System and method for carrier and/or cell switch off/on optimization based on an A1 policy in a telecommunications network
A method for implementing a carrier and/or cell switch off/on optimization in an O-RAN based on an A1 policy, the method includes: deploying, by the NRT-RIC framework, a AI/ML model to the nRT-RIC; providing, by the rApp, an A1 policy to prepare and execute the cell and/or carrier switch off/on optimization through the NRT-RIC framework to the nRT-RIC; collecting, by the nRT-RIC, data to perform the cell and/or carrier switch off/on via the E2-interface from the O-RU via the E2 node; evaluating, by nRT-RIC, the collected data; generating, by the nRT-RIC, an E2 message and send, by the nRT-RIC, the E2 message to the O-RU via the E2 node; implementing, by the E2 node and the O-RU, the cell and/or carrier switch off/on optimization within the O-RAN; receiving, by the rApp, an A1 policy feedback through an NRT-RIC framework from the O-RU via the E2 node and the nRT-RIC.
1 . A system for implementing at least one of carrier or cell switch off/on optimization in an open radio access network (O-RAN) based on an A1 policy, the system comprising:
a memory storing instructions; and
at least one processor configured to implement a service management and orchestration (SMO) framework, a near-real-time radio intelligent controller (nRT-RIC), a non-real-time radio intelligent controller (NRT-RIC), a NRT-RIC framework and an application (rApp), the at least one processor configured to execute the instructions to:
collect, by the application (rApp), data to perform at least one of cell or carrier switch off/on optimization through at least one of the NRT-RIC framework or the SMO framework via the nRT-RIC from an open radio unit (O-RU);
based on the collected optimization data, by the SMO, re-train at least one artificial intelligence/machine learning (AI/ML) model;
among the at least one re-trained AI/ML, deploy, by the NRT-RIC framework, a re-trained AI/ML model to the nRT-RIC;
provide, by the application (rApp), an A1 policy to prepare and execute the at least one of cell or carrier switch off/on optimization through the NRT-RIC framework to the nRT-RIC;
based on the provided A1 policy, by the nRT-RIC, collect data to perform the at least one of cell or carrier switch off/on from the O-RU via an E2 node;
based on the deployed AI/ML model and the provided A1 policy, evaluate, by nRT-RIC, the collected data to perform the at least one of cell or carrier switch off/on;
based on the evaluating, generate, by the nRT-RIC, at least one E2 message to prepare and execute the at least one of cell or carrier switch off/on optimization and send, by the nRT-RIC, the at least one E2 message to the O-RU via the E2 node;
based on the at least one E2 message, implement, by the E2 node and the O-RU, the at least one of cell or carrier switch off/on optimization within the O-RAN;
based on the implementing, receive, by the application (rApp), an A1 policy feedback through an NRT-RIC framework from the O-RU via the E2 node and the nRT-RIC.
2 . The system as claimed in claim 1 , wherein while collecting the data to perform the at least one of cell or carrier switch off/on by the nRT-RIC, the at least one processor is configured to:
interpret, by the nRT-RIC, the provided A1 policy for at least one of cell or carrier switch off/on;
based on the interpretation of the provided A1 policy, send, by the nRT-RIC, a data collection request via an E2 interface to the E2 node;
receive, by the E2 node, the data collection request from the nRT-RIC;
collect, by the E2 node, the data to perform the at least one of cell or carrier switch off/on from the O-RU via an open FH M-Plane interface; and
send, by the E2 node, the collected data to perform the at least one of cell or carrier switch off/on via the E2 interface to the nRT-RIC.
3 . The system as claimed in claim 2 , wherein while evaluating the collected data to perform the at least one of cell or carrier switch off/on, the at least one processor is configured to:
receive, by the nRT-RIC, the collected data to perform the at least one of cell or carrier switch off/on from the E2 node via the E2 interface; and
based on the collected data to perform the at least one of cell or carrier switch off/on, apply, by the nRT-RIC, AI/ML model inference to evaluate the at least one E2 message.
4 . The system as claimed in claim 3 , wherein while generating the at least one E2 message to prepare and execute the at least one of cell or carrier switch off/on optimization, the at least one processor is configured to:
based on the AI/ML model inference, generate, by the nRT-RIC, at least one of an E2 control command or an E2 policy command for the E2 node that corresponds to the O-RU's capabilities; and
send, by the nRT-RIC, the at least one of the E2 control command or the at least one E2 policy command to the E2 node via the E2 interface.
5 . The system as claimed in claim 1 , wherein while receiving the A1 policy feedback from the O-RU, the at least one processor is further configured to:
notify, by the nRT-RIC, the A1 policy feedback based on feedback on the at least one E2 message from the O-RU towards the application (rApp) through the at least one of the NRT-RIC framework or the SMO framework;
based on the notified A1 policy feedback, monitor, by the application (rApp), at least one performance objective;
determine that at least one predetermined performance objective is not achieved based on the notified A1 policy feedback; and
initiate a fallback mechanism to pertain to the at least one predetermined performance objective.
6 . The system as claimed in claim 5 , wherein while initiating the fallback mechanism the at least one processor is further configured to:
based on the initiation, modify, by the application (rApp), the provided A1 policy to prepare and execute the at least one of cell or carrier switch off/on optimization based on the at least one predetermined performance objective; and
request, by the application (rApp), the NRT-RIC to deploy the modified A1 policy from the NRT-RIC framework to the nRT-RIC.
7 . The system as claimed in claim 6 , wherein while initiating the fallback mechanism the at least one processor is further configured to:
based on the initiation, by the application (rApp), initiates at least one of an AI/ML model update or an AI/ML model retraining based on the at least one predetermined performance objective;
update, by the application (rApp), an AI/ML model, by:
sending, by the application (rApp), an initiation request for re-training the AI/ML model to the NRT-RIC framework;
re-training, by the NRT-RIC, the AI/ML model by the NRT-RIC framework;
monitoring, by the application (rApp), re-trained AI/ML model parameters and
based on the re-trained AI/ML model parameters, by the application (rApp), determining to request the NRT-RIC to deploy the re-trained AI/ML model from the NRT-RIC framework to the nRT-RIC.
8 . A method for implementing at least one of carrier or cell switch off/on optimization in an open radio access network (O-RAN) based on an A1 policy, the method comprising:
collecting, by an application (rApp), data to perform at least one of cell or carrier switch off/on optimization through at least one of a non-real-time radio intelligent controller (NRT-RIC) framework or a service management and orchestration (SMO) framework via a near-real-time radio intelligent controller (nRT-RIC) from an open radio unit (O-RU) via an E2 node;
based on the collected optimization data, by the SMO, re-training at least one artificial intelligence/machine learning (AI/ML) model and,
among the at least one re-trained AI/ML, deploying, by the NRT-RIC framework, one re-trained AI/ML model to the nRT-RIC;
providing, by an application (rApp), an A1 policy to prepare and execute the at least one of cell or carrier switch off/on optimization through the NRT-RIC framework to the nRT-RIC;
based on the provided A1 policy, by the nRT-RIC, collecting data to perform the at least one of cell or carrier switch off/on from the O-RU via an E2 node;
based on the deployed AI/ML model and the provided A1 policy, evaluating, by nRT-RIC, the collected data to perform the at least one of cell or carrier switch off/on;
based on the evaluating, generating, by the nRT-RIC, at least one E2 message to prepare and execute the at least one of cell or carrier switch off/on optimization and
sending, by the nRT-RIC, the at least one E2 message to the O-RU via the E2 node;
based on the at least one E2 message, implementing, by the E2 node and the O-RU, the at least one of cell or carrier switch off/on optimization within the O-RAN;
based on the implementing, receiving, by the application (rApp), an A1 policy feedback through an NRT-RIC framework from the O-RU via the E2 node and the nRT-RIC.
9 . The method as claimed in claim 8 , wherein the collecting the data to perform the at least one of cell or carrier switch off/on by the nRT-RIC comprises:
interpreting, by the nRT-RIC, the provided A1 policy for at least one of cell or carrier switch off/on;
based on the interpretation of the provided A1 policy, sending, by the nRT-RIC, a data collection request via an E2 interface to the E2 node;
receiving, by the E2 node, the data collection request from the nRT-RIC;
collecting, by the E2 node, the data to perform the at least one of cell or carrier switch off/on from the O-RU via an open FH M-Plane interface; and
sending, by the E2 node, the collected data to perform the at least one of cell or carrier switch off/on via the E2 interface to the nRT-RIC.
10 . The method as claimed in claim 9 , wherein the evaluating the collected data to perform the at least one of cell or carrier switch off/on comprises:
receiving, by the nRT-RIC, the collected data to perform the at least one of cell or carrier switch off/on from the E2 node via the E2 interface; and
based on the collected data to perform the at least one of cell or carrier switch off/on, applying, by the nRT-RIC, AI/ML model inference to evaluate the at least one E2 message.
11 . The method as claimed in claim 10 , wherein the generating the at least one E2 message to prepare and execute the at least one of cell or carrier switch off/on optimization comprises:
based on the AI/ML model inference, generating, by the nRT-RIC, at least one of an E2 control command or an E2 policy command for the E2 node that corresponds to the O-RU's capabilities; and
sending, by the nRT-RIC, the at least one of the E2 control command or the E2 policy command to the E2 node via the E2 interface.
12 . The method as claimed in claim 8 , wherein the receiving of the A1 policy feedback through an NRT-RIC framework from the O-RU via the E2 node and the nRT-RIC comprises:
notifying, by the nRT-RIC, the A1 policy feedback based on feedback on the at least one E2 message from the O-RU towards the application (rApp) through the at least one of the NRT-RIC framework or the SMO framework;
based on the notified A1 policy feedback, monitoring, by the application (rApp), at least one performance objective;
determining that at least one predetermined performance objective is not achieved based on the notified A1 policy feedback; and
initiating a fallback mechanism to pertain to the at least one predetermined performance objective.
13 . The method as claimed in claim 12 , wherein the initiating the fallback mechanism comprises:
based on the initiation, modifying, by the application (rApp), the provided A1 policy to prepare and execute the at least one of cell or carrier switch off/on optimization based on the at least one predetermined performance objective; and
requesting, by the application (rApp), the NRT-RIC to deploy the modified A1 policy from the NRT-RIC framework to the nRT-RIC.
14 . The method as claimed in claim 12 , wherein the initiating the fallback mechanism comprises:
based on the initiation, by the application (rApp), initiating at least one of an AI/ML model update or an AI/ML model retraining based on the at least one predetermined performance objective;
updating, by the application (rApp), an AI/ML model, wherein the updating comprises:
sending, by the application (rApp), an initiation request for re-training the AI/ML model to the NRT-RIC framework;
re-training, by the NRT-RIC, the AI/ML model by the NRT-RIC framework; and
based on the re-trained AI/ML model parameters, by the application (rApp), determining to request the NRT-RIC to deploy the re-trained AI/ML model from the NRT-RIC framework to the nRT-RIC.
15 . A non-transitory computer-readable recording medium having recorded thereon instructions executable by at least one processor, the at least one processor further configured to perform a method for implementing at least one of carrier or cell switch off/on optimization in an open radio access network (O-RAN) based on an A1 policy, the method comprising:
collecting, by an application (rApp), data to perform at least one of a cell or carrier switch off/on optimization through at least one of a non-real-time radio intelligent controller (NRT-RIC) framework or a service management and orchestration (SMO) framework via a near-real-time radio intelligent controller (nRT-RIC) from an open radio unit (O-RU) via an E2 node;
based on the collected optimization data, by the SMO, re-training at least one artificial intelligence/machine learning (AI/ML) model and,
among the at least one re-trained AI/ML, deploying, by the NRT-RIC framework, one re-trained AI/ML model to the nRT-RIC;
providing, by an application (rApp), an A1 policy to prepare and execute the at least one of cell or carrier switch off/on optimization through the NRT-RIC framework to the nRT-RIC;
based on the provided A1 policy, by the nRT-RIC, collecting data to perform the at least one of cell or carrier switch off/on from the O-RU via an E2 node;
based on the deployed AI/ML model and the provided A1 policy, evaluating, by nRT-RIC, the collected data to perform the at least one of cell or carrier switch off/on;
based on the evaluating, generating, by the nRT-RIC, at least one E2 message to prepare and execute the at least one of cell or carrier switch off/on optimization and
sending, by the nRT-RIC, the at least one E2 message to the O-RU via the E2 node;
based on the at least one E2 message, implementing, by the E2 node and the O-RU, the at least one of cell or carrier switch off/on optimization within the O-RAN;
based on the implementing, receiving, by the application (rApp), an A1 policy feedback through an NRT-RIC framework from the O-RU via the E2 node and the nRT-RIC.
16 . The non-transitory computer-readable recording medium as claimed in claim 15 , wherein the collecting the data to perform the at least one of cell or carrier switch off/on by the nRT-RIC comprises:
interpreting, by the nRT-RIC, the provided A1 policy for at least one of cell or carrier switch off/on;
based on the interpretation of the provided A1 policy, sending, by the nRT-RIC, a data collection request via an E2 interface to the E2 node;
receiving, by the E2 node, the data collection request from the nRT-RIC;
collecting, by the E2 node, the data to perform the at least one of cell or carrier switch off/on from the O-RU via an open FH M-Plane interface; and
sending, by the E2 node, the collected data to perform the at least one of cell or carrier switch off/on via the E2 interface to the nRT-RIC.
17 . The non-transitory computer-readable recording medium as claimed in claim 16 , wherein the evaluating the collected data to perform the at least one of cell or carrier switch off/on comprises:
receiving, by the nRT-RIC, the collected data to perform the at least one of cell or carrier switch off/on from the E2 node via the E2 interface; and
based on the collected data to perform the at least one of cell or carrier switch off/on, applying, by the nRT-RIC, AI/ML model inference to evaluate the at least one E2 message.
18 . The non-transitory computer-readable recording medium as claimed in claim 17 , wherein the generating the at least one E2 message to prepare and execute the at least one of cell or carrier switch off/on optimization comprises:
based on the AI/ML model inference, generating, by the nRT-RIC, at least one of an E2 control command or an E2 policy command for the E2 node that corresponds to the O-RU's capabilities; and
sending, by the nRT-RIC, the at least one of the E2 control command or the E2 policy command to the E2 node via the E2 interface.
19 . The non-transitory computer-readable recording medium as claimed in claim 15 , wherein the receiving of the A1 policy feedback through an NRT-RIC framework from the O-RU via the E2 node and the nRT-RIC comprises:
notifying, by the nRT-RIC, the A1 policy feedback based on feedback on the at least one E2 message from the O-RU towards the application (rApp) through the at least one of the NRT-RIC framework or the SMO framework;
based on the notified A1 policy feedback, monitoring, by the application (rApp), at least one performance objective;
determining that at least one predetermined performance objective is not achieved based on the notified A1 policy feedback; and
initiating a fallback mechanism to pertain to the at least one predetermined performance objective.
20 . The non-transitory computer-readable recording medium as claimed in claim 19 , wherein the initiating the fallback mechanism comprises:
based on the initiation, modifying, by the application (rApp), the provided A1 policy to prepare and execute the at least one of cell or carrier switch off/on optimization based on the at least one predetermined performance objective; and
requesting, by the application (rApp), the NRT-RIC to deploy the modified A1 policy from the NRT-RIC framework to the nRT-RIC.