US-12628077-B2 - Systems and methods for saving energy in a network

US12628077B2US 12628077 B2US12628077 B2US 12628077B2US-12628077-B2

Abstract

Present disclosure generally relates to energy saving technology, and more particularly relates to systems and methods for saving energy using r-Apps ( 112 ) and x-Apps ( 114 B) in an Open Radio Access Network (O-RAN). The user ( 128 ) via first computing device ( 124 ) may input initial energy saving policies to “energy saving” r-Apps ( 112 ) which creates policies to start measurements in NR capacity booster cells and candidate cell. The r-Apps acts on various inputs and may decide to move NR booster cell into energy saving mode. Once all the mobile devices move to other cells, the NR capacity cell may move to energy saving mode. The r-Apps decide to de-activate energy saving mode. The Service Management and Orchestration (SMO) device ( 108 ) de-activate energy saving mode on capacity booster cell using O1 interface.

Inventors

Vikas DIXIT

Assignees

Jio Platforms Limited

Dates

Publication Date: 20260512
Application Date: 20220728
Priority Date: 20210729

Claims (20)

1 . A system for saving energy in a heterogenous network, said system comprising: a network device equipped with a Non-Real Time Radio Intelligent Controller (Non-RT RIC) and communicatively coupled to a Near-Real Time Radio Intelligent Controller (Near-RT RIC), wherein the network device is further operatively coupled to a plurality of cells in the heterogenous network, the plurality of cells comprising one or more booster cells and one or more candidate cells, the plurality of cells further communicatively coupled to an open radio access network unit (O-RAN), each said cell having one or more mobile devices associated with said cell; wherein said network device further comprises a processor that executes a set of executable instructions that are stored in a memory, upon execution of which, the processor causes the network device to: receive, a set of data packets from one or more first computing devices, said set of data packets pertaining to a set of initial energy saving requirements; receive, a set of measurements pertaining to an amount of energy consumed by the plurality of cells and an amount of traffic associated with the heterogenous network at a predefined time, wherein the set of measurements is received on execution of a second set of instructions on the Near-RT RIC configured to extract an amount of energy consumed from each emulated (E2) node, wherein each E2 interface is a bidirectional interface associated with an open radio access network and the Near-RT RIC, wherein an open radio access network distributed unit (O-DU) is further associated with the O-RAN unit; extract, by a first set of instructions to be executed on the Non-RT RIC, a first set of attributes based on the set of data packets and the set of measurements received, the first set of attributes pertaining to parameters associated with an optimal amount of energy to be saved in each cell of the heterogenous network and an increase in the amount of traffic in the heterogenous network beyond a predefined threshold; determine, by a machine learning engine associated with the network device, an amount of energy to be saved in the heterogenous network based on the first set of attributes extracted and a predetermined energy policy definition; and based on the amount of energy determined to be saved, activate the one or more booster cells to an energy saving mode, wherein the first set of instructions are configured to create policies to start a set of energy measurements in the one or more booster cells and the one or more candidate cells irrespective of whether the energy saving mode is switched on or off, wherein the set of energy measurements are stored in a centralized server, and wherein the first set of instructions are further configured to transmit the created policies to start the set of energy measurements to the Near-RT RIC on execution of the second set of instructions through a predefined interface.
2 . The system as claimed in claim 1 , wherein the network device is further operatively coupled to the one or more mobile devices through an Open radio access network Radio Unit (O-RU).
3 . The system as claimed in claim 1 , wherein the energy saving mode comprises switching off one or more booster cells associated with the plurality of cells for a predefined time period.
4 . The system as claimed in claim 1 , wherein the second set of instructions instructs each said E2 interface to start the set of energy measurements, wherein each said E2 interface transmits the set of energy measurements to the network device via a second predefined interface.
5 . The system as claimed in claim 4 , wherein each said E2 interface further transmits the set of energy measurements to the second set of instructions, wherein the second set of instructions further is configured to transmit feedback of the set of energy measurements to the first set of instructions via the second predefined interface.
6 . The system as claimed in claim 1 , wherein the ML engine configures the one or more booster cells in the energy saving mode to move the one or more first computing devices and the one or more mobile devices associated with the one or more booster cells to other cells, wherein the one or more booster cells are configured to stop receiving new one or more first computing devices and new one or more mobile devices.
7 . The system as claimed in claim 1 , wherein the Near-RT RIC is coupled to the Open radio access network Distributed Unit (O-DU), wherein the O-DU further coupled to an Open radio access network Central Unit Control Plane (O-CU-CP) and an Open radio access network Central Unit User Plane (O-CU-UP) and a User Plane Function (UPF).
8 . The system as claimed in claim 1 , wherein the network device is a System on Chip (SoC) system equipped with a Micro-Services Architecture (MSA) having a plurality of microservices to support portability.
9 . The system as claimed in claim 1 , wherein the network device is modular and flexible to accommodate any kind of changes.
10 . The system as claimed in claim 1 , wherein the network device is equipped with an ML based prediction engine configured to predict energy consumption in the heterogenous network.
11 . The system as claimed in claim 1 , wherein the network device is remotely monitored.
12 . The system as claimed in claim 1 , wherein the network device de-activates the energy saving mode on the one or more capacity booster cells using the second predefined interface once when the amount of traffic associated with the heterogenous network increases above the predefined threshold, wherein the network device further informs one or more neighboring cells that the energy saving mode has been deactivated.
13 . A network device for saving energy in a heterogenous network, said network device comprising: a Non-Real Time Radio Intelligent Controller (Non-RT RIC), the Non-RT RIC further communicatively coupled to a Near-Real Time Radio Intelligent Controller (Near-RT RIC), a processor, said processor executes a set of executable instructions that are stored in a memory, upon execution of which, the processor causes the network device to: receive, a set of data packets from one or more first computing devices, said set of data packets pertaining to a set of initial energy saving requirements, wherein the one or more computing devices are associated with a plurality of cells in the heterogenous network, the plurality of cells is further operatively coupled to the network device, said plurality of cells comprising one or more booster cells and one or more candidate cells, wherein the plurality of cells is further communicatively coupled to an open radio access network unit (O-RAN), each said cell having one or more mobile devices associated with said cell; receive, a set of measurements pertaining to an amount of energy consumed by the plurality of cells in the heterogenous network and an amount of traffic in the heterogenous network at a predefined time, wherein the set of measurements is received on execution of a second set of instructions on the Near-RT RIC configured to extract an amount of energy consumed from each emulated (E2) node, wherein each E2 interface is a bidirectional interface associated with an open radio access network and the Near-RT RIC, wherein an open radio access network distributed unit (O-DU) is further associated with the O-RAN unit; extract, by a first set of instructions to be executed on the Non-RT RIC, a first set of attributes based on the set of data packets and the set of measurements received, the first set of attributes pertaining to parameters associated with an optimal amount of energy to be saved in each cell of the heterogenous network and an increase in the amount of traffic in the heterogenous network beyond a predefined threshold; determine, by a machine learning (ML) engine associated with the network device, an amount of energy to be saved in the heterogenous network based on the first set of attributes extracted and a predetermined energy policy definition; and based on the amount of energy determined to be saved, activate the one or more booster cells to an energy saving mode, wherein the first set of instructions are configured to create policies to start a set of energy measurements in the one or more booster cells and the one or more candidate cells irrespective of whether the energy saving mode is switched on or off, wherein the set of energy measurements are stored in a centralized server, and wherein the first set of instructions are further configured to transmit the created policies to start the set of energy measurements to the Near-RT RIC on execution of the second set of instructions through a predefined interface.
14 . The network device as claimed in claim 13 , wherein the ML engine configures the one or more booster cells in the energy saving mode to move the one or more first computing devices and the one or more mobile devices associated with the one or more booster cells to other cells, wherein the one or more booster cells are configured to stop receiving new one or more first computing devices and new one or more mobile devices.
15 . A method for saving energy in a heterogenous network, said method comprising: receiving, by a network device, a set of data packets from one or more first computing devices, said set of data packets pertaining to a set of initial energy saving requirements, wherein the network device is equipped with a Non-Real Time Radio Intelligent Controller (Non-RT RIC) and communicatively coupled to a Near-Real Time Radio Intelligent Controller (Near-RT RIC), wherein the network device is further operatively coupled to a plurality of cells in the heterogenous network, the plurality of cells comprising one or more booster cells and one or more candidate cells, the plurality of cells further communicatively coupled to an open radio access network unit (O-RAN), each said cell having one or more mobile devices associated with said cell; receiving, by the network device, a set of measurements pertaining to an amount of energy consumed by the plurality of cells in the heterogenous network and an amount of traffic in the heterogenous network at a predefined time, wherein the set of measurements are received on execution of a second set of instructions on a Near-RT RIC) configured to extract an amount of energy consumed from each E2 interface, wherein each said E2 interface is a bidirectional interface associated with an open radio access network distributed and the Near-RT RIC, wherein an open radio access network distributed unit (O-DU) is further associated with the O-RAN unit; extracting, by a first set of instructions to be executed on the Non-RT RIC, a first set of attributes based on the set of data packets and the set of measurements received, the first set of attributes pertaining to parameters associated with an optimal amount of energy to be saved in each cell of the heterogenous network and an increase in the amount of traffic in the heterogenous network beyond a predefined threshold; determining, by a machine learning (ML) engine associated with the network device, an amount of energy to be saved in the heterogenous network based on the first set of attributes extracted and a predetermined energy policy definition; and based on the amount of energy determined to be saved, switching, by the network device, the one or more booster cells to an energy saving mode, wherein the first set of instructions are configured to create policies to start a set of energy measurements in the one or more booster cells and the one or more candidate cells irrespective of whether the energy saving mode is switched on or off, wherein the set of energy measurements are stored in a centralized server, and wherein the first set of instructions are further configured to transmit the created policies to start the set of energy measurements to the Near-RT RIC on execution of the second set of instructions through a predefined interface.
16 . The method as claimed in claim 15 , wherein the method further comprises: switching off one or more booster cells associated with the plurality of cells for a predefined time period during the energy saving mode.
17 . The system as claimed in claim 1 , wherein the processor further causes the network device to: upgrade a candidate cell from the one or more candidate cells to a booster cell upon determining that the amount of traffic in the heterogenous network has increased beyond a second predefined threshold.
18 . The system as claimed in claim 1 , further comprising: a cloud-based data lake, communicatively coupled to the network device, wherein the processor further causes the network device to deposit the set of measurements and the set of data packets into the cloud-based data lake, and wherein the ML engine is further configured to process data in the cloud-based data lake to perform predictive maintenance for the heterogenous network.
19 . The method as claimed in claim 15 , further comprising: upgrading a candidate cell from the one or more candidate cells to a booster cell upon determining that the amount of traffic in the heterogenous network has increased beyond a second predefined threshold.
20 . The method as claimed in claim 15 , further comprising: depositing the set of measurements and the set of data packets into a cloud-based data lake; and processing, by the ML engine, data in the cloud-based data lake to perform predictive maintenance for the heterogenous network.

Description

FIELD OF INVENTION The embodiments of the present disclosure generally relate to energy saving technology. More particularly, the present disclosure relates to systems and methods for saving energy using r-Apps and x-Apps in an Open Radio Access Network (O-RAN). BACKGROUND OF THE INVENTION The following description of related art is intended to provide background information pertaining to the field of the disclosure. This section may include certain aspects of the art that may be related to various features of the present disclosure. However, it should be appreciated that this section be used only to enhance the understanding of the reader with respect to the present disclosure, and not as admissions of prior art. In general, Third Generation Partnership Project (3GPP) may be in a process of defining energy saving features for Next Generation Node Bs (gNBs). According to 3GPP a cell or a network element or network function may be on one of these two states such as not-energy-saving state and energy-saving state, with respect to energy saving. When the cell is in energy saving state, the cell may need candidate cells to pick up the load. However, the cell in energy-saving state should not cause coverage holes or create undue load on the surrounding cells. Further, all traffic on the respective cell may be expected to be drained to other overlaid/umbrella candidate cells before the cell moves to energy-saving state. One typical scenario of energy saving is to switch off capacity booster cells when the traffic demand is low, and re-activated them on a need basis. The energy saving consists of two scenarios where the capacity booster cell gNB is fully or partially overlaid by the candidate cell(s). Further, energy saving activation procedure and energy saving deactivation procedure may be initiated in different ways such as centralized energy saving solution and distributed energy saving solution. Currently, energy saving methods provide basic tool to move a cell or a network element or network function in to either not-energy-saving state or energy-saving state. However, current energy saving methods may not have the capability to learn dynamic behaviour of network and adapt according to the dynamic behaviour of network. Energy consumption is one of major contribution of Operating Expenditures (OPEX) for network operators to operate a network. Further, introduction of Fifth Generation (5G) may have more gNBs for coverage and capacity requirement, This may further increase the energy consumption of future network deployments. Further, the network operators may aim at decreasing power consumption in 5G networks to lower their operational expense with energy saving management solutions. With the upcoming deployment of large number of gNBs, e.g., small base stations with massive Multiple Input Multiple Output (MIMO) in high-band, energy saving may need to be expedited and which in turn may be challenging. Management of 5G networks may contribute to energy saving by reducing energy consumption of 5G networks, while maintaining coverage, capacity and quality of service. Network Operators may determine the permitted impact on coverage, capacity and quality of service decision. Further, by reducing power consumption of the 5G networks it may be possible to minimize negative impact on environment. Therefore, there is a need in the art to provide systems and methods that can overcome the abovementioned shortcomings of the existing prior art without any coverage or Quality of Service (QoS) loss during the 5G network operation. OBJECTS OF THE PRESENT DISCLOSURE Some of the objects of the present disclosure, which at least one embodiment herein satisfies are as listed herein below. An object of the present disclosure is to provide an efficient and reliable systems and methods for saving energy in a network. An object of the present disclosure is to provide systems and methods for saving energy using r-Apps and x-Apps in an Open Radio Access Network (O-RAN). An object of the present disclosure is to enable the RAN architecture to include energy saving feature as a service using r-Apps and x-Apps in a unified manner into the network which can have multi-vendor O-RAN nodes. An object of the present disclosure is to enable the r-Apps and x-Apps to work across technologies, multiple vendors and types of RAN nodes (Macro, Micro, Pico etc) for saving the energy in the network. An object of the present disclosure is to guide a cell or a network element or network function using RAN based approach, to activate/de-activate the energy saving mode. An object of the present disclosure is to enhance overall efficiency of activating/de-activating of the energy saving mode using machine learning techniques to learn dynamic behaviour or network and guide a cell or a network element or network function to activate/de-activate the energy saving mode. An object of the present disclosure is to enable Long Term Evolution (LTE) and New Radio (NR