Implementing an advanced sleep mode in a telecommunications network
System and method for implementing advanced sleep modes in an O-RAN, the method includes collecting measurement data for training an AI/ML model; based on the collected measurement data, training an A/ML model and deploying the AI/ML model in the nRT-RIC; activating the trained AI/ML model in the nRT-RIC; monitoring energy optimization data for AI/ML model inference from an open radio unit (O-RU) via an E2 node; activating at least one advanced sleep mode (ASM) in the nRT-RIC; based on the activation of the ASM, collecting data to temporarily deactivate O-RU components; based on the activated AI/ML model and the ASM, evaluating, the collected data to temporarily deactivate O-RU components; based on the evaluating, requesting to initiate the ASM to the O-RU; based on the ASM initiation request, implementing the ASM, by the E2 node and the O-RU.
1 . A system for implementing advanced sleep modes in an open radio access network (O-RAN), the system comprising:
a near-real-time radio access network (RAN) intelligent controller (nRT-RIC) and a service management and orchestration (SMO) framework, the SMO framework comprising a non-real-time radio access network (RAN) intelligent controller (NRT-RIC), wherein the system is configured to:
collect, by the SMO framework, measurement data for training an artificial intelligence/machine learning (AI/ML) model;
based on the collected measurement data, by the NRT-RIC, train an AI/ML model and deploy the AI/ML model in the nRT-RIC;
activate, by the SMO framework, the trained AI/ML model in the nRT-RIC;
monitor, by the NRT-RIC, energy optimization data for A/ML model inference from an open radio unit (O-RU) via an E2 node;
activate, by the SMO framework, at least one advanced sleep mode (ASM) in the nRT-RIC;
based on the activation of the at least one ASM, by the nRT-RIC, collect data to temporarily deactivate one or more O-RU components from the O-RU via the E2 node;
based on the activated AI/Mb model and the at least one ASM, evaluate, by the nRT-RIC, the collected data to temporarily deactivate one or more O-RU components;
based on the evaluating, request, by the nRT-RIC, to initiate the at least one ASM to the O-RU via the E2 node; and
based on the ASM initiation request, by the O-RU, implement the at least one ASM.
2 . The system as claimed in claim 1 , wherein the system is configured to collect the data to temporarily deactivate the one or more O-RU components based on the activation of the at least one ASM, by:
sending, by the nRT-RIC, a data collection request for energy saving via an E2 interface to the E2 node;
receiving, by the E2 node, the data collection request for energy saving from the nRT-RIC;
collecting, by the E2 node, the data to temporarily deactivate the one or more O-RU components from the O-RU via the open FH M-Plane interface; and
sending, by the E2 node, the collected data to temporarily deactivate the one or more O-RU components via the E2 interface to the nRT-RIC.
3 . The system as claimed in claim 1 , wherein the system is configured to evaluate the collected data to temporarily deactivate the one or more O-RU components, by:
receiving, by the nRT-RIC, the collected data to temporarily deactivate the one or more O-RU components from the E2 node;
based on the collected data to temporarily deactivate the one or more O-RU components, applying, by the nRT-RIC, AI/ML model inference;
based on the AI/ML model inference, generating, by the nRT-RIC, at least one of an E2 control command to request to initiate the at least one ASM to the E2 node that complies with the O-RU's capabilities; and
sending, by the nRT-RIC, the at least one of the E2 control command to the E2 node.
4 . The system as claimed in claim 1 , wherein the system is configured to evaluate the collected data to temporarily deactivate the one or more O-RU components, by:
receiving, by the nRT-RIC, the collected data to temporarily deactivate the one or more O-RU components from the E2 node;
based on the collected data to temporarily deactivate the one or more O-RU components, applying, by the nRT-RIC, AI/ML model inference;
based on the AI/ML model inference, generating, by the nRT-RIC, at least one of an E2 policy command for an energy savings (ES) that guides the E2 node that complies with the O-RU's capabilities to initiate the at least one ASM; and
sending, by the nRT-RIC, the at least one E2 policy command to the E2 node.
5 . The system as claimed in claim 1 , wherein the system is further configured to:
based on the implementing, receive, by the NRT-RIC via the SMO framework, feedback comprising a performance analysis of the AI/ML model;
analyze, by the NRT-RIC, the performance of the AI/ML model in the nRT-NIC;
determine, by the NRT-RIC, that at least one predetermined performance objective is not achieved based on the performance of the AI/Mb model; and
initiate, by the NRT-RIC, a fallback mechanism to pertain to the at least one predetermined performance objective.
6 . The system as claimed in claim 1 , wherein the system is configured to implement the at least one ASM by:
based on the capabilities of the O-RU, scheduling, by the E2 node or the O-RU, at least one symbol to minimize a number of symbols in time domain, wherein the E2 node or the O-RU is configured to perform the scheduling by:
converging the at least one symbol from time domain into frequency domain to elongate a deactivation time period of the one or more O-RU components, wherein the converged at least one symbol is at least one of a Physical Downlink Channel PDSCH symbol and a Physical Downlink Control Channel PDCCH symbol.
7 . The system as claimed in claim 1 , wherein the system is configured to implement the at least one ASM by:
based on the capabilities of the O-RU, scheduling, by the E2 node or the O-RU, at least one slot to minimize a number of slots in time domain, wherein the E2 node or the O-RU is configured to perform the scheduling by:
converging the at least one slot from time domain into frequency domain to elongate a deactivation time period of the one or more O-RU components, wherein the converged at least one slot is at least one of a Synchronization Signal Block-Master Information Block SSB-MIB slot, System Information Block Type 1 SIB1 slot, SI slot, Paging Frame slot and a Physical Downlink Channel PDSCH slot.
8 . A method for implementing advanced sleep modes in an open radio access network (O-RAN), the method comprising:
collecting, by a service management and orchestration (SMO) framework, measurement data for training an artificial intelligence/machine learning (AI/ML) model;
based on the collected measurement data, by a non-real-time radio access network (RAN) intelligent controller (NRT-RIC), training an AI/ML model and deploying the AI/ML model in an a near-real-time radio access network (RAN) intelligent controller (nRT-RIC);
activating, by the SMO framework, the trained AI/ML model in the nRT-RIC;
monitoring, by the NRT-RIC, energy optimization data for AI/ML model inference from an open radio unit (O-RU) via an E2 node;
activating, by the SMO framework, at least one advanced sleep mode (ASM) in the nRT-RIC;
based on the activation of the at least one ASM, by the nRT-RIC, collecting data to temporarily deactivate one or more O-RU components from the O-RU via the E2 node;
based on the activated AI/ML model and the at least one ASM, evaluating, by nRT-RIC, the collected data to temporarily deactivate one or more O-RU components;
based on the evaluating, requesting, by the nRT-RIC, to initiate the at least one ASM to the O-RU via the E2 node; and
based on the ASM initiation request, by the O-RU, implementing the at least one ASM.
9 . The method as claimed in claim 8 , wherein collecting the data to temporarily deactivate the one or more O-RU components based on the activation of the at least one ASM comprises:
sending, by the nRT-RIC, a data collection request for energy saving via an E2 interface to the E2 node;
receiving, by the E2 node, the data collection request for energy saving from the nRT-RIC;
collecting, by the E2 node, the data to temporarily deactivate the one or more O-RU components from the O-RU via the open FH M-Plane interface; and
sending, by the E2 node, the collected data to temporarily deactivate the one or more 0-RU components via the E2 interface to the nRT-RIC.
10 . The method claimed in claim 8 , wherein evaluating the collected data to temporarily deactivate the one or more O-RU components comprises:
receiving, by the nRT-RIC, the collected data to temporarily deactivate the one or more O-RU components from the E2 node;
based on the collected data to temporarily deactivate the one or more O-RU components, applying, by the nRT-RIC, AI/ML model inference;
based on the AI/ML model inference, generating, by the nRT-RIC, at least one of an E2 control command to request to initiate the at least one ASM to the E2 node that complies with the O-RU's capabilities; and
sending, by the nRT-RIC, the at least one of the E2 control command to the E2 node.
11 . The method as claimed in claim 8 , wherein evaluating the collected data to temporarily deactivate the one or more O-RU components comprises:
receiving, by the nRT-RIC, the collected data to temporarily deactivate the one or more O-RU components from the E2 node;
based on the collected data to temporarily deactivate the one or more O-RU components, applying, by the nRT-RIC, AI/ML model inference;
based on the AI/ML model inference, generating, by the nRT-RIC, at least one of an E2 policy command for energy savings (ES) that guides the E2 node that complies with the O-RU's capabilities to initiate the at least one ASM; and
sending, by the nRT-RIC, the at least one E2 policy command to the E2 node.
12 . The method as claimed in claim 8 , the method comprises:
based on the implementing, receiving, by the NRT-RIC via the SMO framework, feedback comprising a performance analysis of the AI/ML model;
analyzing, by the NRT-RIC, the performance of the AI/ML model in the nRT-NIC;
determining, by the NRT-RIC, that at least one predetermined performance objective is not achieved based on the performance of the AI/ML model; and
initiating, by the NRT-RIC, a fallback mechanism to pertain to the at least one predetermined performance objective.
13 . The method as claimed in claim 8 , wherein implementing the at least one ASM comprises:
based on the capabilities of the O-RU, scheduling, by the E2 node or the O-RU, at least one symbol to minimize a number of symbols in time domain, wherein the scheduling comprises:
converging the at least one symbol from time domain into frequency domain to elongate a deactivation time period of the one or more O-RU components, wherein the converged at least one symbol is at least one of a Physical Downlink Channel PDSCH symbol and a Physical Downlink Control Channel PDCCH symbol.
14 . The method as claimed in claim 8 , wherein implementing the at least one ASM, by the E2 node and the O-RU, comprises:
based on the capabilities of the O-RU, scheduling, by the E2 node or the O-RU, at least one slot to minimize a number of slots in time domain, wherein the scheduling comprises:
converging the at least one slot from time domain into frequency domain to elongate a deactivation time period of the one or more O-RU components, wherein the converged at least one slot is at least one of a Synchronization Signal Block-Master Information Block SSB-MIB slot, a System Information Block Type 1 SIB1 slot, a SI slot, a Paging Frame slot and a Physical Downlink Channel PDSCH slot.
15 . A non-transitory computer-readable recording medium having recorded thereon instructions executable by at least one processor, at least one processor configured to implement a method for implementing advanced sleep modes in an open radio access network (O-RAN), the method comprising:
collecting, by a service management and orchestration (SMO) framework, measurement data for training an artificial intelligence/machine learning (AI/ML) model;
based on the collected measurement data, by a non-real-time radio access network (RAN) intelligent controller (NRT-RIC), training an AI/ML model and deploying the AI/ML model in a near-real-time radio access network (RAN) intelligent controller (nRT-RIC);
activating, by the SMO framework, the trained AI/ML model in the nRT-RIC;
monitoring, by the NRT-RIC, energy optimization data for AI/ML model inference from an open radio unit (O-RU) via an E2 node;
activating, by the SMO framework, at least one advanced sleep mode (ASM) in the nRT-RIC;
based on the activation of the at least one ASM, by the nRT-RIC, collecting data to temporarily deactivate one or more O-RU components from the O-RU via the E2 node;
based on the activated AI/ML model and the at least one ASM, evaluating, by nRT-RIC, the collected data to temporarily deactivate one or more O-RU components;
based on the evaluating, requesting, by the nRT-RIC, to initiate the at least one ASM to the O-RU via the E2 node; and
based on the ASM initiation request, by the O-RU, implementing the at least one ASM.
16 . The non-transitory computer-readable recording medium as claimed in claim 15 , wherein collecting the data to temporarily deactivate the one or more O-RU components based on the activation of the at least one ASM, comprises:
sending, by the nRT-RIC, a data collection request for energy saving via an E2 interface to the E2 node;
receiving, by the E2 node, the data collection request for energy saving from the nRT-RIC;
collecting, by the E2 node, the data to temporarily deactivate the one or more O-RU components from the O-RU via the open FH M-Plane interface; and
sending, by the E2 node, the collected data to temporarily deactivate the one or more 0-RU components via the E2 interface to the nRT-RIC.
17 . The non-transitory computer-readable recording medium as claimed in claim 15 , wherein evaluating the collected data to temporarily deactivate the one or more O-RU components, comprises:
receiving, by the nRT-RIC, the collected data to temporarily deactivate the one or more O-RU components from the E2 node;
based on the collected data to temporarily deactivate the one or more O-RU components, applying, by the nRT-RIC, AI/ML model inference;
based on the AI/ML model inference, generating, by the nRT-RIC, at least one of an E2 control command to request to initiate the at least one ASM to the E2 node that complies with the O-RU's capabilities;
based on the AI/ML model inference, generating, by the nRT-RIC, at least one of an E2 policy command for energy savings (ES) that guides the E2 node that complies with the O-RU's capabilities to initiate the at least one ASM; and
sending, by the nRT-RIC, at least one of the E2 control command and/or the E2 policy command to the E2 node.
18 . The non-transitory computer-readable recording medium as claimed in claim 15 , the method comprises:
based on the implementing, receiving, by the NRT-RIC via the SMO framework, feedback comprising a performance analysis of the AI/ML model;
analyzing, by the NRT-RIC, the performance of the AI/ML model in the nRT-NIC;
determining, by the NRT-RIC, that at least one predetermined performance objective is not achieved based on the performance of the AI/ML model; and
initiating, by the NRT-RIC, a fallback mechanism to pertain to the at least one predetermined performance objective.
19 . The non-transitory computer-readable recording medium as claimed in claim 15 , wherein implementing the at least one ASM comprises:
based on the capabilities of the O-RU, scheduling, by the E2 node or the O-RU, at least one symbol to minimize a number of symbols in time domain, wherein the scheduling, comprises:
converging the at least one symbol from time domain into frequency domain to elongate a deactivation time period of the one or more O-RU components, wherein the converged at least one symbol is at least one of a Physical Downlink Channel PDSCH symbol and a Physical Downlink Control Channel PDCCH symbol.
20 . The non-transitory computer-readable recording medium as claimed in claim 15 , wherein implementing the at least one ASM comprises:
based on the capabilities of the O-RU, scheduling, by the E2 node or the O-RU, at least one slot to minimize a number of slots in time domain, wherein the scheduling, comprises:
converging the at least one slot from time domain into frequency domain to elongate a deactivation time period of the one or more O-RU components, wherein the converged at least one slot is at least one of a Synchronization Signal Block-Master Information Block SSB-MIB slot, a System Information Block Type 1 SIB1 slot, a SI slot, a Paging Frame slot and a Physical Downlink Channel PDSCH slot.