1 about the action taken (i.e., notify one or more E2 nodes and O-RUs of the deployment of an optimized configuration for EE / ES through the R1 / O1 interface of the SMO and NRT-RIC framework) (e.g., configure R1 / O1 consuming and / or producing services).

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

[0106] 1 may be configured to perform actions necessary for EE / ES optimization. These actions necessary for EE / ES optimization may include checking for ongoing emergency calls and alert messages, performing some preparatory actions to stop (i.e., switch off) one or more carriers and / or cells, etc. In one embodiment, an rApp may be configured to enable, disable, modify, etc. carrier aggregation and / or dual connectivity for HO traffic and trigger UE transitions from one or more cells and / or carriers to other cells or carriers, inform neighboring nodes via the X2 / Xn interface, etc.

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

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

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

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

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

[0112] 5, a method for optimizing carrier and / or cell on / off switching is implemented by a Service Management and Orchestration (SMO) framework comprising a Non-Real-Time RAN Intelligent Controller (NRT-RIC), an NRT-RIC framework, at least one SMO function (e.g., O1 termination), and an rApp hosted by the NRT-RIC. The SMO framework may act as an intermediary between the rApp and the E2 node and the O-RU (via the E2 node) within the O-RAN.

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

[0115] In one embodiment, for collecting O1-related data that provides the O1 configuration necessary to perform cell and / or carrier on / off switching, the E2 node may activate measurement reports (i.e., EE / EC measurement reports) to the O-RU, and the O-RU provides measurement data (i.e., input data) for the measurement reports.

[0116] Upon collecting O1 related data from the O-RU, the E2 node may send the O1 related data to the rApp via the R1 interface providing the O1 configuration necessary to perform cell and / or carrier on / off switching collected via the O1 interface through the SMO functions in the SMO framework and the NRT-RIC framework.

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

[0118] In one embodiment, O1-related data that provides the O1 configuration necessary to perform cell and / or carrier on / off switching may be input data (e.g., measurement data) used in training and inference of AI / ML models. The O1-related data providing 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 (data volume in DL delivered from O-CU-UP to O-DU) per interface, per public land mobile network (PLMN), per Quality of Service (QoS) level, per slice, per F1-U interface, Xn-U interface, X2-U interface; uplink Packet Data Convergence Protocol Service Data Unit (UP PDCP SDU) data volume (data volume in DL delivered from O-CU-UP to O-DU) per interface, per public land mobile network (PLMN), per Quality of Service (QoS) level, per slice, per F1-U interface, Xn-U interface, X2-U interface; SDU) data volume (volume of data in the UL delivered from O-CU-UP to O-DU), Reference Signal Received Quality (RSRQ) measurement results per cell per Synchronization Signal Block (SSB), Reference Signal Received Power (RSRP) measurement results per cell per SSB, Signal-to-Interference-plus-Noise Ratio (SINR) measurement results per cell per SSB, energy consumption, power consumed by hardware components, transmit power.

[0119] In one embodiment, the NRT-RIC framework may retrain at least one AI / ML model. According to this embodiment, an rApp selects an AI / ML model from multiple AI / ML models and sends an initiation request to the NRT-RIC framework to retrain the AI / ML model. The NRT-RIC framework retrains the AI / ML model. In doing so, the rApp monitors the retrained AI / ML model parameters and determines, based on the retrained AI / ML model parameters, to retrieve the retrained AI / ML model from the NRT-RIC framework. Based on the retrieval decision, the 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 the rApp.

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

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

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

[0123] Additionally, in one embodiment, the rApp monitors performance and energy consumption parameters for evaluation of the O1 configurations required to perform cell and carrier shutdown. These performance and energy consumption parameters may include configurations, performance indicators, 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) providing the O1 configuration necessary to perform cell and / or carrier on / off switching may include at least one of configuration, performance indicators, and measurement reports provided from 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 energy consumption of the E2 node, energy consumption of the O-RU, and one or more performance-related KPIs of the E2 node.

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

[0126] Further, in step 504, when the rApp generates O1 configuration data for preparing and performing cell and / or carrier on / off switching, it sends the O1 configuration data for preparing and performing cell and / or carrier on / off switching 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, based on O1-related data (i.e., input data) that provides the O1 configuration required to perform cell and / or carrier on / off switching, if a predetermined performance target (e.g., EE / ES performance target) is not achieved, the rApp determines to generate O1 configuration data (i.e., output data) for preparing and performing cell and / or carrier on / off switching. In this case, the rApp generates O1 configuration data for preparing and performing cell and / or carrier on / off switching.

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

[0129] In one embodiment, the generated O1 configuration data (i.e., output data) for preparing and performing cell and / or carrier on / off switching may include, for example, an NRCellCU Information Object Class IOC, an NRCellDU IOC, a GNBDUFunction IOC, a GNBCUCPFunction IOC, a 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 shutdown rApps to reconfigure resources over the O1 interface.

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

[0131] In one embodiment, implementing the O1 configuration data to prepare and perform cell and / or carrier on / off switching may further include the O-RU notifying the E2 node of completion of the implementation of the cell and / or carrier on / off switching. Upon receiving the notification from the O-RU, the E2 node notifies the rApp via the O1 interface through the SMO function and via the R1 interface through the NRT-RIC framework. According to an embodiment, the O-RU may notify the E2 node via an open fronthaul management plane (FH M-Plane) interface between the E2 node and the O-RU.

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

[0133] 6 illustrates a data collection flow according to one embodiment. Referring to FIG. 6, the data collection is aimed at enabling carrier and cell on / off switching energy saving functions in the O-RAN through configuration parameter changes (i.e., implementation of O1 configuration data to prepare and execute cell and / or carrier on / off switching) and actions that enable AI / ML-based solutions to optimize cell and / or carrier on / off switching for EE / ES in the O-RAN controlled by the NRT-RIC.

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

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

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

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

[0138] As a result, the method for optimizing cell and / or carrier on / off switching may begin when the network operator activates the optimization rApp along with the initial AI / ML models for carrier and cell on / off switching ES functions and the E2 nodes and O-RUs are operational.

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

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

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

[0142] In operation 4, upon receiving the request from the SMO, the E2 node (i.e., O-CU, O-DU, etc.) requests and collects O1-related data (i.e., configuration data, configured measurement data) required to optimize carrier and / or cell on / off switching from the O-RU via the open FH M-Plane interface.

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

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

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

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

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

[0148] In operation 10, the rApp monitors the retrained AI / ML 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 in the SMO based on the R1 service.

[0149] In operation 11, upon receiving a retrieval request from the rApp, the NRT-RIC framework forwards the AI / ML model (i.e., the retrained AI / ML model) to the 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 SMO based on the R1 service.

[0151] In operation 13, once the retraining of the AI / ML models has been performed, at least one AI / ML model (including the retrained AI / ML model) is deployed and activated for inference (i.e., for inferring data that provides the O1 configuration necessary to perform cell and / or carrier on / off switching within the O-RAN).

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

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

[0154] In operation 13 of FIG. 8, once the retraining of the AI / ML models has been performed, at least one AI / ML model (including the retrained AI / ML model) is deployed and activated for inference (i.e., for inferring data that provides the O1 configuration necessary to perform on / off switching of cells and / or carriers within the O-RAN).

[0155] 7 and 8, in operation 13, the rApp constantly monitors the performance and energy consumption of the E2 node, the energy consumption of the O-RU, etc. For example, the rApp monitors the performance and energy consumption in order to evaluate the O1 configuration required to perform cell and carrier shutdown.

[0156] In one embodiment, the O1-related data may be measurement input data used in training and inference of AI / ML models. The O1-related data may include, in addition to other O1-related data, the following measurement data for monitoring 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 (data volume in DL delivered from O-CU-UP to O-DU) per interface, per public land mobile network (PLMN), per quality of service (QoS) level, per slice, per F1-U interface, Xn-U interface, and X2-U interface; uplink Packet Data Convergence Protocol Service Data Unit (UP PDCP SDU) data volume (data volume in DL delivered from O-CU-UP to O-DU) per interface, per public land mobile network (PLMN), per quality of service (QoS) level, per slice, per F1-U interface, Xn-U interface, and X2-U interface; SDU) data volume (data volume in the UP delivered from the O-CU-UP to the O-DU), Reference Signal Received Quality (RSRQ) measurement results per cell per Synchronization Signal Block (SSB), Reference Signal Received Power (RSRP) measurement results per cell per SSB, Signal-to-Interference-plus-Noise Ratio (SINR) measurement results per cell per SSB, energy consumption, power consumed by hardware components, transmit power, etc.

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

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

[0159] In one embodiment, the generated O1 configuration data for preparing and performing cell and / or carrier on / off switching 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 shutdown and reconfigure resources via the O1 interface.

[0160] In operation 15, the NRT-RIC requests, via the O1 interface, that the SMO framework function (i.e., 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 informs the O-RU of the updated O-RU configuration via an open FH M-Plane. In one embodiment, the O-RU may inform the E2 node when the O-RU configuration is implemented.

[0163] In operation 18, the E2 node notifies the SMO once the cell or carrier on / off switching is complete.

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

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

[0166] In operation 21, the NRT-RIC continuously analyzes the performance of the AI / ML model. In one embodiment, if the energy savings goal is not achieved, the 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 cell and / or carrier on / off switching may terminate when the E2 node goes out of service or when the operator disables the optimization function or the AI / ML model for saving energy (i.e., the AI / ML model for EE / ES).

[0168] In another embodiment, the rApp maintains closed-loop monitoring of energy saving functions in the E2 node and the O-RU (via the E2 node).

[0169] According to operation 21, the E2 node and O-RU are put into operation using the 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 an embodiment, a system for implementing carrier and / or cell on / off switching optimization via a Service Management and Orchestration (SMO) framework in an Open Radio Access Network (O-RAN) is provided, the system including: a memory storing instructions; and at least one processor configured to implement a Non-Real-Time RAN Intelligent Controller (NRT-RIC), the NRT-RIC framework, at least one SMO function, and an rApp hosted by the NRT-RIC.The at least one processor, via an rApp, collects O1-related data from the E2 node via an R1 interface through the NRT-RIC framework and via an O1 interface through an SMO function in the SMO framework, the O1-related data providing O1 configurations necessary to perform cell and / or carrier on / off switching, the O1-related data being collected via an open fronthaul management plane (FH M-Plane) interface between the E2 node and the open radio unit (O-RU); retrains, via the SMO, at least one artificial intelligence / machine learning (AI / ML) model based on the collected O1-related data; deploys and activates, via the rApp, one retrained AI / ML model from the at least one retrained AI / ML for inferring data providing O1 configurations necessary to perform cell and / or carrier on / off switching within the O-RAN; and deploys, via the rApp, one retrained AI / ML model for inferring data providing O1 configurations necessary to perform cell and / or carrier on / off switching within the O-RAN. and executing instructions to: monitor O1-related data providing the O1 configuration required to perform carrier on / off switching; evaluate, by an rApp, the O1-related data providing the O1 configuration required to perform cell and / or carrier on / off switching; determine, by the rApp, to generate O1 configuration data for preparing and performing cell and / or carrier on / off switching; send, by the rApp, the O1 configuration data for preparing and performing cell and / or carrier on / off switching to at least one E2 node via an R1 interface through the NRT-RIC framework and via an O1 interface through at least one SMO function in the SMO framework; and implement, by the E2 node and the O-RU, the cell and / or carrier on / off switching in the O-RAN.The at least one processor is further configured, during implementation, to convert, by the E2 node, O1 configuration data for preparing and performing on / off switching of cells and / or carriers, and to instruct, by the E2 node, via the open FH M-Plane, the O-RU to perform on / off switching of cells and / or carriers.

[0171] The at least one processor may be further configured to, during retraining of the at least one AI / ML model, select, by the rApp, an AI / ML model from the plurality of AI / ML models; send, by the rApp, an initiation request to retrain the AI / ML model to the NRT-RIC framework; retrain, by the NRT-RIC framework, the AI / ML model; monitor, by the rApp, the retrained AI / ML model parameters; determine, based on the retrained AI / ML model parameters, to retrieve the retrained AI / ML model from the NRT-RIC framework; request, by the rApp, the retrained AI / ML model from the NRT-RIC framework; and send, by the NRT-RIC framework, the retrained AI / ML model to the rApp.

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

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

[0174] The at least one processor may be configured to: during collection of O1-related data providing O1 configurations necessary to perform cell and / or carrier on / off switching, send, by the rApp, an O1-related data collection request to the E2 node via the R1 interface through the NRT-RIC framework and via the O1 interface through an SMO function in the SMO framework; receive, by the E2 node, the O1-related data collection request from the SMO function; collect, by the E2 node, O1-related data providing O1 configurations necessary to perform cell and / or carrier on / off switching from the O-RU via an open fronthaul management plane (FH M-Plane) interface between the E2 node and the O-RU; and send, by the E2 node, the O1-related data providing O1 configurations 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] The at least one processor may be further configured to: notify, by the O-RU, the E2 node via an FH M-Plane interface between the E2 node and the O-RU of completion of implementation of the cell and / or carrier on / off switching, while instructing the O-RU via the open FH M-Plane to perform cell and / or carrier on / off switching; and notify, by the E2 node, the rApp via an O1 interface through an SMO function and via an R1 interface through an NRT-RIC framework within the SMO framework of completion of implementation of the cell and / or carrier on / off switching.

[0176] The at least one processor may be further configured to monitor, by the NRT-RIC, the performance of the retrained AI / ML model, determine that predetermined performance goals are not achieved based on the collected O1-related data, and initiate a fallback mechanism and / or an update or retraining of the AI / ML model.

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

[0178] Retraining at least one AI / ML model may include selecting, by the rApp, an AI / ML model from the plurality of AI / ML models; sending, by the rApp, an initiation request to the NRT-RIC framework to retrain the AI / ML model; retraining, by the NRT-RIC framework, the AI / ML model; monitoring, by the rApp, retrained AI / ML model parameters and determining, based on the retrained AI / ML model parameters, to retrieve the retrained AI / ML model from the NRT-RIC framework; requesting, by the rApp, the retrained AI / ML model from the NRT-RIC framework; and sending, by the NRT-RIC framework, the retrained AI / ML model to the rApp.

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

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

[0181] Collecting O1-related data that provides the O1 configuration required to perform cell and / or carrier on / off switching may include sending, by the rApp, an O1-related data collection request to the E2 node via the R1 interface through the NRT-RIC framework and via the O1 interface through an SMO function in the SMO framework; receiving, by the E2 node, the O1-related data collection request from the SMO function; collecting, by the E2 node, O1-related data from an open radio unit (O-RU) via an open fronthaul management plane (FH M-Plane) interface between the E2 node and the O-RU that provides the O1 configuration required to perform cell and / or carrier on / off switching; and sending, by the E2 node, the O1-related data that provides the O1 configuration required to perform cell and / or carrier on / off switching that has been 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 completion of implementation of the cell and / or carrier on / off switching via an FH M-Plane interface between the E2 node and the O-RU; and the E2 node notifying the rApp of completion of implementation of the cell and / or carrier on / off switching via an O1 interface through an SMO function and via an R1 interface through an NRT-RIC framework within the SMO framework.

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

[0184] According to an embodiment, there is provided a non-transitory computer-readable storage medium having stored thereon instructions executable by at least one processor configured to implement a Non-Real-Time RAN Intelligent Controller (NRT-RIC), an NRT-RIC framework, at least one SMO function, and an rApp hosted by the NRT-RIC to perform a method for implementing carrier and / or cell on / off switching optimization via a Service Management and Orchestration (SMO) framework in an Open Radio Access Network (O-RAN).The method includes collecting, by an rApp, O1-related data from an E2 node via an R1 interface through an NRT-RIC framework and via an O1 interface through an SMO function in an SMO framework, the O1-related data providing O1 configurations necessary to perform cell and / or carrier on / off switching, the O1-related data being collected via an open fronthaul management plane (FH M-Plane) interface between an E2 node and an open radio unit (O-RU); retraining, by the SMO, at least one artificial intelligence / machine learning (AI / ML) model based on the collected O1-related data; deploying and activating, by the rApp, one of the at least one retrained AI / ML model for inferring data providing O1 configurations necessary to perform cell and / or carrier on / off switching in an O-RAN; and transmitting, by the rApp, O1-related data through the NRT-RIC framework via the R1 interface and via the O1 interface through an SMO function in the SMO framework. and monitoring O1-related data providing the O1 configuration required to perform cell and / or carrier on / off switching; evaluating, by the rApp, the O1-related data providing the O1 configuration required to perform cell and / or carrier on / off switching; determining, by the rApp, to generate O1 configuration data for preparing and performing cell and / or carrier on / off switching; sending, by the rApp, the O1 configuration data for preparing and performing cell and / or carrier on / off switching to at least one E2 node via an R1 interface through the NRT-RIC framework and via an O1 interface through at least one SMO function in the SMO framework; and implementing, by the E2 node and the O-RU, the cell and / or carrier on / off switching in the O-RAN.The implementing may include: converting, by the E2 node, O1 configuration data for preparing and performing on / off switching of the cell and / or carrier; and instructing, by the E2 node, via an open FH M-Plane, the O-RU to perform on / off switching of the cell and / or carrier.

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

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

[0187] Collecting O1-related data that provides the O1 configuration required to perform cell and / or carrier on / off switching may include sending, by the rApp, an O1-related data collection request to the E2 node via the R1 interface through the NRT-RIC framework and via the O1 interface through an SMO function in the SMO framework; receiving, by the E2 node, the O1-related data collection request from the SMO function; collecting, by the E2 node, O1-related data from an open radio unit (O-RU) via an open fronthaul management plane (FH M-Plane) interface between the E2 node and the O-RU that provides the O1 configuration required to perform cell and / or carrier on / off switching; and sending, by the E2 node, the O1-related data that provides the O1 configuration required to perform cell and / or carrier on / off switching that has been 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 completion of implementation of the cell and / or carrier on / off switching via an FH M-Plane interface between the E2 node and the O-RU; and the E2 node notifying the rApp of completion of implementation of the cell and / or carrier on / off switching via an O1 interface through an SMO function and via an R1 interface through an NRT-RIC framework within the SMO framework.

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

[0190] According to embodiments, the systems and methods provide carrier and / or cell on / off switching control that considers network-wide energy efficiency instead of local optimization in O-RAN. For example, the capabilities of the AI / ML model may include prediction of future traffic, user mobility, and resource usage, and may predict expected energy efficiency improvements, resource usage, and network performance for different energy-saving optimization states.

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

[0192] The foregoing disclosure provides illustration and description, but is not intended to be exhaustive or to limit implementations to the precise form disclosed. Modifications and variations are possible in light of the foregoing disclosure or may be acquired from practice of the implementations.

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

[0194] A computer-readable storage medium may be a tangible device that can hold and store instructions for use by an instruction execution device. A computer-readable storage medium may be, for example, but 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 the following: 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 versatile disk (DVD), memory sticks, floppy disks, mechanically encoded devices such as punch cards or raised structures in grooves in which instructions are recorded, and any suitable combination thereof. As used herein, computer-readable storage medium is not to be understood as a transitory signal per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., light pulses passing through a fiber optic cable), or electrical signals transmitted over wires.

[0195] The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium into each computing / processing device, or may be downloaded to an external computer or external storage device over 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 fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and transfers the computer-readable program instructions for storage in a computer-readable storage medium within the respective computing / processing device.

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

[0197] These computer-readable program instructions may be provided to a processor of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus to produce an apparatus, such that the instructions, when executed by the processor of the computer or other programmable data processing apparatus, produce means for implementing the functions / acts set forth in the flowcharts and / or block diagrams (one or more blocks). These computer-readable program instructions may be stored on a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and / or other device to function in a particular manner, such that the computer-readable storage medium on which the instructions are stored comprises an article including instructions that implement aspects of the functions / acts set forth in the flowcharts and / or block diagrams (one or more blocks).

[0198] The computer-readable program instructions may be loaded onto a computer, other programmable data processing apparatus, or other device such that a series of operational steps are performed on the computer, other programmable apparatus, or other device to generate a computer-implemented process such that the instructions, executing on the computer, other programmable apparatus, or other device, implement the functions / acts described in the flowcharts and / or block diagrams (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. Each block in a flowchart or block diagram may represent a microservice, module, segment, or portion of instructions, comprising one or more executable instructions for implementing specific logical functions. The methods, computer systems, and computer-readable media may include additional, fewer, different, or differently arranged blocks than those shown in the figures. In some alternative implementations, the functions shown in the blocks may occur out of the order shown in the figures. For example, two blocks shown in succession may actually be executed concurrently or substantially concurrently, depending on the functionality involved, or the blocks may be executed in the reverse order. Note that each block of the block diagram and / or flowchart illustrations, and combinations of blocks in the block diagram and / or flowchart illustrations, may be implemented by a dedicated hardware-based system that performs specific functions or acts, or by executing a combination of dedicated hardware and computer instructions.

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

Claims

1. 1. A system for implementing carrier and / or cell on / off switching optimization via a service management and orchestration (SMO) framework in an open radio access network (O-RAN), comprising: a memory for storing instructions; at least one processor configured to implement a Non-Real-Time RAN Intelligent Controller (NRT-RIC), an NRT-RIC framework, at least one SMO function, and an rApp hosted by the NRT-RIC; Equipped with The at least one processor: collecting, by an rApp, O1-related data from the E2 node via an R1 interface through an NRT-RIC framework and via an O1 interface through an SMO function within the SMO framework, the O1-related data providing the O1 configuration required to perform the cell and / or carrier on / off switching, the O1-related data being collected via an open fronthaul management plane (FH M-Plane) interface between the E2 node and an open radio unit (O-RU); retraining, by the SMO, at least one artificial intelligence / machine learning (AI / ML) model based on the collected O1-related data; deploying and activating, by the rApp, one of the at least one retrained AI / ML models to infer data providing O1 configurations necessary to perform on / off switching of the cell and / or carrier within the O-RAN; monitoring, by the rApp, the O1-related data that provides the O1 configuration necessary to perform the 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; evaluating, by the rApp, the O1-related data providing the O1 configuration necessary to perform the cell and / or carrier on / off switching; determining, by the rApp, to generate O1 configuration data for preparing and performing on / off switching of the cell and / or carrier; sending, by the rApp, the O1 configuration data to the at least one E2 node via the R1 interface through the NRT-RIC framework and via the O1 interface through the at least one SMO function within the SMO framework, for preparing and performing the cell and / or carrier on / off switching; Implementing, by the E2 node and the O-RU, on / off switching of the cells and / or carriers within the O-RAN; configured to execute the instructions to The at least one processor, during implementation, translating, by the E2 node, the O1 configuration data for preparing and performing on / off switching of the cell and / or carrier; instructing the O-RU to perform on / off switching of the cell and / or carrier via the open FH M-Plane by the E2 node; The system further comprises:

2. The at least one processor, during retraining of the at least one AI / ML model, The rApp selects one AI / ML model from multiple AI / ML models, sending, by the rApp, an initiation request to the NRT-RIC framework to retrain the AI / ML model; retraining the AI / ML model with the NRT-RIC framework; monitoring, by the rApp, retrained AI / ML model parameters and determining, based on the retrained AI / ML model parameters, the withdrawal of the retrained AI / ML model from the NRT-RIC framework; requesting the retrained AI / ML model from the NRT-RIC framework by the rApp; Sending the retrained AI / ML model to the rApp via the NRT-RIC framework; The system of claim 1 further configured to:

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

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

5. The at least one processor, during collection of the O1 related data, provides the O1 configuration necessary to perform the cell and / or carrier on / off switching. sending, by the rApp, an O1-related data collection request to the E2 node via an R1 interface through the NRT-RIC framework and via an O1 interface through the SMO function in the SMO framework; receiving, by the E2 node, the O1-related data collection request from the SMO function; collecting, by the E2 node, from the open radio unit (O-RU) via an open fronthaul management plane (FH M-Plane) interface between the E2 node and the O-RU, the O1-related data providing the O1 configuration required to perform on / off switching of the cell and / or carrier; sending, by the E2 node, to the rApp via the R1 interface, the O1-related data providing the O1 configuration necessary to perform the SMO functions within the SMO framework and the cell and / or carrier on / off switching collected via the O1 interface through the NRT-RIC framework; The system of claim 1 configured to:

6. While the at least one processor instructs the O-RU to perform on / off switching of the cell and / or carrier via the open FH M-Plane, notifying the E2 node, by the O-RU, of the completion of the implementation of the cell and / or carrier on / off switching via the FH M-Plane interface between the E2 node and the O-RU; notifying the rApp of the completion of the implementation of the cell and / or carrier on / off switching by the E2 node via the O1 interface through the SMO function and via the R1 interface through the NRT-RIC framework within the SMO framework; The system of claim 1 further configured to:

7. The at least one processor: monitoring the performance of the retrained AI / ML model by the NRT-RIC; determining that predetermined performance goals will not be achieved based on the collected O1-related data; Initiating fallback mechanisms and / or updating or retraining AI / ML models; The system of claim 1 further configured to:

8. 1. A method for implementing carrier and / or cell on / off switching optimization via a service management and orchestration (SMO) framework in an open radio access network (O-RAN), comprising: collecting, by an rApp, O1-related data that provides the O1 configuration required to perform the cell and / or carrier on / off switching, the O1-related data being collected via an open fronthaul management plane (FH M-Plane) interface between an E2 node and an open radio unit (O-RU), from the E2 node via an R1 interface through an NRT-RIC framework and via an O1 interface through an SMO function within the SMO framework; retraining, by the SMO, at least one artificial intelligence / machine learning (AI / ML) model based on the collected O1-related data; deploying and activating, by the rApp, one of the at least one retrained AI / ML models to infer data providing O1 configurations necessary to perform on / off switching of the cell and / or carrier within the O-RAN; monitoring, by the rApp, the O1-related data providing the O1 configuration necessary to perform 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; evaluating, by the rApp, the O1 related data that provides the O1 configuration necessary to perform the cell and / or carrier on / off switching; determining, by said rApp, to generate O1 configuration data for preparing and performing said cell and / or carrier on / off switching; sending, by the rApp, the O1 configuration data to the at least one E2 node via the R1 interface through the NRT-RIC framework and via the O1 interface through the at least one SMO function within the SMO framework, for preparing and performing the cell and / or carrier on / off switching; Implementing, by the E2 node and the O-RU, on / off switching of the cells and / or carriers within the O-RAN; Equipped with The implementation comprises: converting, by the E2 node, the O1 configuration data for preparing and performing on / off switching of the cell and / or carrier; instructing, by the E2 node, the O-RU via the open FH M-Plane to perform on / off switching of the cell and / or carrier; A method for providing the above.

9. retraining the at least one AI / ML model selecting, by the rApp, one AI / ML model from a plurality of AI / ML models; sending, by the rApp, an initiation request to retrain the AI / ML model to the NRT-RIC framework; retraining the AI / ML model with the NRT-RIC framework; monitoring, by the rApp, retrained AI / ML model parameters and determining, based on the retrained AI / ML model parameters, the withdrawal of the retrained AI / ML model from the NRT-RIC framework; requesting the retrained AI / ML model from the NRT-RIC framework by the rApp; Sending the retrained AI / ML model to the rApp via the NRT-RIC framework; The method of claim 8 comprising:

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

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

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

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

14. monitoring the performance of the retrained AI / ML model by the NRT-RIC; determining that predetermined performance goals will not be achieved based on the collected O1-related data; and Initiating fallback mechanisms and / or updating or retraining AI / ML models; and The method of claim 8 further comprising:

15. 1. A non-transitory computer-readable storage medium having stored thereon instructions executable by at least one processor configured to implement a Non-Real-Time RAN Intelligent Controller (NRT-RIC), an NRT-RIC framework, at least one SMO function, and an rApp hosted by the NRT-RIC to perform a method for implementing carrier and / or cell on / off switching optimization via a Service Management and Orchestration (SMO) framework in an Open Radio Access Network (O-RAN), the non-transitory computer-readable storage medium comprising: The method comprises: collecting, by an rApp, O1-related data that provides the O1 configuration required to perform the cell and / or carrier on / off switching, the O1-related data being collected via an open fronthaul management plane (FH M-Plane) interface between an E2 node and an open radio unit (O-RU), from the E2 node via an R1 interface through an NRT-RIC framework and via an O1 interface through an SMO function within the SMO framework; retraining, by the SMO, at least one artificial intelligence / machine learning (AI / ML) model based on the collected O1-related data; deploying and activating, by the rApp, one of the at least one retrained AI / ML models to infer data providing O1 configurations necessary to perform on / off switching of the cell and / or carrier within the O-RAN; monitoring, by the rApp, the O1-related data providing the O1 configuration necessary to perform 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; evaluating, by the rApp, the O1 related data that provides the O1 configuration necessary to perform the cell and / or carrier on / off switching; determining, by said rApp, to generate O1 configuration data for preparing and performing said cell and / or carrier on / off switching; sending, by the rApp, the O1 configuration data to the at least one E2 node via the R1 interface through the NRT-RIC framework and via the O1 interface through the at least one SMO function within the SMO framework, for preparing and performing the cell and / or carrier on / off switching; Implementing, by the E2 node and the O-RU, on / off switching of the cells and / or carriers within the O-RAN; Equipped with The implementation comprises: converting, by the E2 node, the O1 configuration data for preparing and performing on / off switching of the cell and / or carrier; instructing, by the E2 node, the O-RU via the open FH M-Plane to perform on / off switching of the cell and / or carrier; A non-transitory computer-readable recording medium comprising:

16. retraining the at least one AI / ML model selecting, by the rApp, one AI / ML model from a plurality of AI / ML models; sending, by the rApp, an initiation request to retrain the AI / ML model to the NRT-RIC framework; retraining the AI / ML model with the NRT-RIC framework; monitoring, by the rApp, retrained AI / ML model parameters and determining, based on the retrained AI / ML model parameters, the withdrawal of the retrained AI / ML model from the NRT-RIC framework; requesting the retrained AI / ML model from the NRT-RIC framework by the rApp; Sending the retrained AI / ML model to the rApp via the NRT-RIC framework; 16. The non-transitory computer-readable storage medium of claim 15, comprising:

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

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

16. The non-transitory computer-readable storage medium of claim 15.

19. collecting the O1 related data that provides the O1 configuration necessary to perform the cell and / or carrier on / off switching; sending, by the rApp, an O1-related data collection request to the E2 node via an R1 interface through the NRT-RIC framework and via an O1 interface through the SMO function in the SMO framework; receiving, by the E2 node, the O1-related data collection request from the SMO function; collecting, by the E2 node, from the open radio unit (O-RU) via an open fronthaul management plane (FH M-Plane) interface between the E2 node and the O-RU, the O1-related data providing the O1 configuration required to perform on / off switching of the cell and / or carrier; sending, by the E2 node, to the rApp via the R1 interface, the O1-related data providing the O1 configuration necessary to perform the SMO functions within the SMO framework and the cell and / or carrier on / off switching collected via the O1 interface through the NRT-RIC framework; 16. The non-transitory computer-readable storage medium of claim 15, comprising:

20. Instructing the O-RU to perform on / off switching of the cell and / or carrier includes: notifying, by the O-RU, the E2 node of completion of the implementation of the cell and / or carrier on / off switching via the FH M-Plane interface between the E2 node and the O-RU; notifying the rApp of completion of the implementation of the cell and / or carrier on / off switching by the E2 node via the O1 interface through the SMO function and via the R1 interface through the NRT-RIC framework within the SMO framework; 16. The non-transitory computer-readable storage medium of claim 15, further comprising:

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