System and method for optimizing high-frequency channel reconfiguration in a communication network

Abstract

A system and method for implementing optimization of RF reconfiguration by an SMO framework within an O-RAN, the method comprising: collecting O1-related data from an E2 node, the O1-related data being O1-related data that provides an O1 configuration required to perform RF channel reconfiguration and that is collected between the E2 node and an Open Radio Unit (O-RU); retraining an AI / ML model based on the collected O1-related data; deploying and activating one retrained AI / ML model for inferring data that provides an O1 configuration required to perform RF channel reconfiguration within the O-RAN; monitoring O1-related data that provides an O1 configuration required to perform RF channel reconfiguration; evaluating the O1-related data that provides an O1 configuration required to perform RF channel reconfiguration; determining to generate O1 configuration data for preparing and performing RF channel reconfiguration and sending it to the E2 node; and implementing RF channel reconfiguration within the O-RAN.

Description

Technical Field

[0001] Embodiments consistent with the present disclosure relate to the generation and deployment of optimizations for radio frequency (RF) channel reconfiguration in m-MIMO antennas to save energy in communication networks.

Background Art

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

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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the O-RAN related to the related art, a large-capacity multi-input multi-output (m-MIMO) antenna is used for beamforming technology to increase cell capacity and traffic throughput. To realize beamforming, the RU must concentrate the power amplifier at the antenna site by combining radiation elements such as a transmitter / receiver (Tx / Rx) array.

[0005] In the related art, up to 64 transmitter / receiver (Tx / Rx) arrays can be deployed to increase cell capacity and traffic throughput to the maximum. Here, as the number of Tx / Rx arrays becomes larger, the cell capacity and traffic throughput become larger. In this case, the m-MIMO antenna also consumes the maximum energy for beamforming.

[0006] Up to 64 transmitter / receiver (Tx / Rx) arrays are preferably used as the standard setting of the m-MIMO antenna in the related art. This maximum beamforming has the drawback that, in the case of low O-RAN load, that is, when the expected traffic volume or the number of connected users is lower than the configured threshold, the high power consumption of the RU due to the maximum use of the Tx / Rx array results in an operation of the RU with low energy efficiency within the O-RAN. Means for Solving the Problems

[0007] According to an embodiment, a system and method are provided for implementing optimization of radio frequency (RF) channel reconfiguration in an O-RAN m-MIMO antenna by an open radio access network and service management and orchestration (SMO). The SMO 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 RF channel reconfiguration within the m-MIMO antenna by one or more E2 nodes (i.e., O-RUs via the E node). With the assistance of artificial intelligence / machine learning (AI / ML) techniques, O1-related data from the E2 node (i.e., from the O-RU via the E2 node) is used for retraining, deploying, and activating an AI / ML model to infer data that provides the O1 configuration required to execute RF channel reconfiguration within the m-MIMO antenna in the O-RU. The RF channel reconfiguration control provides the O1 configuration required to execute RF channel reconfiguration in the O-RU and considers the energy efficiency of the entire network instead of local optimization in the O-RAN. According to an example, the system and method enable energy savings (ES) by reducing the power consumption of the O-RU by RF channel reconfiguration (e.g., by switching off 32 of the 64 Tx / Rx arrays of the O-RU in a digital m-MIMO architecture).

[0008] For example, when the network load is low (i.e., when the expected traffic volume or the number of connected users is lower than a configured threshold), ES can be achieved by reducing the power consumption of the O-RU, for example, by switching off 32 of the 64 Tx / Rx arrays of the O-RU in a digital m-MIMO architecture and reducing the number of corresponding spatial layers and synchronization signal blocks (SSBs). The procedure for RF channel reconfiguration (i.e., the involvement of each O-RAN interface) depends on the management architecture model (hybrid or hierarchical) and deployment options.

[0009] The determination of RF channel reconfiguration can be performed by an AI / ML model within an inference host deployed in an NRT-RIC or a near real-time RAN intelligent controller (nRT-RIC). The AI / ML model may include predictions of future traffic, user mobility, and resource usage in addition to other parameters (i.e., prediction data), and may predict the expected energy efficiency improvement, resource usage, and network performance for different ES optimization states.

[0010] As a result, the system and method implement an NRT-RIC framework that enables a network operator to flexibly configure on / off switching parameters for RF channel reconfiguration (e.g., for switching on / off Tx / Rx arrays within m-MIMO antennas in an O-RU) to optimize the energy efficiency of the entire network instead of local optimization in O-RAN.

[0011] According to an embodiment, a system for implementing optimization of radio frequency (RF) reconfiguration by a service management and orchestration (SMO) framework within an Open Radio Access Network (O-RAN) is provided. The system includes a memory for storing instructions and at least one processor configured to implement a non-real-time RAN intelligent controller (NRT-RIC), an NRT-RIC framework, at least one SMO function, and an rApp hosted by the NRT-RIC.At least one processor collects, via the R1 interface through the NRT-RIC framework and via the O1 interface through the SMO function within the SMO framework, O1-related data provided by the rApp for performing RF channel reconfiguration, which is O1-related data collected via the open front-haul management plane (FH M-Plane) interface between the E2 node and the open radio unit (O-RU). The SMO retrains at least one artificial intelligence / machine learning (AI / ML) model based on the collected O1-related data. The rApp deploys and activates one retrained AI / ML model for inferring data that provides the O1 configuration necessary for performing RF channel reconfiguration within O-RAN among at least one retrained AI / ML. The rApp monitors the O1-related data that provides the O1 configuration necessary for performing RF channel reconfiguration via the R1 interface through the NRT-RIC framework and via the O1 interface through the SMO function within the SMO framework. The rApp evaluates the O1-related data that provides the O1 configuration necessary for performing RF channel reconfiguration. The rApp determines to generate O1 configuration data for preparing and performing RF channel reconfiguration. The rApp sends, via the R1 interface through the NRT-RIC framework and via the O1 interface through at least one SMO function within the SMO framework, the O1 configuration data for preparing and performing RF channel reconfiguration to at least one E2 node. The E2 node and the O-RU are configured to execute instructions to implement the RF channel reconfiguration within O-RAN. At least one processor is further configured such that, during implementation, the E2 node converts the O1 configuration data for preparing and performing RF channel reconfiguration and instructs the O-RU to perform RF channel reconfiguration via the open FH M-Plane.

[0012] At least one processor may be further configured to, during the retraining of at least one AI / ML model, select, by an rApp, one AI / ML model from a plurality of AI / ML models, send, by the rApp, a start request for retraining 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, the retrieval of 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.

[0013] At least one processor may be further configured to, during the retraining of at least one AI / ML model, select, by an rApp, one AI / ML model from a plurality of AI / ML models for retraining.

[0014] The O1-related data that provides the O1 configuration necessary to perform RF channel reconfiguration 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 energy efficiency / energy consumption (EE / EC) measurement reports. The energy efficiency / energy consumption (EE / EC) measurement reports include reference signal received quality (RSRQ) measurement results for each synchronization signal block (SSB) per cell, reference signal received power (RSRP) measurement results for each SSB per cell, signal-to-interference plus noise ratio (SINR) measurement results for each SSB per cell, energy consumption, power consumed by hardware components, transmit power, load statistics per cell and per carrier (number of active users, average number of radio resource control (RRC) connections, average number of scheduled active users per transmission time interval (TTI), physical resource block (PRB) utilization rate, downlink / uplink (DL / UL) cell / user throughput, precoding matrix indicator / channel state information (PMI / CSI) reports, latency statistics per cell, and power consumption metric information on supported Tx / Rx array selection combined with power consumption key performance indicator (KPI), etc.), and may include at least one of them.

[0015] At least one processor, during the collection of O1-related data that provides the O1 configuration necessary to perform RF channel reconfiguration, sends an O1-related data collection request to an E2 node by an rApp, via the R1 interface through the NRT-RIC framework, and via the O1 interface through the SMO function within the SMO framework. The E2 node receives the O1-related data collection request from the SMO function, and the E2 node collects O1-related data that provides the O1 configuration necessary to perform RF channel reconfiguration from an O-RU via the open front-haul management plane (FH M-Plane) interface between the E2 node and the open radio unit (O-RU). The E2 node then sends the O1-related data that provides the O1 configuration necessary to perform RF channel reconfiguration, collected via the O1 interface through the SMO function within the SMO framework and the NRT-RIC framework, to the rApp via the R1 interface. It may be configured as such.

[0016] The O1 configuration data for preparing and performing RF channel reconfiguration may include at least one of O-RU Tx / Rx array selection, change in the number of SU / MU MIMO spatial streams or data layers, change in the number of SSB beams, and change in the antenna transmit power of the O-RU.

[0017] At least one processor may be further configured to monitor the performance of the retrained AI / ML model by the NRT-RIC, determine that a predetermined performance target is not achieved based on the collected O1-related data, and initiate a fallback mechanism and / or an update or retraining of the AI / ML model.

[0018] According to an embodiment, a method for implementing optimization of radio frequency (RF) reconfiguration by a service management and orchestration (SMO) framework within an Open Radio Access Network (O-RAN) is provided. The method includes: O1-related data provided by an rApp for an O1 configuration required to perform RF channel reconfiguration, the O1-related data collected via an Open Front-Haul Management Plane (FH M-Plane) interface between an E2 node and an Open Radio Unit (O-RU), being collected 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 SMO retraining at least one artificial intelligence / machine learning (AI / ML) model based on the collected O1-related data; the rApp deploying and activating one retrained AI / ML model for inferring data for an O1 configuration required to perform RF channel reconfiguration within the O-RAN among the at least one retrained AI / ML; the rApp monitoring O1-related data for an O1 configuration required to perform RF channel reconfiguration via an R1 interface through an NRT-RIC framework and via an O1 interface through at least one SMO function within the SMO framework; the rApp evaluating the O1-related data for an O1 configuration required to perform RF channel reconfiguration; the rApp determining to generate O1 configuration data for preparing and performing RF channel reconfiguration; the rApp sending the O1 configuration data for preparing and performing RF channel reconfiguration to at least one E2 node via an R1 interface through an NRT-RIC framework and via an O1 interface through at least one SMO function within the SMO framework; and the E2 node and the O-RU implementing RF channel reconfiguration within the O-RAN.Implementing includes the E2 node converting O1 configuration data for preparing and executing RF channel reconfiguration, and the E2 node instructing the O-RU to execute RF channel reconfiguration via the open FH M-Plane.

[0019] Retraining at least one AI / ML model includes the rApp selecting one AI / ML model from a plurality of AI / ML models, the rApp sending a start request for retraining the AI / ML model to the NRT-RIC framework, the NRT-RIC framework retraining the AI / ML model, the rApp monitoring the retrained AI / ML model parameters and determining the retrieval of the retrained AI / ML model from the NRT-RIC framework based on the retrained AI / ML model parameters, the rApp requesting the retrained AI / ML model from the NRT-RIC framework, and the NRT-RIC framework sending the retrained AI / ML model to the rApp.

[0020] Retraining at least one AI / ML model includes the rApp retraining one AI / ML model from a plurality of AI / ML models.

[0021] The O1-related data that provides the O1 configuration necessary to perform RF channel reconfiguration 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 energy efficiency / energy consumption (EE / EC) measurement reports. The energy efficiency / energy consumption (EE / EC) measurement reports include reference signal received quality (RSRQ) measurement results for each synchronization signal block (SSB) per cell, reference signal received power (RSRP) measurement results for each SSB per cell, signal-to-interference plus noise ratio (SINR) measurement results for each SSB per cell, energy consumption, power consumed by hardware components, transmission power, load statistics per cell and per carrier (number of active users, average number of radio resource control (RRC) connections, average number of scheduled active users per transmission time interval (TTI), physical resource block (PRB) utilization rate, downlink / uplink (DL / UL) cell / user throughput, precoding matrix indicator / channel state information (PMI / CSI) report, latency statistics per cell, and power consumption metric information on supported Tx / Rx array selection combined with power consumption key performance indicator (KPI), etc.), and may include at least one of these.

[0022] Collecting O1-related data that provides the O1 configuration necessary to perform RF channel reconfiguration is done by the rApp through the NRT-RIC framework via the R1 interface and via the O1 interface through the SMO function within the SMO framework, sending an O1-related data collection request to the E2 node, receiving the O1-related data collection request from the SMO function by the E2 node, collecting by the E2 node O1-related data that provides the O1 configuration necessary to perform RF channel reconfiguration via the open front haul management plane (FH M-Plane) interface between the E2 node and the open radio unit (O-RU) from the O-RU, and sending by the E2 node the O1-related data that provides the O1 configuration necessary to perform RF channel reconfiguration collected via the O1 interface through the SMO function within the SMO framework and the NRT-RIC framework to the rApp via the R1 interface.

[0023] The O1 configuration data for preparing and performing RF channel reconfiguration may include at least one of O-RU Tx / Rx array selection, changing the number of SU / MU MIMO spatial streams or data layers, changing the number of SSB beams, and changing the antenna transmit power of the O-RU.

[0024] The method may further include the NRT-RIC monitoring the performance of the retrained AI / ML model, determining that a predetermined performance goal is not achieved based on the collected O1-related data, and initiating a fallback mechanism and / or updating or retraining the AI / ML model.

[0025] According to an embodiment, in an Open Radio Access Network (O-RAN), a non-transitory computer-readable recording medium having recorded thereon executable instructions executable by at least one processor configured to implement a non-real-time RAN intelligent controller (NRT-RIC), an NRT-RIC framework, at least one SMO function, and an rApp hosted by the NRT-RIC is provided to execute a method for implementing optimization of radio frequency (RF) reconfiguration by a service management and orchestration (SMO) framework.The method includes: O1-related data provided by the rApp for performing RF channel reconfiguration, which is O1-related data collected via the open front-haul management plane (FH M-Plane) interface between the E2 node and the open radio unit (O-RU), collecting the O1-related data from the E2 node via the R1 interface through the NRT-RIC framework and via the O1 interface through the SMO function within the SMO framework; the SMO retraining at least one artificial intelligence / machine learning (AI / ML) model based on the collected O1-related data; the rApp deploying and activating one retrained AI / ML model for inferring data for providing the O1 configuration required to perform RF channel reconfiguration within O-RAN among at least one retrained AI / ML; the rApp monitoring the O1-related data for providing the O1 configuration required to perform RF channel reconfiguration via the R1 interface through the NRT-RIC framework and via the O1 interface through at least one SMO function within the SMO framework; the rApp evaluating the O1-related data for providing the O1 configuration required to perform RF channel reconfiguration; the rApp determining to generate O1 configuration data for preparing and performing RF channel reconfiguration; the rApp sending the O1 configuration data for preparing and performing RF channel reconfiguration 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 within the SMO framework; and the E2 node and the O-RU implementing the RF channel reconfiguration within O-RAN. Implementing includes the E2 node converting the O1 configuration data for preparing and performing RF channel reconfiguration and the E2 node instructing the O-RU to perform RF channel reconfiguration via the open FH M-Plane.

[0026] The retraining of at least one AI / ML model includes: the rApp selects one AI / ML model from multiple AI / ML models; the rApp sends a start request for retraining the AI / ML model to the NRT-RIC framework; the NRT-RIC framework retrains the AI / ML model; the rApp monitors the retrained AI / ML model parameters and determines the recovery of the retrained AI / ML model from the NRT-RIC framework based on the retrained AI / ML model parameters; the rApp requests the retrained AI / ML model from the NRT-RIC framework; and the NRT-RIC framework sends the retrained AI / ML model to the rApp.

[0027] The retraining of at least one AI / ML model includes the rApp retraining one AI / ML model from multiple AI / ML models.

[0028] The O1-related data that provides the O1 configuration necessary to perform RF channel reconfiguration may include at least one of configuration, performance indicators, and measurement reports provided by the O-RU. The measurement report may include at least one of energy efficiency / energy consumption (EE / EC) measurement reports. The energy efficiency / energy consumption (EE / EC) measurement report includes, for each cell, for each synchronization signal block (SSB), reference signal received quality (RSRQ) measurement results, for each cell, for each SSB, reference signal received power (RSRP) measurement results, for each cell, for each SSB, signal-to-interference plus noise ratio (SINR) measurement results, energy consumption, power consumed by hardware components, transmit power, load statistics for each cell and each carrier (number of active users, average number of radio resource control (RRC) connections, average number of scheduled active users per transmission time interval (TTI), physical resource block (PRB) utilization rate, downlink / uplink (DL / UL) cell / user throughput, precoding matrix indicator / channel state information (PMI / CSI) report, latency statistics for each cell, and power consumption metric information on supported Tx / Rx array selection combined with power consumption key performance indicator (KPI), etc.), and may include at least one of them.

[0029] Collecting O1-related data that provides the O1 configuration necessary to perform RF channel reconfiguration is done by the rApp through the NRT-RIC framework via the R1 interface and via the O1 interface through the SMO function within the SMO framework, sending an O1-related data collection request to the E2 node, receiving the O1-related data collection request by the E2 node from the SMO function, collecting by the E2 node, via the open front-haul management plane (FH M-Plane) interface between the E2 node and the open radio unit (O-RU), the O1-related data that provides the O1 configuration necessary to perform RF channel reconfiguration from the O-RU, and sending by the E2 node, via the R1 interface, to the rApp the O1-related data that provides the O1 configuration necessary to perform RF channel reconfiguration collected via the O1 interface through the SMO function within the SMO framework and the NRT-RIC framework.

[0030] The O1 configuration data for preparing and performing RF channel reconfiguration may include at least one of O-RU Tx / Rx array selection, changing the number of SU / MU MIMO spatial streams or data layers, changing the number of SSB beams, and changing the antenna transmit power of the O-RU.

[0031] The method may further include the NRT-RIC monitoring the performance of the retrained AI / ML model, determining that a predetermined performance goal is not achieved based on the collected O1-related data, and initiating a fallback mechanism and / or an update or retraining of the AI / ML model.

[0032] Additional aspects may be partially presented in the following description, partially apparent from the description, or realized by the practice of the disclosed embodiments.

Brief Description of the Drawings

[0033] The 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.

[0034] FIG. 1 illustrates an O-RAN architecture in the related art.

[0035] FIG. 2 is a diagram of an example environment in which the systems and / or methods described herein may be implemented.

[0036] FIG. 3 is a diagram of an example of components of a device according to one embodiment.

[0037] FIG. 4 illustrates an NRT-RIC framework within an O-RAN according to one embodiment.

[0038] FIG. 5 is a flowchart of a method for implementing optimization of RF reconfiguration according to one embodiment.

[0039] FIG. 6 illustrates a data collection flow according to one embodiment.

[0040] FIG. 7 illustrates data analysis, training, and inference flows of an AI / ML model according to one embodiment.

[0041] FIG. 8 illustrates data analysis, training, and inference flows of an AI / ML model according to other embodiments.

[0042] FIG. 9 illustrates the generation and implementation of O1 configuration data for preparing and performing RF channel reconfiguration in an m-MIMO antenna in an O-RU according to one embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0043] The following detailed description of the embodiments refers to the accompanying drawings. The foregoing disclosure provides illustration and description, but is not intended to be exhaustive or to limit the implementation to the exact forms disclosed. Changes and modifications are possible in light of the foregoing disclosure or may be obtained from practice of the implementation. Further, one or more features or components of one embodiment may be integrated with or combined with those of other embodiments (or one or more features of other embodiments). Additionally, in the flowcharts and operation descriptions provided below, one or more operations may be omitted, one or more operations may be added, one or more operations may be executed simultaneously (at least in part), and the order of one or more operations may be interchanged.

[0044] It will be apparent that the systems and / or methods described herein may be implemented in different forms of hardware, firmware, or a combination of hardware and software. The actual special control hardware or software code used to implement these systems and / or methods is not a limitation of the implementation. For this reason, the operations and behavior of the systems and / or methods are described herein without reference to specific software code. It is understood that software and hardware may be designed based on the description herein to implement the systems and / or methods.

[0045] Even if particular combinations of features 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 different manners than specifically recited in the claims and / or specifically disclosed in the specification. Each of the dependent claims listed below may depend directly on only one claim, but the disclosure of possible implementations includes each dependent claim in combination with all other claims in the claim group.

[0046] None of the elements, acts, or instructions used herein should be construed as important or essential unless explicitly described. Also, as used herein, the articles "a" and "an" are intended to include one or more items and may be used interchangeably with "one or more." When only one item is intended, the term "one" or a similar term is used. Also, as used herein, terms such as "has," "have," "having," "include," "including," etc. are intended to be open-ended terms. Further, the phrase "based on" is intended to mean "at least in part, based on" unless explicitly stated otherwise. Further, expressions such as "at least one of A and B" or "at least one of A or B" are understood to include only A, only B, or both A and B.

[0047] Embodiments of the present disclosure provide a system and method in which, for example, an A1 policy or optimization trigger on an O1 interface for nRT-RIC defined by an NRT-RIC (i.e., by at least one rApp hosted by the NRT-RIC and / or by an NRT-RIC framework assisted by a machine learning (ML) technique) causes the NRT-RIC framework and / or rApp to configure RF channel reconfiguration parameters (e.g., for switching on / off a Tx / Rx array of an m-MIMO antenna in an O-RU) that enable flexible RF channel reconfiguration. An action of the nRT-RIC via the E2 interface may activate the deployment of O1 configuration data to prepare and execute RF channel reconfiguration for one or more E2 nodes. The implementation based on the O1 configuration data for preparing and executing cell and RF channel reconfiguration in the O-RU is initiated by the E2 node via an open FH M-Plane interface between the E2 node and the O-RU.

[0048] For this purpose, before applying RF channel reconfiguration (e.g., switching the on / off of the Tx / Rx array), the E2 node may need to perform preparatory actions for RF channel reconfiguration. For example, the E2 node may check load statistics per cell and per carrier (the number of active users, the average number of radio resource control (RRC) connections, the average number of scheduled active users per transmission time interval (TTI), the physical resource block (PRB) utilization rate, downlink / uplink (DL / UL) cell / user throughput, precoding matrix indicator / channel state information (PMI / CSI) reports, etc.). Further, the E2 node may check latency statistics per cell (e.g., when an ultra-reliable low-latency communication (URLLC) slice is involved, latency is used for the definition of energy efficiency (EE)).

[0049] Figure 1 illustrates an O-RAN architecture in the related art. Referring to Figure 1, the RAN functions in the O-RAN architecture are controlled and optimized by the RIC. The RIC is a software-defined component that implements modular applications for realizing the necessary multi-vendor operability in the O-RAN system and automating and optimizing RAN operations. The RIC is divided into two types: non-real-time RIC (NRT-RIC) and near-real-time RIC (nRT-RIC).

[0050] The NRT-RIC is a control point for non-real-time control loops and operates on a time scale longer than one second within the Service Management and Orchestration (SMO) framework. Its functions are implemented through modular applications called rApps (rApp 1, …, rApp N), and include providing policy-based guidance and enrichment across the A1 interface, which is an interface enabling communication between the NRT-RIC and nRT-RIC, performing data analytics, AI / Machine Learning (AI / ML) training and inference for RAN optimization, and / or recommending configuration management actions on the O1 interface, which is an interface connecting the SMO to RAN management elements (e.g., nRT-RIC, O-RAN Centralized Unit (O-CU), O-RAN Distributed Unit (O-DU), etc.).

[0051] The nRT-RIC operates on time scales between 10 milliseconds and 1 second and connects via the E2 interface to the O-DU, O-CU (which is decomposed into an O-CU control plane (O-CU-CP) and an O-CU user plane (O-CU-UP)), and the open evolved NodeB (O-eNB). The nRT-RIC uses the E2 interface to control the underlying RAN elements (E2 nodes / network functions (NFs)) on a near-real-time control loop. The nRT-RIC monitors, suspends / stops, overrides, and controls the E2 nodes (O-CU, O-DU, and O-eNB) via policies. For example, the nRT-RIC sets policy parameters on the functions activated in the E2 nodes. Further, the nRT-RIC hosts xApps for implementing functions such as quality of service (QoS) optimization, mobility optimization, slicing optimization, interference mitigation, load balancing, and security. Two types of RICs cooperate to optimize O-RAN. For example, the NRT-RIC provides the policies, data, and artificial intelligence / machine learning (AI / ML) models enabled and used by the nRT-RIC for RAN optimization on the A1 interface, and the nRT-RIC returns policy feedback (i.e., how the policies set by the NRT-RIC work).

[0052] The SMO framework in which the NRT-RIC is located manages and coordinates the RAN elements. Specifically, the SMO manages and coordinates what is represented as the O-RAN cloud (O-Cloud). The O-Cloud is a collection of physical RAN nodes that host the RIC, O-CU, and O-DU, support software components (e.g., operating systems and runtime environments), and the SMO itself. In other words, the SMO manages the O-Cloud from within. The O2 interface is the interface between the SMO and the O-Cloud in which it resides. The SMO provides infrastructure management services (IMS) and deployment management services (DMS) via the O2 interface.

[0053] On the one hand, O-Cloud is a cloud computing platform comprising a set of physical infrastructure nodes that meet O-RAN specifications for hosting related O-RAN functions (e.g., nRT-RIC, O-CU-CP, O-CU-UP, O-DU, etc.), support software components (operating systems, virtual machine monitors, container runtimes, etc.), and appropriate management and orchestration functions.

[0054] The SMO framework where the NRT-RIC is located manages and coordinates RAN elements. SMO performs the management and orchestration of RAN elements through four key interfaces (the A1 interface for RAN optimization between the NRT-RIC and nRT-RIC in SMO; the O1 interface for FCAPS support between SMO and O-RAN network functions; in the case of the hybrid model, the open front-haul M plane interface for FCAPS support between SMO and O-RU; the O2 interface for platform resource and workload management between SMO and O-Cloud).

[0055] FIG. 2 is a diagram of an example of an environment 200 in which the systems and / or methods described herein may be implemented. As shown in FIG. 2, the environment 200 may include user devices 210, a platform 220, and a network 230. The devices of the environment 200 may be interconnected via wired connections, wireless connections, or a combination of wired and wireless connections. In embodiments, any of the functions and operations described above with reference to FIG. 1 may be performed by any combination of the elements illustrated in FIG. 2.

[0056] The user device 210 includes one or more devices capable of receiving, generating, storing, processing, and / or providing information related to the platform 220. For example, the user device 210 may include a computing device (e.g., a desktop computer, a laptop computer, a tablet computer, a handheld computer, a smart speaker, a server, etc.), a mobile phone (e.g., a smartphone, a wireless phone, etc.), a wearable device (e.g., smart glasses or a smartwatch), or a similar device. In some implementations, the user device 210 may receive information from the platform 220 and / or transmit information to the platform 220.

[0057] The platform 220 includes one or more devices capable of receiving, generating, storing, processing, and / or providing information. In some implementations, the platform 220 may include a cloud server or a group of cloud servers. In some implementations, the platform 220 may be designed to be modular such that specific software components may be swapped (in or out) according to specific needs. Thus, the platform 220 may be easily and / or quickly reconfigured for different uses.

[0058] In some implementations, as shown, the platform 220 may be hosted in a cloud computing environment 222. Note that the implementations described herein describe the platform 220 as being hosted in the cloud computing environment 222, but in some implementations, the platform 220 may not be cloud-based (i.e., may be implemented outside of a cloud computing environment) or may be partially cloud-based.

[0059] The cloud computing environment 222 includes an environment that hosts the platform 220. The cloud computing environment 222 may provide services that do not require knowledge of the physical location and configuration of the end user (e.g., user device 210) of the system and / or device that hosts the platform 220, such as computing, software, data access, storage, etc. As shown, the cloud computing environment 222 may include a group of computing resources 224 (collectively referred to as "computing resources 224" and individually referred to as "computing resource 224").

[0060] The computing resources 224 include one or more personal computers, clusters of computing devices, workstation computers, server devices, or other types of computing and / or communication devices. In some implementations, the computing resources 224 may host the platform 220. The cloud resources may include computing instances running on the computing resources 224, storage devices provided on the computing resources 224, data transfer devices provided by the computing resources 224, etc. In some implementations, the computing resources 224 may communicate with other computing resources 224 via a wired connection, a wireless connection, or a combination of wired and wireless connections.

[0061] As further shown in FIG. 2, the computing resources 224 include a group of cloud resources such as one or more applications ("APP") 224-1, one or more virtual machines ("VM") 224-2, virtualized storage ("VS") 224-3, one or more hypervisors ("HYP") 224-4, etc.

[0062] Application 224-1 includes one or more software applications that may be provided to user device 210 or accessed by user device 210. Application 224-1 may eliminate the need to install and execute software applications on user device 210. For example, Application 224-1 may include software associated with platform 220 and / or any other software that may be provided via cloud computing environment 222. In some implementations, one Application 224-1 may communicate information with one or more other Applications 224-1 via virtual machine 224-2.

[0063] 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 degree of use by virtual machine 224-2 and correspondence with any real 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 and 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-duration data transfer.

[0064] The virtualized storage 224-3 includes one or more storage systems and / or devices of one or more devices or computing resources 224 that use virtualization technology within the storage system. In some implementations, within the context of the storage system, the types of virtualization may include block virtualization and file virtualization. Block virtualization may represent the abstraction (or separation) of logical storage from physical storage so that the storage system may be accessed without considering the physical storage or heterogeneous structure. The separation may provide flexibility to the storage system administrator when managing storage for end users. File virtualization may remove the dependency between the data accessed at the file level and the location where the files are physically stored. This may enable optimization of storage usage, server consolidation, and / or performance of non-disruptive file migration.

[0065] The hypervisor 224-4 may provide hardware virtualization technology that enables multiple operating systems (e.g., "guest operating systems") to run concurrently on a host computer such as computing resources 224. The hypervisor 224-4 may present a virtual operating platform to the guest operating systems and may manage the execution of the guest operating systems. Multiple instances of various operating systems may share the virtualized hardware resources.

[0066] Network 230 includes one or more wired and / or wireless networks. For example, Network 230 may include a cellular network (e.g., a fifth-generation (5G) network, a long-term evolution (LTE) network, a third-generation (3G) network, a code division multiple access (CDMA) network, etc.), a public land mobile network (PLMN), a local area network (LAN), a wide area network (WAN), a metropolitan area network (MAN), a telephone network (e.g., a Public Switched Telephone Network (PSTN), a private network, an ad hoc network, an intranet, the Internet, an optical fiber-based network, etc.), and / or combinations of these or other types of networks.

[0067] The number and arrangement of devices and networks shown in FIG. 2 are provided as an example. In fact, there may be additional devices and / or networks, fewer devices and / or networks, different devices and / or networks, or devices and / or networks in a different arrangement than those shown in FIG. 2. Furthermore, two or more devices shown in FIG. 2 may be implemented within a single device, and a single device shown in FIG. 2 may be implemented as 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 described as being performed by another set of devices in Environment 200.

[0068] Figure 3 is a diagram of an example component of device 300. Device 300 may correspond to user device 210 and / or platform 220. As shown in Figure 3, device 300 may include bus 310, processor 320, memory 330, storage component 340, input component 350, output component 360, and communication interface 370.

[0069] Bus 310 includes components that enable communication among the components of device 300. Processor 320 may be implemented in hardware, firmware, or a combination of hardware and software. Processor 320 may be a central processing unit (CPU), a graphics processing unit (GPU), an 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, processor 320 includes one or more programmable processors for executing functions. Memory 330 includes random access memory (RAM), read-only memory (ROM), and / or other types of dynamic or static storage devices (e.g., flash memory, magnetic memory, and / or optical memory) for storing information and / or instructions for use by processor 320.

[0070] The storage component 340 stores information and / or software related to the operation and use of the device 300. For example, the storage component 340, together with the corresponding drive, may include a hard disk (e.g., magnetic disk, optical disk, magneto-optical disk, and / or solid-state disk), a compact disc (CD), a digital versatile disc (DVD), a floppy disk, a cartridge, a magnetic tape, and / or other types of non-transitory computer-readable media. The input component 350 includes components that enable the device 300 to receive information via user input (e.g., touch screen display, keyboard, keypad, mouse, button, switch, and / or microphone), etc. Additionally or alternatively, the input component 350 may include sensors (e.g., global positioning system (GPS) component, accelerometer, gyroscope, and / or actuator) for measuring information. The output component 360 includes components that provide output information from the device 300 (e.g., display, speaker, and / or one or more light-emitting diodes (LEDs)).

[0071] The communication interface 370 includes components such as a transceiver (e.g., transceiver and / or separate receiver and transmitter) that enable the device 300 to communicate with other devices via a wired connection, a wireless connection, or a combination of wired and wireless connections, etc. The communication interface 370 enables the device 300 to receive information from other devices and / or provide information to other devices. For example, the communication interface 370 may include an Ethernet interface, an optical interface, a coaxial interface, an infrared interface, a radio frequency (RF) interface, a universal serial bus (USB) interface, a Wi-Fi interface, a cellular network interface, etc.

[0072] Device 300 may execute one or more of the processes described herein. Device 300 may execute these processes in response to a processor 320 that executes software instructions stored by a non-transitory computer-readable medium such as a memory 330 and / or a storage component 340. The computer-readable medium is defined herein as a non-transitory memory device. The memory device includes a memory space within a single physical storage device or a memory space distributed across multiple physical storage devices.

[0073] The software instructions may be read into the memory 330 and / or the storage component 340 from another computer-readable medium or from another device via a communication interface 370. When executed, the software instructions stored in the memory 330 and / or the storage component 340 may cause the processor 320 to execute one or more of the processes described herein.

[0074] In addition or alternatively, a wired circuit may be used instead of or in combination with the software instructions to execute one or more of the processes described herein. Thus, the implementations described herein are not limited to a particular combination of hardware circuitry and software. The number and arrangement of components shown in FIG. 3 are provided as an example. In fact, device 300 may include additional components, fewer components, different components, or components arranged differently than those shown in FIG. 3. In addition or alternatively, a set of components (e.g., one or more components) of device 300 may perform one or more functions described as being performed by another set of components of device 300.

[0075] In an embodiment, any operation or process of FIGS. 4, 5, 6, 7, and 8 may be implemented by or using any element illustrated in FIGS. 1, 2, and 3. Other embodiments are not limited thereto and may be implemented in various different architectures (e.g., bare metal architecture, any cloud-based architecture, or deployment architectures such as Kubernetes, Docker, OpenStack, etc.).

[0076] FIG. 4 illustrates an NRT-RIC framework (or platform) within an SMO framework system architecture and an rApp for an R1 interface hosted by the NRT-RIC, and O1, O2, and A1 interfaces within O-RAN, according to one embodiment.

[0077] Referring to FIG. 4, the NRT-RIC represents a subset of the functions of the SMO framework. The NRT-RIC can access other SMO framework functions and can affect (i.e., control and / or execute) what is executed across the O1 and O2 interfaces (e.g., perform configuration management (CM) and / or performance management (PM)).

[0078] Generally, FCAPS management, software management, and file management are achieved by an O1 interface for operation and management between a management entity (network management system (NMS) / element management system (EMS) / management and orchestration of network function virtualization (MANO)) and an O-RAN management element.

[0079] The SMO framework system architecture includes SMO functions including O1 termination that enables communication between the SMO framework and an E2 node (i.e., O-CU, O-DU, etc.) via the O1 interface.

[0080] The NRT-RIC includes the NRT-RIC framework. The NRT-RIC framework includes an R1 service exposer function that handles the R1 services provided according to the embodiments, in addition to a plurality of other functions. Generally, the NRT-RIC functions within the NRT-RIC framework support, for the rApp, authentication, authorization, registration, discovery, communication support, etc.

[0081] Generally, the R1 services may include a set of services including, but not limited to, service registration and discovery services, authentication and authorization services, AI / ML workflow services, and A1, O1, and O2-interface related services.

[0082] The NRT-RIC application (rApp) is an application that utilizes the functions available in the NRT-RIC framework and / or the SMO framework to provide value-added services related to RAN operation and optimization. The scope of the rApp includes, but is not limited to, wireless resource management, data analytics, etc., and enrichment of information. Generally, the rApp represents an application designed to consume and / or generate R1 services.

[0083] For this purpose, the NRT-RIC framework generates and / or consumes the R1 services according to the embodiments via the R1 interface. The R1 interface terminates at the R1 termination of the NRT-RIC framework. The R1 termination enables the NRT-RIC framework and the rApp to exchange messages / data (i.e., requests and responses with a data model) for connecting to the NRT-RIC framework and the rApp via the R1 interface and accessing the R1 services.

[0084] Generally, the R1 interface is defined as an interface between the rApp and the NRT-RIC framework where R1 services can be generated and consumed.

[0085] Furthermore, the NRT-RIC framework comprises A1-related functions. The A1-related functions of the NRT-RIC framework support, for example, A1 logical termination, A1 policy coordination and catalog, A1-EI coordination and catalog, etc.

[0086] The data management and exposure services within the NRT-RIC framework deliver data generated or collected by the data generator to the data consumer according to needs (e.g., function management (FM) / consumption management (CM) / production management (PM) data for the rApp or CM changes from the rApp to O-RAN via the O1 interface).

[0087] The NRT-RIC framework further comprises external termination. The external termination supports, for example, the exchange of data between the NRT-RIC framework and external AI / ML functions, enrichment information (EI) sources, or external oversight.

[0088] Within the NRT-RIC framework, the AI / ML workflow service provides access to the AI / ML workflow. For example, the AI / ML workflow service may assist in model training, monitoring of the AI / ML models deployed in the NRT-RIC, etc.

[0089] Furthermore, the NRT-RIC framework comprises A2-related functions that support, for example, A2 logical termination, A2 policy coordination and catalog, etc.

[0090] Still referring to FIG. 4, within the NRT-RIC, the R1 interface is an open logical interface within the O-RAN architecture between the rApp and the NRT-RIC framework of the NRT-RIC. The R1 interface supports the exchange of control signaling information and the delivery between data collection and endpoints. The R1 interface enables, for example, multi-vendor rApps to consume and / or generate R1 services.

[0091] The R1 interface is independent of a particular implementation of the SMO and the NRT-RIC framework of the NRT-RIC. The R1 interface is defined in an extensible way that allows new services and data types to be added without the need to change the protocol or procedure.

[0092] In particular, the R1 interface enables the interconnection between rApps and NRT-RIC frameworks supplied by different vendors (i.e., enables interconnection in a multi-vendor environment). For this purpose, the R1 interface provides a level of abstraction between the rApp and the NRT-RIC framework and / or the SMO framework.

[0093] Referring to FIG. 4, for example, the NRT-RIC framework (e.g., at least one rApp hosted by the NRT-RIC and / or the NRT-RIC framework) enables flexible RF channel reconfiguration (e.g., to switch on / off the Tx / Rx array in the m-MIMO antenna in the O-RU) by an A1 policy or an optimization trigger on the O1 interface for the nRT-RIC defined by the NRT-RIC (i.e., by the NRT-RIC framework supported by at least one rApp hosted by the NRT-RIC and / or machine learning (ML) technology). Actions of the nRT-RIC via the E2 interface may activate the deployment of RF channel reconfiguration parameters for one or more E2 nodes.

[0094] Referring to FIG. 4, the SMO framework function is configured to collect configuration, performance indicators, and measurement reports (e.g., cell load-related information and traffic information, energy efficiency (EE) measurement reports, energy consumption (EC) measurement reports, etc.) from the E2 node and the O-RU (via the E2 node) for the purpose of decision-making. The decision-making may be based on, for example, the training and inference of AI / ML models that support such energy efficiency (EE) and / or energy consumption (EC) functions.

[0095] 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.

[0096] Furthermore, the SMO framework function is configured to transfer the collected data to the NRT-RIC framework and signal (i.e., send) the execution of updated RF channel reconfiguration parameters and optimization actions to the E2 node via the O1 interface.

[0097] The 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) measurement reports and / or energy consumption (EC) measurement reports, etc.) for the purpose of decision-making. The decision-making may be based on, for example, the training and inference of AI / ML models that support such energy efficiency (EE) and / or energy savings (ES) functions.

[0098] Furthermore, the NRT-RIC framework is configured to transfer the collected data (i.e., O1-related data for RF channel reconfiguration parameters (e.g., for switching on / off the Tx / Rx arrays in the m-MIMO antennas in the O-RU)) to the rApp and signal (i.e., send) the configuration for updated EE / ES optimization to the E2 node (i.e., O-CU, O-DU, etc.) through the SMO framework function.

[0099] In one embodiment, the NRT-RIC framework may be configured to retrain, update, and configure the EE / ES AI / ML models in the NRT-RIC.

[0100] Still referring to FIG. 4, one or more rApps hosted by the NRT-RIC are configured to retrieve the necessary configuration, performance indicators, and measurement reports, etc. for the training and execution of the associated AI / ML models (e.g., EE / ES AI / ML models) from the E2 node and the O-RU (via the E2 node transferred by the SMO) (e.g., may be equipped with R1 / O1 consumer and / or production services).

[0101] Furthermore, one or more rApps hosted by the NRT-RIC are configured to infer the O1 configuration optimized for EE / ES through the R1 / O1 interface (e.g., may be equipped with R1 / O1 consumer and / or production services).

[0102] In one embodiment, the rApp may be configured to retrain, update, and configure the EE / ES AI / ML model.

[0103] For this purpose, one or more E2 nodes (i.e., O-DU, O-CU, etc.) in FIG. 1 are configured to report, for example, cell configuration, performance indicators, measurement reports (e.g., cell load-related information, traffic information, EE / EC measurement reports, etc.) to the SMO via the O1 interface. SMO framework functions such as O1 termination enable the SMO to communicate with the E2 nodes.

[0104] Furthermore, one or more E2 nodes (i.e., O-DU, O-CU, etc.) in FIG. 1 are configured to perform RF channel reconfiguration (e.g., O-RU Tx / Rx array selection, change in the number of SSB beams, change in the antenna transmission power of the O-RU, change in the number of single-user / multi-user (SU / MU) MIMO data layers or spatial streams) as part of EE / ES optimization.

[0105] In one embodiment, the O-RU in FIG. 1 is configured to report energy consumption (EC) and energy efficiency (EE) related information (e.g., cell load-related information, traffic information, EE / EC measurement reports, etc.) to the E2 node (i.e., O-DU) via the open FH M-Plane interface.

[0106] In one embodiment, one or more O-RUs in FIG. 1 may be configured to directly report energy consumption (EC) and energy efficiency (EE) related information (e.g., cell load-related information, traffic information, EE / EC measurement reports, etc.) to the SMO / NRT-RIC.

[0107] Hereinafter, decision-making including potentially training and inference of AI / ML models is performed at the NRT-RIC.

[0108] FIG. 5 is a flowchart of a method for implementing optimization of RF channel reconfiguration in an m-MIMO antenna of an O-RU according to one embodiment.

[0109] Referring to FIG. 5, a method for optimizing RF channel reconfiguration in an m-MIMO antenna in an O-RU is implemented by a non-real-time RAN intelligent controller (NRT-RIC), an NRT-RIC framework, at least one SMO function (e.g., O1 termination), and a service management and orchestration (SMO) framework hosted by the NRT-RIC. The SMO framework may function as an intermediary between the rApp and the E2 node and the O-RU (via the E2 node) within the O-RAN.

[0110] In step 501, the rApp collects O1-related data that provides the O1 configuration necessary to perform RF channel reconfiguration from an E2 node (i.e., O-CU, O-DU, etc.) via the R1 interface through the NRT-RIC framework and via the O1 interface through an SMO function within the SMO framework (i.e., the SMO function configured as the O1 termination of the O1 interface in the SMO). The O1-related data is collected via the open front-haul management plane (FH M-Plane) interface between the E2 node and the open radio unit (O-RU).

[0111] In one embodiment, collecting O1-related data that provides the O1 configuration necessary to perform RF channel reconfiguration may include the rApp sending an O1-related data collection request to the E2 node via the R1 interface through the NRT-RIC framework and via the O1 interface (i.e., O1 termination) through an SMO function within the SMO framework. The E2 node may receive the O1-related data collection request from the SMO function and collect the O1-related data that provides the O1 configuration necessary to perform RF channel reconfiguration from the O-RU via the open front-haul management plane (FH M-Plane) interface between the E2 node and the open radio unit (O-RU).

[0112] In one embodiment, regarding the collection of O1-related data that provides the O1 configuration necessary to perform RF channel reconfiguration, the E2 node may activate a measurement report (i.e., an EE / EC measurement report) to the O-RU, and the O-RU provides measurement data (i.e., input data) for the measurement report.

[0113] When collecting O1-related data from the O-RU, the E2 node may send, via the R1 interface to the rApp, the O1-related data that provides the O1 configuration necessary to perform RF channel reconfiguration collected via the O1 interface through the SMO function and the NRT-RIC framework within the SMO framework.

[0114] In step 502, the SMO retrains at least one artificial intelligence / machine learning (AI / ML) model based on the collected O1-related data, and the rApp deploys and activates one retrained AI / ML model for inferring data that provides the O1 configuration necessary to perform RF channel reconfiguration within the O-RAN among at least one of the retrained AI / MLs.

[0115] In one embodiment, the O1-related data that provides the O1 configuration necessary to perform RF channel reconfiguration may be input data (e.g., measurement data) used in the training and inference of the AI / ML model.

[0116] The O1-related data (i.e., input data) that provides the O1 configuration necessary to perform RF channel reconfiguration may include the following measurement data for monitoring the energy consumption and energy efficiency (EC / EE) of one or more E2 nodes and one or more O-RUs, in addition to other O1-related data: 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, per Xn-U interface, per X2-U interface; Uplink packet data convergence protocol service data unit (UP PDCP SDU) data volume (data volume in UL delivered from O-CU-UP to O-DU) per interface, per PLMN, per QoS level, per slice, per F1-U interface, per Xn-U interface, per X2-U interface; 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, transmission power, load statistics per cell and per carrier (number of active users, average number of radio resource control (RRC) connections, average number of scheduled active users per transmission time interval (TTI), physical resource block (PRB) utilization rate, downlink / uplink (DL / UL) cell / user throughput, precoding matrix indicator / channel state information (PMI / CSI) report, latency statistics per cell (e.g., when the ultra-reliable low-latency communication (URLLC) slice is involved, latency is used for the definition of energy efficiency (EE)) and power consumption (i.e., specific EEPower consumption metrics (i.e., average total / carrier power consumption, average total / carrier transmit power, etc.) on the supported Tx / Rx array selection in conjunction with the site / O-RU input power required for KPI (KPIs).

[0117] In one embodiment, the NRT-RIC framework may retrain at least one AI / ML model. According to this embodiment, the rApp selects one AI / ML model from a plurality of AI / ML models and sends a start request for retraining the AI / ML model to the NRT-RIC framework. The NRT-RIC framework retrains the AI / ML model. At this time, the rApp monitors the retrained AI / ML model parameters and determines the recovery of the retrained AI / ML model from the NRT-RIC framework based on the retrained AI / ML model parameters. The rApp requests the retrained AI / ML model from the NRT-RIC framework based on the determination of the recovery. When receiving the request, the NRT-RIC framework sends the retrained AI / ML model to the rApp.

[0118] In other embodiments, the rApp hosts a plurality of AI / ML models and retrains one of the plurality of AI / ML models.

[0119] Still referring to FIG. 5, in step 503, the rApp monitors the O1-related data that provides the O1 configuration required to perform RF channel reconfiguration through the R1 interface through the NRT-RIC framework and through the O1 interface through the SMO function in the SMO framework.

[0120] 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.

[0121] Furthermore, in one embodiment, the rApp monitors performance and energy consumption parameters for the evaluation of the O1 configuration required to perform RF channel reconfiguration. These performance and energy consumption parameters may include configuration, performance indicators, measurement reports (e.g., cell load related information, traffic information, EE / EC measurement reports, etc.).

[0122] For example, the O1-related data (i.e., input data) providing the O1 configuration required to perform RF channel reconfiguration may include at least one of configuration, performance indicators, and measurement reports provided by the O-RU. The measurement report may include at least one of cell load related information, measurement reports, traffic information measurement reports, energy efficiency / energy consumption (EE / EC) measurement reports. The energy efficiency / energy consumption (EE / EC) measurement report may include at least one of the energy consumption of the E2 node, the energy consumption of the O-RU, and one or more performance related KPIs of the E2 node.

[0123] In step 504, the rApp evaluates the O1-related data providing the O1 configuration required to perform RF channel reconfiguration and determines to generate O1 configuration data for preparing and performing RF channel reconfiguration.

[0124] Furthermore, in step 504, when generating the O1 configuration data for preparing and performing RF channel reconfiguration, the rApp sends the O1 configuration data for preparing and performing RF channel reconfiguration to the E2 node via the R1 interface through the NRT-RIC framework and via the O1 interface through the SMO function.

[0125] In one embodiment, if a predetermined performance target (e.g., EE / ES performance target) is not achieved based on the O1-related data (i.e., input data) that provides the O1 configuration required to perform RF channel reconfiguration, the rApp determines to generate the O1 configuration data (i.e., output data) for preparing and performing the RF channel reconfiguration. In this case, the rApp generates the O1 configuration data for preparing and performing the RF channel reconfiguration.

[0126] For example, the EE / ES performance target may be an A1 policy in the NRT-RIC or a target set by the network operator for the energy saving (ES) function in the NRT-RIC (i.e., a predetermined performance parameter for EE / ES in the O-RAN, e.g., one or more predetermined performance targets for EE / EC in the O-RAN).

[0127] In one embodiment, the O1 configuration data (i.e., output data) generated for preparing and performing the RF channel reconfiguration may include, for example, 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 an RF channel reconfiguration that saves energy.

[0128] For example, RF channel reconfiguration may include O-RU Tx / Rx array selection, changing the number of SU / MU MIMO spatial streams or data layers, changing the number of SSB beams, changing the antenna transmission power of the O-RU, and the like.

[0129] In step 505, when the E2 node receives the O1 configuration data for preparing and executing RF channel reconfiguration at the E2 node, it converts the O1 configuration data for preparing and executing RF channel reconfiguration and instructs the O-RU to execute RF channel reconfiguration via the open FH M-Plane.

[0130] In one embodiment, the implementation of the O1 configuration data for preparing and executing RF channel reconfiguration may further include the O-RU notifying the E2 node of the completion of the implementation of the RF channel reconfiguration.

[0131] When the E2 node receives the notification from the O-RU, it 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 the open front-haul management plane (FH M-Plane) interface between the E2 node and the O-RU.

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

[0133] FIG. 6 illustrates a data collection flow according to an embodiment. Referring to FIG. 6, data collection aims to enable the RF channel by actions that enable RF channel reconfiguration parameter changes (i.e., implementation of O1 configuration data for preparing and executing RF channel reconfiguration) and AI / ML-based solutions for optimizing the RF channel for EE / ES within the O-RAN controlled by the NRT-RIC.

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

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

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

[0137] To optimize EE / ES within O-RAN, the network operator may set targets (i.e., predetermined performance parameters for EE / ES within O-RAN, e.g., one or more predetermined performance targets for EE / EC within O-RAN) for the energy saving (ES) function in the NRT-RIC. The targets may include ES targets to reduce the power consumption of the O-RU by switching off, for example, 56, 48, 32, 16, 8, etc. of the 64 Tx / Rx arrays of the O-RU in the digital m-MIMO architecture, thereby reducing the number of corresponding spatial layers and synchronization signal blocks (SSBs).

[0138] As a result, a method for optimizing RF channel reconfiguration may start when the network operator activates an optimization rApp with an initial AI / ML model for the RF channel reconfiguration ES function and the E2 node and O-RU are in an operating state.

[0139] In operation 1, the rApp requests the NRT-RIC framework to collect O1-related data such as necessary configurations, performance indicators, measurement data (i.e., input data), etc. via the R1 interface.

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

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

[0142] In Operation 4, when one or more E2 nodes (i.e., O-CU, O-DU, etc.) receive a request from the SMO, they request and collect O1-related data (i.e., input data) from the O-RU via the open FH M-Plane interface.

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

[0144] In Operation 6, the NRT-RIC retrieves O1-related data (i.e., input data) (e.g., for consuming and / or producing R1 services related to EE / ES).

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

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

[0147] In Operation 9, when the NRT-RIC framework receives a retraining request from the rApp, it initiates the 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 an alternative AI / ML workflow within the SMO based on the R1 service.

[0149] In Operation 11, when the NRT-RIC framework receives a retrieval request from the rApp, it transfers 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 an alternative AI / ML workflow within the SMO based on the R1 service.

[0151] In Operation 12, when the retraining of the AI / ML model is hosted by the rApp, the AI / ML model is retrained on the rApp itself.

[0152] In Operation 13, when the retraining of the AI / ML model is executed, at least one AI / ML model (including the retrained AI / ML model) is deployed and activated for inference (i.e., to infer the data required to provide the O1 configuration necessary to perform RF channel reconfiguration within O-RAN).

[0153] Figure 8 illustrates a data analysis, AI / ML model training, and inference flow according to one embodiment. Referring to Figure 8, the data analysis, AI / ML training, and inference may be performed by the rApp.

[0154] For this purpose, in operation 12, the retraining of the AI / ML model is hosted by the rApp, and the AI / ML model is retrained on top of the rApp itself.

[0155] In operation 13 of FIG. 8, when the retraining of the AI / ML model is executed, at least one AI / ML model (including the retrained AI / ML model) is deployed and activated for inference (i.e., to infer the data required to provide the O1 configuration for performing the on / off switching of cells and / or carriers within O-RAN).

[0156] Referring to FIGS. 7 and 8, in operation 13, the rApp constantly monitors the performance and energy consumption of the E2 node, the energy consumption of the O-RU, etc. For example, the rApp monitors the performance and energy consumption for the evaluation of the O1 configuration (i.e., the input data) required to execute the RF channel reconfiguration.

[0157] In one embodiment, the O1-related data (i.e., the input data) may be the input data used in the training and inference of the AI / ML model. In addition to other O1-related data, the O1-related data may include the following measurement data for monitoring the energy consumption and energy efficiency (EC / EE) of one or more E2 nodes and one or more O-RUs: for each interface, for each public land mobile network (PLMN), for each service quality (QoS) level, for each slice, for the F1-U interface, for the Xn-U interface, for the X2-U interface, the downlink packet data convergence protocol service data unit (DL PDCP SDU) data volume (the data volume in the DL delivered from the O-CU-UP to the O-DU), for each interface, for each public land mobile network (PLMN), for each service quality (QoS) level, for each slice, for the F1-U interface, for the Xn-U interface, for the X2-U interface, the uplink packet data convergence protocol service data unit (UP PDCP SDU) data volume (the data volume in the UL delivered from the O-CU-UP to the O-DU), for each cell for each synchronization signal block (SSB), the reference signal received quality (RSRQ) measurement result, for each cell for each SSB, the reference signal received power (RSRP) measurement result, for each cell for each SSB, the signal-to-interference plus noise ratio (SINR) measurement result, energy consumption, the power consumed by the hardware components, transmission power, load statistics for each cell and each carrier (the number of active users, the average number of radio resource control (RRC) connections, the average number of scheduled active users per transmission time interval (TTI), the physical resource block (PRB) utilization rate, the downlink / uplink (DL / UL) cell / user throughput, the precoding matrix indicator / channel state information (PMI / CSI) report, the latency statistics for each cell (e.g., when the ultra-reliable low-latency communication (URLLC) slice is involved, the latency is used for the definition of energy efficiency (EE)) and power consumption (i.e., a specific EEPower consumption metrics (i.e., average total / carrier power consumption, average total / carrier transmit power, etc.) on the supported Tx / Rx array selection in conjunction with the site / O-RU input power required for KPI (KPIs).

[0158] FIG. 9 illustrates the generation and implementation of an O1 configuration for preparing and executing RF channel reconfiguration according to an embodiment.

[0159] Referring to FIG. 9, in operation 14, the rApp generates an O1 configuration (i.e., output data) for preparing and executing RF channel reconfiguration and sends the O1 configuration (i.e., output data) to the SMO via the R1 interface through the NRT-RIC framework.

[0160] In one example, the generated O1 configuration data (i.e., output data) for preparing and executing RF channel reconfiguration enables cell and carrier shutdown for energy savings and resource reconfiguration, and may include output data such as NRCellCU Information Object Class IOC, NRCellDU IOC, GNBDUFunction IOC, GNBCUCPFunction IOC, GNBCUUPFunction IOC, etc., as defined in, for example, 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.

[0161] Furthermore, the O1 configuration data (i.e., output data) for preparing and executing the RF channel reconfiguration may include output data such as O-RU Tx / Rx array selection, change in the number of SU / MU MIMO spatial streams or data layers, change in the number of SSB beams, change in the antenna transmission power of the O-RU, etc.

[0162] In operation 15, the NRT-RIC requests, through the O1 interface, that the SMO framework function (i.e., the SMO function) configure the E2 node to prepare and execute the RF channel reconfiguration.

[0163] In operation 16, the SMO instructs the E2 node, via the O1 interface, to execute the request received from the rApp.

[0164] In operation 17, the E2 node notifies the O-RU of the updated O-RU configuration (i.e., the RF channel reconfiguration) via the open FH M-Plane. In one embodiment, the O-RU may notify the E2 node when the implementation of the RF channel reconfiguration is completed.

[0165] In operation 18, when the implementation of the RF channel reconfiguration is completed, the E2 node notifies the SMO.

[0166] In operation 19, the SMO framework function (i.e., the SMO function) notifies the NRT-RIC framework of the completion of the RF channel reconfiguration.

[0167] In operation 20, the NRT-RIC notifies the rApp of the completion of the RF channel reconfiguration on the R1 interface.

[0168] In Operation 21, the NRT-RIC continuously analyzes the performance of the AI / ML model. In one example, if the energy savings goal is not achieved, the NRT-RIC may decide to initiate a fallback mechanism and / or an update or retraining of the AI / ML model.

[0169] In one example, a method for optimizing RF channel reconfiguration may end when the E2 node goes into a non-operational state or when the operator disables the AI / ML model for optimization or energy savings (i.e., the AI / ML model for EE / ES).

[0170] In other examples, the rApp continues the closed-loop monitoring of the energy savings function in the E2 node and the O-RU (via the E2 node).

[0171] According to Operation 21, the E2 node and the O-RU operate using the newly deployed parameters (i.e., O1 configuration data) / models (i.e., retrained AI / ML models) and states (i.e., the on / off state of RF channel reconfiguration).

[0172] According to an embodiment, within an Open Radio Access Network (O-RAN), a system for implementing optimization of radio frequency (RF) reconfiguration by a Service Management and Orchestration (SMO) framework is provided. The system includes a memory for storing instructions, and at least one processor configured to implement a non-real-time RAN intelligent controller (NRT-RIC), an NRT-RIC framework, at least one SMO function, and an rApp hosted by the NRT-RIC.At least one processor collects O1-related data for providing an O1 configuration necessary for performing RF channel reconfiguration by an rApp, the O1-related data collected via an open front-haul management plane (FH M-Plane) interface between an E2 node and an open radio unit (O-RU), from the E2 node via an R1 interface through an NRT-RIC framework and via an O1 interface through an SMO function within an SMO framework, re-trains at least one artificial intelligence / machine learning (AI / ML) model by the SMO based on the collected O1-related data, deploys and activates by the rApp one re-trained AI / ML model for inferring data for providing an O1 configuration necessary for performing RF channel reconfiguration within an O-RAN among at least one re-trained AI / ML, monitors by the rApp the O1-related data for providing an O1 configuration necessary for performing RF channel reconfiguration, evaluates by the rApp the O1-related data for providing an O1 configuration necessary for performing RF channel reconfiguration, determines by the rApp to generate O1 configuration data for preparing and performing RF channel reconfiguration, and sends by the rApp the O1 configuration data for preparing and performing RF channel reconfiguration via an R1 interface through an NRT-RIC framework and via an O1 interface through at least one SMO function within an SMO framework to at least one E2 node, and the E2 node and the O-RU are configured to execute instructions to implement an RF channel reconfiguration within the O-RAN. The at least one processor is further configured such that during implementation, the E2 node converts the O1 configuration data for preparing and performing RF channel reconfiguration and instructs the O-RU to perform RF channel reconfiguration via the open FH M-Plane.

[0173] At least one processor may be further configured to, during the retraining of at least one AI / ML model, select, by the rApp, one AI / ML model from a plurality of AI / ML models, send, by the rApp, a start request for retraining 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, the retrieval of 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.

[0174] At least one processor may be further configured to, during the retraining of at least one AI / ML model, select, by the rApp, one AI / ML model from a plurality of AI / ML models for retraining.

[0175] The O1-related data that provides the O1 configuration necessary to perform RF channel reconfiguration 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 energy efficiency / energy consumption (EE / EC) measurement reports. The energy efficiency / energy consumption (EE / EC) measurement reports include reference signal received quality (RSRQ) measurement results for each synchronization signal block (SSB) per cell, reference signal received power (RSRP) measurement results for each SSB per cell, signal-to-interference plus noise ratio (SINR) measurement results for each SSB per cell, energy consumption, power consumed by hardware components, transmission power, load statistics per cell and per carrier (number of active users, average number of radio resource control (RRC) connections, average number of scheduled active users per transmission time interval (TTI), physical resource block (PRB) utilization rate, downlink / uplink (DL / UL) cell / user throughput, precoding matrix indicator / channel state information (PMI / CSI) report, latency statistics per cell, and power consumption metric information on supported Tx / Rx array selection combined with power consumption key performance indicator (KPI), etc.), and may include at least one of them.

[0176] At least one processor, during the collection of O1-related data that provides the O1 configuration necessary to perform RF channel reconfiguration, sends an O1-related data collection request to the E2 node by the rApp, through the NRT-RIC framework via the R1 interface, and via the O1 interface through the SMO function within the SMO framework. The E2 node receives the O1-related data collection request from the SMO function, and the E2 node collects O1-related data that provides the O1 configuration necessary to perform RF channel reconfiguration from the O-RU via the open front-haul management plane (FH M-Plane) interface between the E2 node and the open radio unit (O-RU). The E2 node sends the O1-related data that provides the O1 configuration necessary to perform RF channel reconfiguration, collected via the O1 interface through the SMO function within the SMO framework and the NRT-RIC framework, to the rApp via the R1 interface, and may be configured as such.

[0177] The O1 configuration data for preparing and performing RF channel reconfiguration may include at least one of O-RU Tx / Rx array selection, change in the number of SU / MU MIMO spatial streams or data layers, change in the number of SSB beams, and change in the antenna transmit power of the O-RU.

[0178] At least one processor may be further configured to monitor the performance of the retrained AI / ML model by the NRT-RIC, determine that a predetermined performance target is not achieved based on the collected O1-related data, and initiate a fallback mechanism and / or an update or retraining of the AI / ML model.

[0179] According to an embodiment, a method for implementing optimization of radio frequency (RF) reconfiguration by a service management and orchestration (SMO) framework within an Open Radio Access Network (O-RAN) is provided. The method includes, by an rApp, collecting O1-related data that provides an O1 configuration necessary to perform RF channel reconfiguration, the O1-related data 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; by the SMO, retraining at least one artificial intelligence / machine learning (AI / ML) model based on the collected O1-related data; by the rApp, deploying and activating one retrained AI / ML model for inferring data that provides an O1 configuration necessary to perform RF channel reconfiguration within the O-RAN, from among the at least one retrained AI / ML; by the rApp, monitoring the O1-related data that provides an O1 configuration necessary to perform RF channel reconfiguration, via an R1 interface through an NRT-RIC framework and via an O1 interface through at least one SMO function within the SMO framework; by the rApp, evaluating the O1-related data that provides an O1 configuration necessary to perform RF channel reconfiguration; by the rApp, determining to generate O1 configuration data for preparing and performing RF channel reconfiguration; by the rApp, sending the O1 configuration data for preparing and performing RF channel reconfiguration to at least one E2 node via an R1 interface through an NRT-RIC framework and via an O1 interface through at least one SMO function within the SMO framework; and implementing RF channel reconfiguration within the O-RAN by the E2 node and the O-RU.Implementing includes the E2 node converting O1 configuration data for preparing and executing RF channel reconfiguration, and the E2 node instructing the O-RU to execute RF channel reconfiguration via the open FH M-Plane.

[0180] Retraining at least one AI / ML model includes the rApp selecting one AI / ML model from a plurality of AI / ML models, the rApp sending a start request for retraining the AI / ML model to the NRT-RIC framework, the NRT-RIC framework retraining the AI / ML model, the rApp monitoring the retrained AI / ML model parameters and determining the retrieval of the retrained AI / ML model from the NRT-RIC framework based on the retrained AI / ML model parameters, the rApp requesting the retrained AI / ML model from the NRT-RIC framework, and the NRT-RIC framework sending the retrained AI / ML model to the rApp.

[0181] Retraining at least one AI / ML model includes the rApp retraining one AI / ML model from a plurality of AI / ML models.

[0182] The O1-related data that provides the O1 configuration necessary to perform RF channel reconfiguration 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 energy efficiency / energy consumption (EE / EC) measurement reports. The energy efficiency / energy consumption (EE / EC) measurement reports include reference signal received quality (RSRQ) measurement results for each synchronization signal block (SSB) per cell, reference signal received power (RSRP) measurement results for each SSB per cell, signal-to-interference plus noise ratio (SINR) measurement results for each SSB per cell, energy consumption, power consumed by hardware components, transmit power, load statistics per cell and per carrier (number of active users, average number of radio resource control (RRC) connections, average number of scheduled active users per transmission time interval (TTI), physical resource block (PRB) utilization rate, downlink / uplink (DL / UL) cell / user throughput, precoding matrix indicator / channel state information (PMI / CSI) report, latency statistics per cell, and power consumption metric information on supported Tx / Rx array selection combined with power consumption key performance indicator (KPI), etc.), and may include at least one of them.

[0183] Collecting O1-related data that provides the O1 configuration necessary to perform RF channel reconfiguration is done by the rApp, through the NRT-RIC framework via the R1 interface, and through the SMO function in the SMO framework via the O1 interface, sending an O1-related data collection request to the E2 node, receiving the O1-related data collection request from the SMO function by the E2 node, collecting by the E2 node O1-related data that provides the O1 configuration necessary to perform RF channel reconfiguration via the open fronthaul management plane (FH M-Plane) interface between the E2 node and the open radio unit (O-RU), and sending by the E2 node the O1-related data that provides the O1 configuration necessary to perform RF channel reconfiguration 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.

[0184] The O1 configuration data for preparing and performing RF channel reconfiguration may include at least one of O-RU Tx / Rx array selection, changing the number of SU / MU MIMO spatial streams or data layers, changing the number of SSB beams, and changing the antenna transmit power of the O-RU.

[0185] The method may further include the NRT-RIC monitoring the performance of the retrained AI / ML model, determining that a predetermined performance goal is not achieved based on the collected O1-related data, and initiating a fallback mechanism and / or an update or retraining of the AI / ML model.

[0186] According to an embodiment, in an Open Radio Access Network (O-RAN), a non-transitory computer-readable recording medium having recorded thereon executable instructions executable by at least one processor configured to implement a non-real-time RAN intelligent controller (NRT-RIC), an NRT-RIC framework, at least one SMO function, and an rApp hosted by the NRT-RIC is provided to execute a method for implementing optimization of radio frequency (RF) reconfiguration by a service management and orchestration (SMO) framework.The method is O1-related data provided by the rApp to provide the O1 configuration necessary for the RF channel reconfiguration, and the O1-related data collected via the open fronthaul management plane (FH M-Plane) interface between the E2 node and the open radio unit (O-RU) is collected from the E2 node through the R1 interface via the NRT-RIC framework and via the O1 interface through the SMO function within the SMO framework; re-training at least one artificial intelligence / machine learning (AI / ML) model by the SMO based on the collected O1-related data; deploying and activating one re-trained AI / ML model for inferring data that provides the O1 configuration necessary for performing RF channel reconfiguration within the O-RAN among at least one re-trained AI / ML by the rApp; monitoring the O1-related data that provides the O1 configuration necessary for performing RF channel reconfiguration by the rApp through the R1 interface via the NRT-RIC framework and via the O1 interface through at least one SMO function within the SMO framework; evaluating the O1-related data that provides the O1 configuration necessary for performing RF channel reconfiguration by the rApp; determining to generate O1 configuration data for preparing and performing RF channel reconfiguration by the rApp; sending the O1 configuration data for preparing and performing RF channel reconfiguration to at least one E2 node by the rApp through the R1 interface via the NRT-RIC framework and via the O1 interface through at least one SMO function within the SMO framework; and implementing the RF channel reconfiguration within the O-RAN by the E2 node and the O-RU. Implementing includes converting the O1 configuration data for preparing and performing RF channel reconfiguration by the E2 node and instructing the O-RU to perform the RF channel reconfiguration via the open FH M-Plane by the E2 node.

[0187] The retraining of at least one AI / ML model includes: the rApp selecting one AI / ML model from a plurality of AI / ML models; the rApp sending a start request for retraining the AI / ML model to the NRT-RIC framework; the NRT-RIC framework retraining the AI / ML model; the rApp monitoring the retrained AI / ML model parameters and determining, based on the retrained AI / ML model parameters, the retrieval of the retrained AI / ML model from the NRT-RIC framework; the rApp requesting the retrained AI / ML model from the NRT-RIC framework; and the NRT-RIC framework sending the retrained AI / ML model to the rApp.

[0188] The retraining of at least one AI / ML model includes the rApp retraining one AI / ML model from a plurality of AI / ML models.

[0189] The O1-related data providing the O1 configuration necessary to perform RF channel reconfiguration 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 energy efficiency / energy consumption (EE / EC) measurement reports. The energy efficiency / energy consumption (EE / EC) measurement reports include, for each cell, for each synchronization signal block (SSB), reference signal received quality (RSRQ) measurement results, for each cell, for each SSB, reference signal received power (RSRP) measurement results, for each cell, for each SSB, signal-to-interference plus noise ratio (SINR) measurement results, energy consumption, power consumed by hardware components, transmit power, load statistics per cell and per carrier (number of active users, average number of radio resource control (RRC) connections, average number of scheduled active users per transmission time interval (TTI), physical resource block (PRB) utilization rate, downlink / uplink (DL / UL) cell / user throughput, precoding matrix indicator / channel state information (PMI / CSI) report, latency statistics per cell, and power consumption metric information on supported Tx / Rx array selection combined with power consumption key performance indicator (KPI), etc.), and may include at least one of these.

[0190] Collecting O1-related data that provides the O1 configuration necessary to perform RF channel reconfiguration is done by the rApp through the NRT-RIC framework via the R1 interface and via the O1 interface through the SMO function within the SMO framework, sending an O1-related data collection request to the E2 node, receiving the O1-related data collection request from the SMO function by the E2 node, collecting by the E2 node O1-related data that provides the O1 configuration necessary to perform RF channel reconfiguration via the open front-haul management plane (FH M-Plane) interface between the E2 node and the open radio unit (O-RU), and sending by the E2 node the O1-related data that provides the O1 configuration necessary to perform RF channel reconfiguration collected via the O1 interface through the SMO function and the NRT-RIC framework to the rApp via the R1 interface.

[0191] The O1 configuration data for preparing and performing RF channel reconfiguration may include at least one of O-RU Tx / Rx array selection, changing the number of SU / MU MIMO spatial streams or data layers, changing the number of SSB beams, and changing the antenna transmit power of the O-RU.

[0192] The method may further include the NRT-RIC monitoring the performance of the retrained AI / ML model, determining that a predetermined performance goal is not achieved based on the collected O1-related data, and initiating a fallback mechanism and / or an update or retraining of the AI / ML model.

[0193] According to an embodiment, a system and method enable energy savings (ES) by reducing the power consumption of an O-RU through RF channel reconfiguration (e.g., by switching off a specific transmitter / receiver (Tx / Rx) array). For example, by switching off 32 out of 64 Tx / Rx arrays of an O-RU in a digital m-MIMO architecture and reducing the corresponding number of spatial layers and synchronization signal blocks (SSBs), ES can be achieved by reducing the power consumption of the O-RU.

[0194] As a result, the system and method implement an NRT-RIC framework that allows a network operator to flexibly configure RF channel reconfiguration (e.g., switch on / off Tx / Rx arrays within an m-MIMO antenna in an O-RU) to optimize the energy efficiency of the entire network rather than local optimization in O-RAN.

[0195] The above disclosure provides illustrations and descriptions, but is not intended to be exhaustive or to limit implementations to the exact forms disclosed. Changes and modifications are possible in light of the above disclosure or may be obtained from the practice of implementations.

[0196] Some embodiments may relate to a system, method, and / or computer-readable medium at any possible level of technical detail of integration. Further, one or more of the above-described components may be stored on a computer-readable medium and implemented as instructions 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) storing computer-readable program instructions for causing a processor to execute operations.

[0197] A computer-readable storage medium may be a tangible device that can hold and store instructions for use by an instruction-executing device. The 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 disc read-only memory (CD-ROM), digital versatile disks (DVDs), memory sticks, floppy disks, punch cards, mechanically encoded devices such as a raised structure in a groove in which instructions are recorded, and any suitable combination thereof. As used herein, a computer-readable storage medium is not construed to be a transitory signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., an optical pulse passing through an optical fiber cable), or an electrical signal transmitted through a wire.

[0198] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to respective computing / processing devices, or can be downloaded from an external computer or an external storage device via a network such as the Internet, a local area network, a wide area network, and / or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and transfers the computer-readable program instructions for storage in a computer-readable storage medium within each respective computing / processing device.

[0199] The computer-readable program code / instructions for performing the operation may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, configuration data for an integrated circuit, or source code or object code written in any combination of one or more programming languages including object-oriented programming languages such as Smalltalk, C++, and procedural programming languages such as the "C" programming language or similar programming languages. The computer-readable program instructions may be executed entirely on the user's computer as a stand-alone software package, partially on the user's computer, partially on the user's computer, partially on a remote computer, or entirely on a remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (e.g., through the Internet using an Internet service provider). In some embodiments, for example, an electronic circuit including a programmable logic circuit, a field-programmable gate array (FPGA), or a programmable logic array (PLA) may execute the computer-readable program instructions by utilizing the state information of the computer-readable program instructions to personalize the electronic circuit for performing the aspect or operation.

[0200] These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions / acts specified in the flowchart and / or block diagram (one or more blocks). These computer-readable program instructions may be stored in a computer-readable storage medium that, when containing instructions that implement aspects of the functions / acts specified in the flowchart and / or block diagram (one or more blocks), causes a computer, programmable data processing apparatus, and / or other device to function in a particular manner.

[0201] 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 produce a computer-implemented process, thereby causing instructions executing on the computer, other programmable apparatus, or other device to implement the functions / acts specified in the flowchart and / or block diagram (one or more blocks).

[0202] The flowchart and block diagrams shown illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer-readable media according to various embodiments. Here, each block in the flowchart or block diagram may represent a microservice, module, segment, or portion of instructions that includes one or more executable instructions for implementing a particular logical function. The methods, computer systems, and computer-readable media may include additional blocks, fewer blocks, different blocks, or differently arranged blocks than those shown in the figures. In some alternative implementations, the functions shown in the blocks may occur out of the order shown in the figures. For example, two blocks shown in succession may, in fact, be executed simultaneously or substantially simultaneously depending on the functions involved, or the blocks may be executed in the reverse order. Note that each block of the illustrations of the block diagrams and / or flowcharts, and combinations of blocks in the illustrations of the block diagrams and / or flowcharts, may be implemented by a system based on dedicated hardware for performing a particular function or act, or by a combination of dedicated hardware and computer instructions.

[0203] It is apparent that the systems and / or methods described herein may be implemented in different forms of hardware, firmware, or a combination of hardware and software. It is understood that the actual dedicated control hardware or software code used to implement these systems and / or methods does not limit the implementation. Thus, the operations and behavior of the systems and / or methods are described herein without reference to specific software code. This is understood to mean that software and hardware may be designed based on the description herein to implement the systems and / or methods.

Claims

1. A system for implementing optimization of radio frequency (RF) reconfiguration by a service management and orchestration (SMO) framework within 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; wherein the at least one processor collects, via an Open Front-Haul Management Plane (FH M-Plane) interface between an E2 node and an Open Radio Unit (O-RU), O1-related data which is O1-related data for providing an O1 configuration required to perform the RF channel reconfiguration by the rApp, and transmits the O1-related data through an R1 interface via the NRT-RIC framework and through an O1 interface via the SMO function within the SMO framework from the E2 node; the SMO retrains at least one artificial intelligence / machine learning (AI / ML) model based on the collected O1-related data; the rApp deploys and activates one retrained AI / ML model for inferring data for providing an O1 configuration required to perform the RF channel reconfiguration within the O-RAN from among the at least one retrained AI / ML; the rApp monitors the O1-related data for providing an O1 configuration required to perform the RF channel reconfiguration through the R1 interface via the NRT-RIC framework and through the O1 interface via the SMO function within the SMO framework; the rApp evaluates the O1-related data for providing an O1 configuration required to perform the RF channel reconfiguration; the rApp determines to generate O1 configuration data for preparing and performing the RF channel reconfiguration. The rApp sends, to the at least one E2 node, the O1 configuration data for preparing and executing the RF channel reconfiguration through the NRT-RIC framework via the R1 interface and via the at least one SMO function within the SMO framework via the O1 interface. The E2 node and the O-RU implement the RF channel reconfiguration within the O-RAN. configured to execute the instructions for During implementation, the at least one processor The E2 node converts the O1 configuration data for preparing and executing the RF channel reconfiguration. The E2 node instructs the O-RU to execute the RF channel reconfiguration via the open FH M-Plane. A system further configured as such. [

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

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

4. ] The O1-related data providing the O1 configuration necessary to perform the RF channel reconfiguration comprises at least one of configuration, performance indicators, and measurement reports provided by the O-RU. The measurement reports comprise at least one of energy efficiency / energy consumption (EE / EC) measurement reports. The energy efficiency / energy consumption (EE / EC) measurement reports include reference signal received quality (RSRQ) measurement results for each synchronization signal block (SSB) per cell, reference signal received power (RSRP) measurement results for each SSB per cell, signal-to-interference plus noise ratio (SINR) measurement results for each SSB per cell, energy consumption, power consumed by hardware components, transmit power, load statistics per cell and per carrier (number of active users, average number of radio resource control (RRC) connections, average number of scheduled active users per transmission time interval (TTI), physical resource block (PRB) utilization rate, downlink / uplink (DL / UL) cell / user throughput, precoding matrix indicator / channel state information (PMI / CSI) report, latency statistics per cell, and power consumption metrics information on supported Tx / Rx array selection combined with power consumption key performance indicator (KPI), etc.), and comprise at least one of them. The system according to claim 1.

5. During the collection of the O1-related data providing the O1 configuration necessary to perform the RF channel reconfiguration, by the at least one processor the rApp sends an O1-related data collection request to the E2 node through the NRT-RIC framework via the R1 interface and via the O1 interface through the SMO function within the SMO framework. The E2 node receives the O1-related data collection request from the SMO function. The E2 node collects the O1-related data providing the O1 configuration necessary to perform the RF channel reconfiguration from the O-RU via the open front haul management plane (FH M-Plane) interface between the E2 node and the open radio unit (O-RU). The O1-related data that the E2 node provides to the rApp via the R1 interface, which is necessary to execute the RF channel reconfiguration collected via the O1 interface through the SMO function in the SMO framework and the NRT-RIC framework. The system according to claim 1, configured as such. **Claim 6** The O1 configuration data for preparing and executing the RF channel reconfiguration includes at least one of O-RU Tx / Rx array selection, changing the number of SU / MU MIMO spatial streams or data layers, changing the number of SSB beams, and changing the antenna transmission power of the O-RU. The system according to claim 1. **Claim 7** The at least one processor The NRT-RIC monitors the performance of the retrained AI / ML model. Based on the collected O1-related data, it determines that a predetermined performance target is not achieved. It starts a fallback mechanism and / or updates or retrains the AI / ML model. The system according to claim 1, further configured as such. **Claim 8** A method for implementing the optimization of radio frequency (RF) reconfiguration by a service management and orchestration (SMO) framework within an Open Radio Access Network (O-RAN), comprising: The rApp collects O1-related data necessary to provide the O1 configuration for executing the RF channel reconfiguration, which is collected via the open front-haul management plane (FH M-Plane) interface between the E2 node and the Open Radio Unit (O-RU), through the NRT-RIC framework via the R1 interface, and via the O1 interface through the SMO function within the SMO framework from the E2 node. The SMO retrains at least one artificial intelligence / machine learning (AI / ML) model based on the collected O1-related data. Deploy and activate, by the rApp, one retrained AI / ML model for inferring data that provides the O1 configuration necessary to perform the RF channel reconfiguration within the O-RAN among the at least one retrained AI / ML; Monitor, by the rApp, the O1-related data that provides the O1 configuration necessary to perform the RF channel reconfiguration, via the R1 interface through the NRT-RIC framework and via the O1 interface through the SMO function within the SMO framework; Evaluate, by the rApp, the O1-related data that provides the O1 configuration necessary to perform the RF channel reconfiguration; Determine, by the rApp, to generate O1 configuration data for preparing and performing the RF channel reconfiguration; Send, by the rApp, the O1 configuration data for preparing and performing the RF channel reconfiguration, via the R1 interface through the NRT-RIC framework and via the O1 interface through at least one SMO function within the SMO framework, to the at least one E2 node; Implement, by the E2 node and the O-RU, the RF channel reconfiguration within the O-RAN; comprising; wherein the implementing comprises; Convert, by the E2 node, the O1 configuration data for preparing and performing the RF channel reconfiguration; Instruct, by the E2 node, via the open FH M-Plane, the O-RU to perform the RF channel reconfiguration; A method comprising. Claim 9 The retraining of the at least one AI / ML model comprises: Select, by the rApp, one AI / ML model from a plurality of AI / ML models; Send, by the rApp, a start request for retraining the AI / ML model to the NRT-RIC framework; Retrain, by the NRT-RIC framework, the AI / ML model; The rApp monitors the re-trained AI / ML model parameters and determines the retrieval of the re-trained AI / ML model from the NRT-RIC framework based on the re-trained AI / ML model parameters. The rApp requests the re-trained AI / ML model from the NRT-RIC framework. The NRT-RIC framework sends the re-trained AI / ML model to the rApp. The method according to claim 8, comprising the above.

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

11. The O1-related data providing the O1 configuration required to perform the RF channel reconfiguration comprises at least one of configuration, performance indicator, and measurement report provided by the O-RU. The measurement report comprises at least one of energy efficiency / energy consumption (EE / EC) measurement reports. The energy efficiency / energy consumption (EE / EC) measurement report comprises at least one of reference signal received quality (RSRQ) measurement results for each synchronization signal block (SSB) per cell, reference signal received power (RSRP) measurement results for each SSB per cell, signal-to-interference plus noise ratio (SINR) measurement results for each SSB per cell, energy consumption, power consumed by hardware components, transmit power, load statistics per cell and per carrier (number of active users, average number of radio resource control (RRC) connections, average number of scheduled active users per transmission time interval (TTI), physical resource block (PRB) utilization rate, downlink / uplink (DL / UL) cell / user throughput, precoding matrix indicator / channel state information (PMI / CSI) report, latency statistics per cell, and power consumption metric information on supported Tx / Rx array selection combined with power consumption key performance indicator (KPI), etc.). The method according to claim 8.

12. Collecting the O1-related data that provides the O1 configuration required to perform the RF channel reconfiguration, sending, by the rApp, an O1-related data collection request to the E2 node through the NRT-RIC framework via the R1 interface and through the SMO function in the SMO framework via the O1 interface; receiving, by the E2 node, the O1-related data collection request from the SMO function; collecting, by the E2 node, the O1-related data that provides the O1 configuration required to perform the RF channel reconfiguration from the O-RU via the open front-haul management plane (FH M-Plane) interface between the E2 node and the open radio unit (O-RU); sending, by the E2 node, the O1-related data that provides the O1 configuration required to perform the RF channel reconfiguration collected via the O1 interface through the SMO function in the SMO framework and the NRT-RIC framework to the rApp via the R1 interface; The method according to claim 8, comprising: **Claim 13** The O1 configuration data for preparing and performing the RF channel reconfiguration comprises at least one of O-RU Tx / Rx array selection, change in the number of SU / MU MIMO spatial streams or data layers, change in the number of SSB beams, and change in the antenna transmission power of the O-RU. The method according to claim 8. **Claim 14** monitoring, by the NRT-RIC, the performance of the retrained AI / ML model; determining that a predetermined performance target is not achieved based on the collected O1-related data; initiating a fallback mechanism and / or an update or retraining of the AI / ML model; The method according to claim 8, further comprising: **Claim 15** In an Open Radio Access Network (O-RAN), a non-transitory computer-readable recording medium having instructions recorded thereon that are executable by at least one processor configured to implement a non-real-time RAN intelligent controller (NRT-RIC), an NRT-RIC framework, at least one SMO function, and an rApp hosted by the NRT-RIC to implement optimization of radio frequency (RF) reconfiguration by a service management and orchestration (SMO) framework, the method comprising: collecting, by the rApp, O1-related data that provides an O1 configuration necessary to perform the RF channel reconfiguration, the O1-related data collected via an open front-haul management plane (FH M-Plane) interface between an E2 node and an open radio unit (O-RU), from the E2 node through an R1 interface through the NRT-RIC framework and through an O1 interface through the SMO function within the SMO framework; retraining, by the SMO, at least one artificial intelligence / machine learning (AI / ML) model based on the collected O1-related data; deploying and activating, by the rApp, one retrained AI / ML model for inferring data that provides an O1 configuration necessary to perform the RF channel reconfiguration within the O-RAN from among the at least one retrained AI / ML; monitoring, by the rApp, the O1-related data that provides an O1 configuration necessary to perform the RF channel reconfiguration through the R1 interface through the NRT-RIC framework and through the O1 interface through the SMO function within the SMO framework; evaluating, by the rApp, the O1-related data that provides an O1 configuration necessary to perform the RF channel reconfiguration; determining, by the rApp, to generate O1 configuration data for preparing and performing the RF channel reconfiguration; The rApp sends, to the at least one E2 node, the O1 configuration data for preparing and executing the RF channel reconfiguration through the NRT-RIC framework via the R1 interface and via the O1 interface through the at least one SMO function within the SMO framework. The E2 node and the O-RU implement the RF channel reconfiguration within the O-RAN. comprising The implementing The E2 node converts the O1 configuration data for preparing and executing the RF channel reconfiguration. The E2 node instructs the O-RU to execute the RF channel reconfiguration via the open FH M-Plane. A non-transitory computer-readable recording medium comprising. [

16. ] The retraining of the at least one AI / ML model The rApp selects one AI / ML model from a plurality of AI / ML models. The rApp sends a start request for retraining the AI / ML model to the NRT-RIC framework. The NRT-RIC framework retrains the AI / ML model. The rApp monitors the retrained AI / ML model parameters and determines the retrieval of the retrained AI / ML model from the NRT-RIC framework based on the retrained AI / ML model parameters. The rApp requests the retrained AI / ML model from the NRT-RIC framework. The NRT-RIC framework sends the retrained AI / ML model to the rApp. The non-transitory computer-readable recording medium according to claim 15, comprising. [

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

18. ] The O1-related data providing the O1 configuration required to perform the RF channel reconfiguration comprises at least one of configuration, performance indicators, and measurement reports provided by the O-RU. The measurement reports comprise at least one of energy efficiency / energy consumption (EE / EC) measurement reports. The energy efficiency / energy consumption (EE / EC) measurement reports include, for each cell, for each synchronization signal block (SSB), reference signal received quality (RSRQ) measurement results, for each cell, for each SSB, reference signal received power (RSRP) measurement results, for each cell, for each SSB, signal-to-interference plus noise ratio (SINR) measurement results, energy consumption, power consumed by hardware components, transmit power, load statistics for each cell and each carrier (number of active users, average number of radio resource control (RRC) connections, average number of scheduled active users per transmission time interval (TTI), physical resource block (PRB) utilization rate, downlink / uplink (DL / UL) cell / user throughput, precoding matrix indicator / channel state information (PMI / CSI) reports, latency statistics for each cell, and power consumption metric information on supported Tx / Rx array selection combined with power consumption key performance indicators (KPIs), etc.), and at least one of these. The non-transitory computer-readable recording medium according to claim 15.

19. Collecting the O1-related data providing the O1 configuration required to perform the RF channel reconfiguration is to send an O1-related data collection request to the E2 node by the rApp through the NRT-RIC framework via the R1 interface and through the O1 interface via the SMO function within the SMO framework; is for the E2 node to receive the O1-related data collection request from the SMO function; is for the E2 node to collect the O1-related data providing the O1 configuration required to perform the RF channel reconfiguration from the O-RU via the open front-haul management plane (FH M-Plane) interface between the E2 node and the open radio unit (O-RU). The E2 node sends, via the R1 interface, to the rApp, the O1-related data that provides the O1 configuration necessary to execute the RF channel reconfiguration collected via the O1 interface through the SMO function in the SMO framework and the NRT-RIC framework. The non-transitory computer-readable recording medium according to claim 15, comprising.

20. The O1 configuration data for preparing and executing the RF channel reconfiguration comprises at least one of O-RU Tx / Rx array selection, change in the number of SU / MU MIMO spatial streams or data layers, change in the number of SSB beams, and change in the antenna transmission power of the O-RU. The non-transitory computer-readable recording medium according to claim 15.

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