THRESHOLD CONFIGURATION

TECHNICAL FIELD

The present disclosure relates to wireless communications, and in particular, to

BACKGROUND

The Third Generation Partnership Project (3GPP) has developed and is developing standards for Fourth Generation (4G) (also referred to as Long Term Evolution (LTE)) and Fifth Generation (5G) (also referred to as New Radio (NR)) wireless communication systems. Such systems provide, among other features, broadband communication between network nodes, such as base stations, and mobile wireless devices (WD), as well as communication between network nodes and between WDs.

With respect to 5G networks, network densification and Massive Multiple- Input Multiple-Output (M-MIMO) transmissions are key enablers for achieving high user throughput. Network densification may result from deployment of multiple nodes (e.g., small cell size 5G Nodes) which may improve network capacity. Further, network densification may be associated with an increase of power consumption (e.g., for achieving an increase throughput) when compared to networks that do not use network densification. In addition, in densely populated networks, overlaid cells are deployed for capacity enhancements, and Cell Sleep Mode may be used. The main benefit of the Cell Sleep Mode is automatic energy saving. When Cell Sleep Mode is used, the overlaid capacity cells can detect low traffic conditions by themselves and turn themselves off to save energy, e.g., with confirmed support from underlaid coverage cells. The underlaid coverage cell may monitor traffic conditions to turn on the sleeping cells (i.e., one or more cells for which Cell Sleep Mode is activated/turned on). When an overlaid capacity cell is turned off, a traffic load existing in the cell is off-loaded to overlaid coverage cells (which may be configured, e.g., to handle the off-loaded traffic). FIG. 1 shows an example of typical coverage and capacity cells in a network. More specifically, at least two cells are shown (i.e., A and B). Cell A may include (i.e., be overlaid with) cells C, D, and/or E (and/or cell B). Cell B may include (i.e., be overlaid with) cells E, F, and G. Some cells shown may be basic coverage cells, and others may be capacity cells. The Cell Sleep Mode feature offers a method for the capacity cells to automatically go into (i.e., activate) an energy saving mode, e.g., when the capacity cells are underutilized such as during low or no traffic conditions. Services may be automatically resumed at the capacity cells, e.g., when traffic demand resumes at the coverage cells.

A radio of a coverage cell may consume more power than a radio of a capacity cell when the coverage and capacity cells are under the same traffic load condition. Sleep and wake-up thresholds (e.g., sleep and wake-up predetermined thresholds) may be used to prevent traffic load or power consumption (e.g., that exceeds a predetermine load/power threshold) on the coverage cells.

FIG. 2 shows an example of how typical cells may behave differently with respect to energy consumption. Not all cells behave the same way with respect to energy consumption and KPIs. In other words, it is difficult to operate cells using a single threshold on KPIs. FIG. 3 shows an example typical MIMO and cell sleep times and consumed energy. When MIMO sleep time is decreased, consumed energy initially decreases but increases immediately when cell sleep time increases. Put differently, decreasing MIMO sleep time does not necessarily decrease consumed energy.

In sum, existing cell configurations and thresholds for cell sleep mode do not adequately correspond at least to changes in connections associated to cells and resources usage, which may result in inefficient management of energy in the network.

SUMMARY

Some embodiments advantageously provide methods, systems, and apparatuses for configuration of thresholds associated with Cell Sleep Mode. In some embodiments, the threshold configuration is an Artificial Intelligence (Al) based dynamic Cell Sleep Mode threshold configuration which may be based on WD and/or network behavior. The configuration may be determined for optimum energy saving (i.e., to achieve a predetermined energy target value).

In one or more embodiments, energy gains may be achieved using the Al based dynamic parameter thresholds configuration which may include a prediction for a Cell Sleep Mode process for one or more cells such as coverage cells, capacity cells, etc. Dynamic parameter thresholds may be inferred (i.e., determined), e.g., considering network performance and/or user experience, to obtain a predetermined energy/power consumption without network performance degradation.

In an embodiment, a causal inference of parameter threshold dynamicity is made/determined, e.g., to achieve a predetermined energy saving target value.

In another embodiment, a machine learning (ML) method may be used as part of the Al based dynamic Cell Sleep Mode configuration parameter thresholds determination. The ML method may be used as quantitative method of evaluating energy savings compared to previous trends.

In some embodiments, when checking a cell level configuration hypothesis in an operator network, an improvement in sleep time and energy consumed (when compared to typical systems) may be obtained. In some other embodiments, the predetermined cost associated with OPEX may be achieved, e.g., by allowing CSPs to cut expenses on power consumption.

In some other embodiments, software applications such as r-Apps and/or x- Apps may be created and/or determined and/or provided for 3GPP-NWDAF (network data analytics function) and open radio access network (O-RAN) based cloud native computing foundation (CNCF) for intelligent automation platform and/or to enable/provide a network with advanced energy saving algorithms. The software applications may be based on (i.e., receive, and perform determinations based on) performance events.

In one embodiment, improved spectral efficiency with reduced interference is provided. More specifically, maintenance of systems may be reduced (when compared to typical systems). Manual intervention to monitor and tweak the parameters may be reduced as automatic inference from machine learning models for optimal parameter thresholds definition can be performed. In another embodiment, once overlaid capacity cells are turned off (i.e., deactivated, disabled, not serving WDs), e.g., for energy saving reasons, cell specific reference signal (CRS) interference in the network may be reduced (when compared to capacity cells that are not turned off). Therefore, the throughput of WDs can be improved.

In some embodiments, capacity cells may be dynamically and/or automatically turned on and/or off, e.g., based on current traffic load. Turning cells on/off may reduce power consumption and inter-cell interference. In some other embodiments, use of the Cell Sleep Mode features may reduce operational expenditures (OPEX).

According to one aspect, a network node configured to operate in a wireless communication network using a first cell and a second cell is described. The network node includes processing circuitry configured to: determine, using artificial intelligence, a plurality of dynamic configuration thresholds associated with at least one of the first cell and the second cell, where the plurality of dynamic configuration thresholds is determined based on at least one parameter; determine a mode of cell operation of the second cell based at least in part on the determined plurality of dynamic configuration thresholds; and cause the second cell to activate the mode of cell operation.

In some embodiments, the processing circuitry is further configured to perform a correlation analysis by determining a plurality of key performance indicators (KPIs) that are associated with at least one of the first and second cells and correlate to a predetermined energy consumption. Correlation analysis is usable to determine the plurality of dynamic configuration thresholds.

In some other embodiments, the processing circuitry is further configured to determine a cell sleep opportunity associated with the second cell; and select the at least one parameter based on the determined cell sleep opportunity.

In one embodiment, the processing circuitry is further configured to perform a validation of the plurality of dynamic configuration thresholds associated with the second cell based on the determined cell sleep opportunity and the selected at least one parameter associated with the first cell.

In another embodiment, the mode of operation includes at least a first mode of operation, and the processing circuitry is further configured to forecast a first energy consumption of the second cell using the first mode of operation and second energy consumption of the second cell not using the first mode of operation.

In some embodiments, the first and second energy consumptions are forecasted using a neural basis expansion analysis for interpretable time series (NBEATS) model.

In some other embodiments, the processing circuitry is further configured to determine whether at least one of the pluralities of dynamic configuration thresholds associated with the second cell is recommended to determine the mode of cell operation at least by comparing the first and second energy consumptions.

In one embodiment, wherein at least one parameter includes at least one of a quantity of radio resource control (RRC) connections, a physical resource block (PRB) utilization, and a data volume.

In another embodiment, the mode of cell operation is one of sleep mode and a wakeup mode.

In some embodiments, the first cell is a coverage cell, and the second cell is a capacity cell.

According to another aspect, a method in a network node configured to operate in a wireless communication network using a first cell and a second cell is described. The method includes determining, using artificial intelligence, a plurality of dynamic configuration thresholds associated with at least one of the first cell and the second cell, where the plurality of dynamic configuration thresholds is determined based on at least one parameter; determining a mode of cell operation of the second cell based at least in part on the determined plurality of dynamic configuration thresholds; and causing the second cell to activate the mode of cell operation.

In some embodiments, the method further includes performing a correlation analysis by determining a plurality of key performance indicators (KPIs) that are associated with at least one of the first and second cells and correlate to a predetermined energy consumption. Correlation analysis is usable to determine the plurality of dynamic configuration thresholds.

In some other embodiments, the method further includes determining a cell sleep opportunity associated with the second cell; and selecting the at least one parameter based on the determined cell sleep opportunity. In one embodiment the method further includes performing a validation of the plurality of dynamic configuration thresholds associated with the second cell based on the determined cell sleep opportunity and the selected at least one parameter associated with the first cell.

In another embodiment, the mode of operation includes at least a first mode of operation, and the method further includes forecasting a first energy consumption of the second cell using the first mode of operation and second energy consumption of the second cell not using the first mode of operation.

In some embodiments, the first and second energy consumptions are forecasted using a neural basis expansion analysis for interpretable time series (NBEATS) model.

In some other embodiments, the method further includes determining whether at least one of the plurality of dynamic configuration thresholds associated with the second cell is recommended to determine the mode of cell operation at least by comparing the first and second energy consumptions.

In an embodiment, the at least one parameter includes least one of a quantity of radio resource control (RRC) connections, a physical resource bloc (PRB) utilization, and a data volume.

In another embodiment, the mode of cell operation is one of a sleep mode and a wakeup mode.

In some embodiments, the first cell is a coverage cell, and the second cell is a capacity cell.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete understanding of the present embodiments, and the attendant advantages and features thereof, will be more readily understood by reference to the following detailed description when considered in conjunction with the accompanying drawings wherein:

FIG. 1 shows an example of typical coverage and capacity cells in a network;

FIG. 2 shows an example of how typical cells may behave differently with respect to energy consumption; FIG. 3 shows an example typical MIMO and cell sleep times and consumed energy;

FIG. 4 is a schematic diagram of an exemplary network architecture illustrating a communication system connected via an intermediate network to a host computer according to the principles in the present disclosure;

FIG. 5 is a block diagram of a host computer communicating via a network node with a wireless device over an at least partially wireless connection according to some embodiments of the present disclosure;

FIG. 6 is a flowchart illustrating exemplary methods implemented in a communication system including a host computer, a network node and a wireless device for executing a client application at a wireless device according to some embodiments of the present disclosure;

FIG. 7 is a flowchart illustrating exemplary methods implemented in a communication system including a host computer, a network node and a wireless device for receiving user data at a wireless device according to some embodiments of the present disclosure;

FIG. 8 is a flowchart illustrating exemplary methods implemented in a communication system including a host computer, a network node and a wireless device for receiving user data from the wireless device at a host computer according to some embodiments of the present disclosure;

FIG. 9 is a flowchart illustrating exemplary methods implemented in a communication system including a host computer, a network node and a wireless device for receiving user data at a host computer according to some embodiments of the present disclosure;

FIG. 10 is a flowchart of an exemplary process in a network node according to some embodiments of the present disclosure;

FIG. 11 shows example sleep thresholds according to some embodiments of the present disclosure;

FIG. 12 shows other example wake-up thresholds according to some embodiments of the present disclosure;

FIG. 13 shows example thresholds according to some embodiments of the present disclosure; FIG. 14 shows a first set of example thresholds per site according to some embodiments of the present disclosure;

FIG. 15 shows a second set of example thresholds per site according to some embodiments of the present disclosure;

FIG. 16 shows example features that are scored by importance according to some embodiments of the present disclosure;

FIG. 17 shows an example process for recommending thresholds according to some embodiments of the present disclosure;

FIG. 18 shows another example process for recommending thresholds according to some embodiments of the present disclosure; and

FIG. 19 shows an example objective framework according to some embodiments of the present disclosure.

DETAILED DESCRIPTION

Before describing in detail exemplary embodiments, it is noted that the embodiments reside primarily in combinations of apparatus components and processing steps related to configuration of thresholds associated with Cell Sleep Mode. In some embodiments, the threshold configuration is an Artificial Intelligence (Al) based dynamic Cell Sleep Mode threshold configuration which may be based on WD and/or network behavior. The configuration may be determined for optimum energy saving (i.e., to achieve a predetermined energy target value).

Accordingly, components have been represented where appropriate by conventional symbols in the drawings, showing only those specific details that are pertinent to understanding the embodiments so as not to obscure the disclosure with details that will be readily apparent to those of ordinary skill in the art having the benefit of the description herein. Like numbers refer to like elements throughout the description.

As used herein, relational terms, such as “first” and “second,” “top” and “bottom,” and the like, may be used solely to distinguish one entity or element from another entity or element without necessarily requiring or implying any physical or logical relationship or order between such entities or elements. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the concepts described herein. As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises,” “comprising,” “includes” and/or “including” when used herein, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

In embodiments described herein, the joining term, “in communication with” and the like, may be used to indicate electrical or data communication, which may be accomplished by physical contact, induction, electromagnetic radiation, radio signaling, infrared signaling or optical signaling, for example. One having ordinary skill in the art will appreciate that multiple components may interoperate and modifications and variations are possible of achieving the electrical and data communication.

In some embodiments described herein, the term “coupled,” “connected,” and the like, may be used herein to indicate a connection, although not necessarily directly, and may include wired and/or wireless connections.

The term “network node” (NN) used herein can be any kind of network node comprised in a radio network which may further comprise any of base station (BS), radio base station, base transceiver station (BTS), base station controller (BSC), radio network controller (RNC), g Node B (gNB), evolved Node B (eNB or eNodeB), Node B, multi- standard radio (MSR) radio node such as MSR BS, multi-cell/multicast coordination entity (MCE), integrated access and backhaul (IAB) node, relay node, donor node controlling relay, radio access point (AP), transmission points, transmission nodes, Remote Radio Unit (RRU) Remote Radio Head (RRH), a core network node (e.g., mobile management entity (MME), self-organizing network (SON) node, a coordinating node, positioning node, MDT node, etc.), an external node (e.g., 3rd party node, a node external to the current network), nodes in distributed antenna system (DAS), a spectrum access system (SAS) node, an element management system (EMS), etc. The network node may also comprise test equipment. The term “radio node” used herein may be used to also denote a wireless device (WD) such as a wireless device (WD) or a radio network node. A network node may be configured to communicate with one or more wireless devices (WDs) using one or more cells. A cell may include one or more types of cells such as a capacity cell, a coverage cell, etc. The term cell, however, is not limited as such and may include any other type of cell.

In some embodiments, the non-limiting terms wireless device (WD) or a user equipment (UE) are used interchangeably. The WD herein can be any type of wireless device capable of communicating with a network node or another WD over radio signals, such as wireless device (WD). The WD may also be a radio communication device, target device, device to device (D2D) WD, machine type WD or WD capable of machine to machine communication (M2M), low-cost and/or low-complexity WD, a sensor equipped with WD, Tablet, mobile terminals, smart phone, laptop embedded equipped (LEE), laptop mounted equipment (LME), USB dongles, Customer Premises Equipment (CPE), an Internet of Things (loT) device, or a Narrowband loT (NB-IOT) device, etc.

Also, in some embodiments the generic term “radio network node” is used. It can be any kind of a radio network node which may comprise any of base station, radio base station, base transceiver station, base station controller, network controller, RNC, evolved Node B (eNB), Node B, gNB, Multi-cell/multicast Coordination Entity (MCE), IAB node, relay node, access point, radio access point, Remote Radio Unit (RRU) Remote Radio Head (RRH).

Note that although terminology from one particular wireless system, such as, for example, 3GPP LTE and/or New Radio (NR), may be used in this disclosure, this should not be seen as limiting the scope of the disclosure to only the aforementioned system. Other wireless systems, including without limitation Wide Band Code Division Multiple Access (WCDMA), Worldwide Interoperability for Microwave Access (WiMax), Ultra Mobile Broadband (UMB) and Global System for Mobile Communications (GSM), may also benefit from exploiting the ideas covered within this disclosure.

Note further, that functions described herein as being performed by a wireless device or a network node may be distributed over a plurality of wireless devices and/or network nodes. In other words, it is contemplated that the functions of the network node and wireless device described herein are not limited to performance by a single physical device and, in fact, can be distributed among several physical devices.

Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs. It will be further understood that terms used herein should be interpreted as having a meaning that is consistent with their meaning in the context of this specification and the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.

Referring again to the drawing figures, in which like elements are referred to by like reference numerals, there is shown in FIG. 4 a schematic diagram of a communication system 10, according to an embodiment, such as a 3GPP-type cellular network that may support standards such as LTE and/or NR (5G), which comprises an access network 12, such as a radio access network, and a core network 14. The access network 12 comprises a plurality of network nodes 16a, 16b, 16c (referred to collectively as network nodes 16), such as NBs, eNBs, gNBs or other types of wireless access points, each defining (e.g., transmitting/receiving using, communicating using, activating, deactivating, etc.) one or more cells such as a corresponding coverage cell 18a, 18b, 18c (referred to collectively as coverage cells 18) and/or a corresponding capacity cell 19a, 19b, 19c (referred to collectively as capacity cells 19). Although coverage and capacity cells 18, 19 are described, the term cell may refer to other types of cells (e.g., Primary Cell, Secondary Cell, etc.) Further, although each capacity cell 19 is shown within a coverage cell 18, capacity cells 19 (and/or coverage cells 18) are not limited as such and may be partially within coverage cell 18 or entirely outside of (i.e., not overlapping with) coverage cell 18. In addition, capacity cells 19 are shown as being smaller than coverage cells 18 but are not limited as such and may be equal to or larger than coverage cells 18.

Each network node 16a, 16b, 16c is connectable to the core network 14 over a wired or wireless connection 20. A first wireless device (WD) 22a located in coverage area 18a is configured to wirelessly connect to, or be paged by, the corresponding network node 16a. A second WD 22b in coverage area 18b is wirelessly connectable to the corresponding network node 16b. While a plurality of WDs 22a, 22b (collectively referred to as wireless devices 22) are illustrated in this example, the disclosed embodiments are equally applicable to a situation where a sole WD is in the coverage area or where a sole WD is connecting to the corresponding network node 16. Note that although only two WDs 22 and three network nodes 16 are shown for convenience, the communication system may include many more WDs 22 and network nodes 16.

Also, it is contemplated that a WD 22 can be in simultaneous communication and/or configured to separately communicate with more than one network node 16 and more than one type of network node 16. For example, a WD 22 can have dual connectivity with a network node 16 that supports LTE and the same or a different network node 16 that supports NR. As an example, WD 22 can be in communication with an eNB for LTE/E-UTRAN and a gNB for NR/NG-RAN.

The communication system 10 may itself be connected to a host computer 24, which may be embodied in the hardware and/or software of a standalone server, a cloud-implemented server, a distributed server or as processing resources in a server farm. The host computer 24 may be under the ownership or control of a service provider, or may be operated by the service provider or on behalf of the service provider. The connections 26, 28 between the communication system 10 and the host computer 24 may extend directly from the core network 14 to the host computer 24 or may extend via an optional intermediate network 30. The intermediate network 30 may be one of, or a combination of more than one of, a public, private or hosted network. The intermediate network 30, if any, may be a backbone network or the Internet. In some embodiments, the intermediate network 30 may comprise two or more sub-networks (not shown).

The communication system of FIG. 4 as a whole enables connectivity between one of the connected WDs 22a, 22b and the host computer 24. The connectivity may be described as an over-the-top (OTT) connection. The host computer 24 and the connected WDs 22a, 22b are configured to communicate data and/or signaling via the OTT connection, using the access network 12, the core network 14, any intermediate network 30 and possible further infrastructure (not shown) as intermediaries. The OTT connection may be transparent in the sense that at least some of the participating communication devices through which the OTT connection passes are unaware of routing of uplink and downlink communications. For example, a network node 16 may not or need not be informed about the past routing of an incoming downlink communication with data originating from a host computer 24 to be forwarded (e.g., handed over) to a connected WD 22a. Similarly, the network node 16 need not be aware of the future routing of an outgoing uplink communication originating from the WD 22a towards the host computer 24.

A network node 16 is configured to include a NN threshold unit 32 which is configured to perform any step and/or task and/or process and/or method and/or feature described in the present disclosure, e.g., determine, using artificial intelligence, a plurality of dynamic configuration thresholds. A wireless device 22 is configured to include a WD threshold unit 34 which is configured to perform any step and/or task and/or process and/or method and/or feature described in the present disclosure.

Example implementations, in accordance with an embodiment, of the WD 22, network node 16 and host computer 24 discussed in the preceding paragraphs will now be described with reference to FIG. 5. In a communication system 10, a host computer 24 comprises hardware (HW) 38 including a communication interface 40 configured to set up and maintain a wired or wireless connection with an interface of a different communication device of the communication system 10. The host computer 24 further comprises processing circuitry 42, which may have storage and/or processing capabilities. The processing circuitry 42 may include a processor 44 and memory 46. In particular, in addition to or instead of a processor, such as a central processing unit, and memory, the processing circuitry 42 may comprise integrated circuitry for processing and/or control, e.g., one or more processors and/or processor cores and/or FPGAs (Field Programmable Gate Array) and/or ASICs (Application Specific Integrated Circuitry) adapted to execute instructions. The processor 44 may be configured to access (e.g., write to and/or read from) memory 46, which may comprise any kind of volatile and/or nonvolatile memory, e.g., cache and/or buffer memory and/or RAM (Random Access Memory) and/or ROM (Read- Only Memory) and/or optical memory and/or EPROM (Erasable Programmable Read-Only Memory).

Processing circuitry 42 may be configured to control any of the methods and/or processes described herein and/or to cause such methods, and/or processes to be performed, e.g., by host computer 24. Processor 44 corresponds to one or more processors 44 for performing host computer 24 functions described herein. The host computer 24 includes memory 46 that is configured to store data, programmatic software code and/or other information described herein. In some embodiments, the software 48 and/or the host application 50 may include instructions that, when executed by the processor 44 and/or processing circuitry 42, causes the processor 44 and/or processing circuitry 42 to perform the processes described herein with respect to host computer 24. The instructions may be software associated with the host computer 24.

The software 48 may be executable by the processing circuitry 42. The software 48 includes a host application 50. The host application 50 may be operable to provide a service to a remote user, such as a WD 22 connecting via an OTT connection 52 terminating at the WD 22 and the host computer 24. In providing the service to the remote user, the host application 50 may provide user data which is transmitted using the OTT connection 52. The “user data” may be data and information described herein as implementing the described functionality. In one embodiment, the host computer 24 may be configured for providing control and functionality to a service provider and may be operated by the service provider or on behalf of the service provider. The processing circuitry 42 of the host computer 24 may enable the host computer 24 to observe, monitor, control, transmit to and/or receive from the network node 16 and or the wireless device 22. The processing circuitry 42 of the host computer 24 may include a host threshold unit 54 configured to enable the service provider to observe/monitor/ control/transmit to/receive from the network node 16 and or the wireless device 22.

The communication system 10 further includes a network node 16 provided in a communication system 10 and including hardware 58 enabling it to communicate with the host computer 24 and with the WD 22. The hardware 58 may include a communication interface 60 for setting up and maintaining a wired or wireless connection with an interface of a different communication device of the communication system 10, as well as a radio interface 62 for setting up and maintaining at least a wireless connection 64 with a WD 22 located in a coverage area 18 served by the network node 16. The radio interface 62 may be formed as or may include, for example, one or more RF transmitters, one or more RF receivers, and/or one or more RF transceivers. The communication interface 60 may be configured to facilitate a connection 66 to the host computer 24. The connection 66 may be direct or it may pass through a core network 14 of the communication system 10 and/or through one or more intermediate networks 30 outside the communication system 10.

In the embodiment shown, the hardware 58 of the network node 16 further includes processing circuitry 68. The processing circuitry 68 may include a processor 70 and a memory 72. In particular, in addition to or instead of a processor, such as a central processing unit, and memory, the processing circuitry 68 may comprise integrated circuitry for processing and/or control, e.g., one or more processors and/or processor cores and/or FPGAs (Field Programmable Gate Array) and/or ASICs (Application Specific Integrated Circuitry) adapted to execute instructions. The processor 70 may be configured to access (e.g., write to and/or read from) the memory 72, which may comprise any kind of volatile and/or nonvolatile memory, e.g., cache and/or buffer memory and/or RAM (Random Access Memory) and/or ROM (Read-Only Memory) and/or optical memory and/or EPROM (Erasable Programmable Read-Only Memory).

Thus, the network node 16 further has software 74 stored internally in, for example, memory 72, or stored in external memory (e.g., database, storage array, network storage device, etc.) accessible by the network node 16 via an external connection. The software 74 may be executable by the processing circuitry 68. The processing circuitry 68 may be configured to control any of the methods and/or processes described herein and/or to cause such methods, and/or processes to be performed, e.g., by network node 16. Processor 70 corresponds to one or more processors 70 for performing network node 16 functions described herein. The memory 72 is configured to store data, programmatic software code and/or other information described herein. In some embodiments, the software 74 may include instructions that, when executed by the processor 70 and/or processing circuitry 68, causes the processor 70 and/or processing circuitry 68 to perform the processes described herein with respect to network node 16. For example, processing circuitry 68 of the network node 16 may include NN threshold unit 32 configured to perform any step and/or task and/or process and/or method and/or feature described in the present disclosure, e.g., determine, using artificial intelligence, a plurality of dynamic configuration thresholds.

The communication system 10 further includes the WD 22 already referred to. The WD 22 may have hardware 80 that may include a radio interface 82 configured to set up and maintain a wireless connection 64 with a network node 16 serving a coverage area 18 in which the WD 22 is currently located. The radio interface 82 may be formed as or may include, for example, one or more RF transmitters, one or more RF receivers, and/or one or more RF transceivers.

The hardware 80 of the WD 22 further includes processing circuitry 84. The processing circuitry 84 may include a processor 86 and memory 88. In particular, in addition to or instead of a processor, such as a central processing unit, and memory, the processing circuitry 84 may comprise integrated circuitry for processing and/or control, e.g., one or more processors and/or processor cores and/or FPGAs (Field Programmable Gate Array) and/or ASICs (Application Specific Integrated Circuitry) adapted to execute instructions. The processor 86 may be configured to access (e.g., write to and/or read from) memory 88, which may comprise any kind of volatile and/or nonvolatile memory, e.g., cache and/or buffer memory and/or RAM (Random Access Memory) and/or ROM (Read-Only Memory) and/or optical memory and/or EPROM (Erasable Programmable Read-Only Memory).

Thus, the WD 22 may further comprise software 90, which is stored in, for example, memory 88 at the WD 22, or stored in external memory (e.g., database, storage array, network storage device, etc.) accessible by the WD 22. The software 90 may be executable by the processing circuitry 84. The software 90 may include a client application 92. The client application 92 may be operable to provide a service to a human or non-human user via the WD 22, with the support of the host computer 24. In the host computer 24, an executing host application 50 may communicate with the executing client application 92 via the OTT connection 52 terminating at the WD 22 and the host computer 24. In providing the service to the user, the client application 92 may receive request data from the host application 50 and provide user data in response to the request data. The OTT connection 52 may transfer both the request data and the user data. The client application 92 may interact with the user to generate the user data that it provides. The processing circuitry 84 may be configured to control any of the methods and/or processes described herein and/or to cause such methods, and/or processes to be performed, e.g., by WD 22. The processor 86 corresponds to one or more processors 86 for performing WD 22 functions described herein. The WD 22 includes memory 88 that is configured to store data, programmatic software code and/or other information described herein. In some embodiments, the software 90 and/or the client application 92 may include instructions that, when executed by the processor 86 and/or processing circuitry 84, causes the processor 86 and/or processing circuitry 84 to perform the processes described herein with respect to WD 22. For example, the processing circuitry 84 of the wireless device 22 may include a WD threshold unit 34 configured to perform any step and/or task and/or process and/or method and/or feature described in the present disclosure.

In some embodiments, the inner workings of the network node 16, WD 22, and host computer 24 may be as shown in FIG. 5 and independently, the surrounding network topology may be that of FIG. 4.

In FIG. 5, the OTT connection 52 has been drawn abstractly to illustrate the communication between the host computer 24 and the wireless device 22 via the network node 16, without explicit reference to any intermediary devices and the precise routing of messages via these devices. Network infrastructure may determine the routing, which it may be configured to hide from the WD 22 or from the service provider operating the host computer 24, or both. While the OTT connection 52 is active, the network infrastructure may further take decisions by which it dynamically changes the routing (e.g., on the basis of load balancing consideration or reconfiguration of the network).

The wireless connection 64 between the WD 22 and the network node 16 is in accordance with the teachings of the embodiments described throughout this disclosure. One or more of the various embodiments improve the performance of OTT services provided to the WD 22 using the OTT connection 52, in which the wireless connection 64 may form the last segment. More precisely, the teachings of some of these embodiments may improve the data rate, latency, and/or power consumption and thereby provide benefits such as reduced user waiting time, relaxed restriction on file size, better responsiveness, extended battery lifetime, etc. In some embodiments, a measurement procedure may be provided for the purpose of monitoring data rate, latency and other factors on which the one or more embodiments improve. There may further be an optional network functionality for reconfiguring the OTT connection 52 between the host computer 24 and WD 22, in response to variations in the measurement results. The measurement procedure and/or the network functionality for reconfiguring the OTT connection 52 may be implemented in the software 48 of the host computer 24 or in the software 90 of the WD 22, or both. In embodiments, sensors (not shown) may be deployed in or in association with communication devices through which the OTT connection 52 passes; the sensors may participate in the measurement procedure by supplying values of the monitored quantities exemplified above, or supplying values of other physical quantities from which software 48, 90 may compute or estimate the monitored quantities. The reconfiguring of the OTT connection 52 may include message format, retransmission settings, preferred routing etc.; the reconfiguring need not affect the network node 16, and it may be unknown or imperceptible to the network node 16. Some such procedures and functionalities may be known and practiced in the art. In certain embodiments, measurements may involve proprietary WD signaling facilitating the host computer’s 24 measurements of throughput, propagation times, latency and the like. In some embodiments, the measurements may be implemented in that the software 48, 90 causes messages to be transmitted, in particular empty or ‘dummy’ messages, using the OTT connection 52 while it monitors propagation times, errors, etc.

Thus, in some embodiments, the host computer 24 includes processing circuitry 42 configured to provide user data and a communication interface 40 that is configured to forward the user data to a cellular network for transmission to the WD 22. In some embodiments, the cellular network also includes the network node 16 with a radio interface 62. In some embodiments, the network node 16 is configured to, and/or the network node’s 16 processing circuitry 68 is configured to perform the functions and/or methods described herein for preparing/initiating/maintaining/supporting/ending a transmission to the WD 22, and/or preparing/terminating/maintaining/supporting/ending in receipt of a transmission from the WD 22. In some embodiments, the host computer 24 includes processing circuitry 42 and a communication interface 40 that is configured to a communication interface 40 configured to receive user data originating from a transmission from a WD 22 to a network node 16. In some embodiments, the WD 22 is configured to, and/or comprises a radio interface 82 and/or processing circuitry 84 configured to perform the functions and/or methods described herein for preparing/initiating/maintaining/supporting/ending a transmission to the network node 16, and/or preparing/terminating/maintaining/supporting/ending in receipt of a transmission from the network node 16.

Although FIGS. 4 and 5 show various “units” such as NN threshold unit 32, and WD threshold unit 34 as being within a respective processor, it is contemplated that these units may be implemented such that a portion of the unit is stored in a corresponding memory within the processing circuitry. In other words, the units may be implemented in hardware or in a combination of hardware and software within the processing circuitry.

FIG. 6 is a flowchart illustrating an exemplary method implemented in a communication system, such as, for example, the communication system of FIGS. 4 and 5, in accordance with one embodiment. The communication system may include a host computer 24, a network node 16 and a WD 22, which may be those described with reference to FIG. 5. In a first step of the method, the host computer 24 provides user data (Block S100). In an optional substep of the first step, the host computer 24 provides the user data by executing a host application, such as, for example, the host application 50 (Block S102). In a second step, the host computer 24 initiates a transmission carrying the user data to the WD 22 (Block S104). In an optional third step, the network node 16 transmits to the WD 22 the user data which was carried in the transmission that the host computer 24 initiated, in accordance with the teachings of the embodiments described throughout this disclosure (Block S106). In an optional fourth step, the WD 22 executes a client application, such as, for example, the client application 92, associated with the host application 50 executed by the host computer 24 (Block S108).

FIG. 7 is a flowchart illustrating an exemplary method implemented in a communication system, such as, for example, the communication system of FIG. 4, in accordance with one embodiment. The communication system may include a host computer 24, a network node 16 and a WD 22, which may be those described with reference to FIGS. 4 and 5. In a first step of the method, the host computer 24 provides user data (Block SI 10). In an optional substep (not shown) the host computer 24 provides the user data by executing a host application, such as, for example, the host application 50. In a second step, the host computer 24 initiates a transmission carrying the user data to the WD 22 (Block SI 12). The transmission may pass via the network node 16, in accordance with the teachings of the embodiments described throughout this disclosure. In an optional third step, the WD 22 receives the user data carried in the transmission (Block SI 14).

FIG. 8 is a flowchart illustrating an exemplary method implemented in a communication system, such as, for example, the communication system of FIG. 4, in accordance with one embodiment. The communication system may include a host computer 24, a network node 16 and a WD 22, which may be those described with reference to FIGS. 4 and 5. In an optional first step of the method, the WD 22 receives input data provided by the host computer 24 (Block SI 16). In an optional substep of the first step, the WD 22 executes the client application 92, which provides the user data in reaction to the received input data provided by the host computer 24 (Block SI 18). Additionally or alternatively, in an optional second step, the WD 22 provides user data (Block S120). In an optional substep of the second step, the WD provides the user data by executing a client application, such as, for example, client application 92 (Block S122). In providing the user data, the executed client application 92 may further consider user input received from the user. Regardless of the specific manner in which the user data was provided, the WD 22 may initiate, in an optional third substep, transmission of the user data to the host computer 24 (Block S124). In a fourth step of the method, the host computer 24 receives the user data transmitted from the WD 22, in accordance with the teachings of the embodiments described throughout this disclosure (Block S126).

FIG. 9 is a flowchart illustrating an exemplary method implemented in a communication system, such as, for example, the communication system of FIG. 4, in accordance with one embodiment. The communication system may include a host computer 24, a network node 16 and a WD 22, which may be those described with reference to FIGS. 4 and 5. In an optional first step of the method, in accordance with the teachings of the embodiments described throughout this disclosure, the network node 16 receives user data from the WD 22 (Block S128). In an optional second step, the network node 16 initiates transmission of the received user data to the host computer 24 (Block SI 30). In a third step, the host computer 24 receives the user data carried in the transmission initiated by the network node 16 (Block SI 32).

FIG. 10 is a flowchart of an exemplary process (i.e., method) in a network node 16. One or more blocks described herein may be performed by one or more elements of network node 16 such as by one or more of processing circuitry 68 (including the NN threshold unit 32), processor 70, radio interface 62 and/or communication interface 60. Network node 16 such as via processing circuitry 68 and/or processor 70 and/or radio interface 62 and/or communication interface 60 is configured to determine (Block SI 34), using artificial intelligence, a plurality of dynamic configuration thresholds associated with at least one of the first cell and the second cell. The plurality of dynamic configuration thresholds are determined based on at least one parameter. Network node 16 such as via processing circuitry 68 and/or processor 70 and/or radio interface 62 and/or communication interface 60 is configured to determine (Block SI 36) a mode of cell operation of the second cell based at least in part on the determined plurality of dynamic configuration thresholds; and cause (Block SI 38) the second cell to activate the mode of cell operation.

In some embodiments, the method further includes performing a correlation analysis by determining a plurality of key performance indicators (KPIs) that are associated with at least one of the first and second cells and correlate to a predetermined energy consumption. The correlation analysis is usable to determine the plurality of dynamic configuration thresholds.

In some other embodiments, the method further includes determining a cell sleep opportunity associated with the second cell; and selecting the at least one parameter based on the determined cell sleep opportunity.

In one embodiment the method further includes performing a validation of the plurality of dynamic configuration thresholds associated with the second cell based on the determined cell sleep opportunity and the selected at least one parameter associated with the first cell. In another embodiment, the mode of operation includes at least a first mode of operation, and the method further includes forecasting a first energy consumption of the second cell using the first mode of operation and second energy consumption of the second cell not using the first mode of operation.

In some embodiments, the first and second energy consumptions are forecasted using a neural basis expansion analysis for interpretable time series, NBEATS, model.

In some other embodiments, he method further includes determining whether at least one of the pluralities of dynamic configuration thresholds associated with the second cell is recommended to determine the mode of cell operation at least by comparing the first and second energy consumptions.

In an embodiment, the at least one parameter includes least one of a quantity of radio resource control (RRC) connections, a physical resource bloc (PRB) utilization, and a data volume.

In another embodiment, the mode of cell operation is one of a sleep mode and a wakeup mode.

In some embodiments, the first cell is a coverage cell 18, and the second cell is a capacity cell 19.

Having described the general process flow of arrangements of the disclosure and having provided examples of hardware and software arrangements for implementing the processes and functions of the disclosure, the sections below provide details and examples of arrangements for configuration of thresholds associated with Cell Sleep Mode. In some embodiments, the threshold configuration is an Artificial Intelligence (Al) based dynamic Cell Sleep Mode threshold configuration which may be based on WD and/or network behavior. The configuration may be determined for optimum energy saving (i.e., to achieve a predetermined energy target value).

In one or more embodiments, a capacity cell 19 may be configured to (i.e., network node 16 associated with the capacity cell 19 may be configured to) look for a cell sleep opportunity based on its PRB and RRC parameter thresholds. The capacity cell 19 may be configured to (i.e., network node 16 associated with the capacity cell 19 may be configured to) send a cell sleep request to a coverage cell 18. In some embodiments, the coverage cell 18 may be configured to (i.e., network node 16 associated with the coverage cell 18 may be configured to) check for “to be added” PRB & RRC values from the capacity cell 19. In some other embodiments, the coverage cell 18 may be configured to (i.e., network node 16 associated with the coverage cell 18 may be configured to) validate the new RRC and PRB values (e.g., after addition of capacity cell 19 - RRC and PRB) against the coverage cell 18 based RRC and PRB thresholds. If the validation is satisfied, the capacity cell 19 is allowed (i.e., triggered) to sleep.

In one or more embodiments, e.g., in a low traffic scenario, whether a coverage cell 18 has the capacity to accept traffic offload from the requesting capacity cell 19 may be determined. For example:

• A capacity cell 19 sends a sleep start request to identified coverage cells 18 with wake-up thresholds and current traffic load (i.e., quantity/number of RRC connections and downlink PRB utilization). The thresholds may be: o CellSleepFunction.covCellDIPrbWakeUpThreshold; and o CellSleepFunction. covCellRrcConnW akeUpThreshold.

If the number/quantity of coverage cells 18 is larger than 1 , a half of current traffic load may be sent to the coverage cells 18.

• A coverage cell 18 decides whether to accept/reject sleep start request from the capacity cell 19. When the following conditions are satisfied, coverage cell 18 accepts sleep start request from capacity cell 19: o (number of RRC connection (capacity cell) + number of RRC connection (coverage cell)) < CellSleepFunction.covCellRrcConnWakeUpThreshold; o (DL PRB utilization (capacity cell) + DL PRB utilization (coverage cell)) < CellSleepFunction.covCellDIPrbWakeUpThresholCSM Threshold Mechanism, where RRC is radio resource control, and DL PRB is downlink physical resource block.

FIG. 11 shows example sleep thresholds. More specifically, two cells (e.g., coverage cell 18 (i.e., a first cell) and a capacity cell 19 (i.e., a second cell). The coverage cell 18 corresponds to a first network node 16a, and the capacity cell 19 corresponds to a second network node 16b. The first and second network nodes 16a, 16b may be different network nodes 16 or the same network node 16. Each one of the coverage and capacity cells 18, 19 (i.e., first and second cells) may be configured to serve one or more WDs 22. Downlink PRB usage and active RRC connections are graphed with respect to time. The range for capCellSleepMonitorDurTimer should/may be changed to 1 minute. Typically, the allowed range may be 5...120 minutes, and the default is 15 minutes. Setting the duration timer to minimal value (1 min) allows capacity cell 19 to enter sleep state (e.g., as fast as possible). Normal condition threshold and additional “high-traffic” thresholds with duration control timer on the coverage cell 18 may be attributes, e.g., usable/included in a configuration.

Some of the attributes/thresholds may include:

• covCellDIPrbWakeUpThreshold = DL PRB usage percentage threshold. At least one of the coverage cells 18 exceeds this threshold to wake-up the capacity cell 19.

• covCellDIPrbWakeUpThreshHigh = Downlink PRB usage percentage for high load.

• covCellRrcConnWakeUpThreshold = Minimum RRC connections to wake-up the capacity cell 19.

• covCellRrcConnWakeUpThresHigh = Active RRC connection count for high load.

• covCellWakeUpMonitorDurTimer = Minimum duration for coverage cell to satisfy configured load thresholds before the capacity cell 19 can wake up.

• covCellWakeUpMonitorDurTHigh = Minimum duration for coverage cell to satisfy the configured high traffic detection thresholds before the capacity cell 19 can wake up.

• capCellDIPrbSleepThreshold = DL PRB usage percentage threshold. The capacity cell 19 to be below this threshold to enter sleep state.

• capCellRrcConnSleepThreshold = Number of active RRC connections. The capacity cell 19 must be below this to enter sleep state

• capCellSleepMonitorDurTimer = Minimum duration for cell to satisfy the configured load thresholds before entering sleep state. • Sleep Start.

• Sleep End.

FIG. 12 shows example wake-up thresholds (e.g., as described above). In normal traffic pattern: legacy default values for PRB % and RRC connections may be used; default duration time (e.g., 15 minutes) before sending wakeup request may be used; and both conditions may have to be met to trigger wakeup detection. In high peak traffic pattern: values for PRB % and RRC connections can be higher than normal and appear in peaks; timer reaction (e.g., 15-minute timer reaction) means that for a majority of that time the cell can be overloaded, and WD attach rejections can occur, thereby lowering attach success KPIs. Further wakeup may be detected earlier, where wakeup may be triggered by a condition that monitors whether a traffic load in the coverage cell 18 is increased (e.g., suddenly): this feature may be able to react to extreme traffic changes (e.g., massive events, traffic accidents, disasters, etc.); coverage cell 18 may be able to wake up capacity cells 19 as quickly as possible when sudden traffic increase is discovered/determined.

FIG. 13 shows examples of downlink PRB usage and active RRC connections across time. With respect to downlink PRB usage, two thresholds are shown, i.e., coverage cell downlink PRB wakeup threshold high, and coverage cell downlink wakeup threshold. With respect to active RRC connections, another two thresholds are shown, i.e., coverage cell RRC connection wakeup threshold high, and coverage cell RRC connection wakeup threshold. A wakeup may be detected after a predetermined time, e.g., 2 minutes.

In one or more embodiments, Cell Sleep Mode activation with static parameter definition per capacity and/or coverage cells 19, 18 is used. In some embodiments, the Cell Sleep Mode activation may be irrespective of traffic pattern, node type, clutter and network geospatial characteristics.

In some embodiments, a communication service provider (CSP) (e.g., having a multiband layer-based network deployed such as to cater coverage and capacity related scenarios) may use spectrum bands, e.g., to satisfy the user’s need and make optimum use of scarce resources (i.e., spectrum). With respect to durations (i.e., time intervals) where less user-traffic is sensed in the network (such as by a network node 16), predetermined spectrum bands may be provisioned to support user-traffic. Provisioning spectrum bands may allow switching off radiation of remaining bands. In addition, static parameter thresholds definitions per cell (e.g., to force cells to enter a sleep mode based on PRB & RRC connection) may be used to reciprocate this scenario, which may be irrespective of network key performance indicators such as accessibility, reliability, coverage hole, mobility, latency, throughput, payload, etc.

Existing static configuration per cell (e.g., with non-adopted traffic pattern and respective KPIs) may cause one or more cells to remain on and/or to radiate across a time interval where less traffic is observed in the network. However, other configurations that use less resources may be used without impacting the network and user experience.

In some embodiments, one or more recommendation such as Artificial Intelligence (Al) based dynamic parameter configuration recommendation per cell may be made/determined, while keeping network performance, traffic pattern, and other important network key factors. Such recommendations may allow service providers to reduce energy consumption (when compared to typical systems), hence reducing the cost and carbon footprint.

In some other embodiments, minimal impact to user experience and/or network performance is kept, e.g., where cell/site on-air possibilities can be controlled and/or modified in accordance with an optimum resource utilization.

In some embodiments, Al based dynamic Cell Sleep Mode configuration parameter thresholds prediction (i.e., determination) provides a quantitative method of evaluating energy savings per period (i.e., on a predetermined frequency such as daily, weekly, monthly, quarterly, and yearly). In some other embodiments, the energy evaluation may be performed across cells, sites, bands, sectors, etc.

Typically, cell sleep mode is activated using pre-defined static thresholds that are configured manually. The pre-defined static thresholds are uniform across all cells and sites. In one or more embodiments, thresholds may be dynamically recommended (i.e., determined) for one or more cell sleep mode parameters. The thresholds may be determined for different cell clusters and may be derived from various factors (e.g., RRC connections, PRB utilization, data volume, etc.). The thresholds and/or factors may influence energy consumption. In some embodiments, one or more thresholds (and/or time windows and/or durations) associated with cell sleep and/or cell wakeup may be identified and/or determined. In a nonlimiting example, optimal cell sleep and/or wakeup thresholds, time windows, and duration for which cells are turned on (woken up) and off (set to sleep) (e.g., for an evaluation period without compromising the radio quality and performance in a serving area) are determined based on forecasted PRB utilization and/or user activities.

In some other embodiments, one or more of the following example steps may be performed (e.g., by network node 16 and/or any of its components):

Step 1 (Trends & Correlation Analysis): Key features/influencers of energy are identified/determined. PRB utilization, RRC connected users, connection attempts behavior trend and correlation with other service KPIs are determined.

Step 2 (Energy Consumption Forecasting): Energy consumption levels per cell and/or score thresholds are predicted based on forecasted energy consumption when cell sleep mode is observed. Cells are determined/deduced for possible improvements in terms of Energy performance. Optimal thresholds (i.e., thresholds) are determined/predicted based on estimated energy gain due to increased cell sleep time.

Step 3 (Recommendations at cell/site level on factors affecting energy consumption): Dynamic configuration per cell using energy gain is recommended.

Dynamic configurations at cell level may be estimated, e.g., by forecasting the energy consumption for a future time and estimating the network KPI impact at that point in time using regression models. Considering the energy consumption values, the thresholds are determined using an NBEATS forecasting model.

FIG. 14 shows a first set of example thresholds according to embodiments of the present disclosure. The one or more of the examples thresholds may correspond to an existing value, although the example thresholds are not limited as such. FIG. 15 shows a second set of example thresholds according to embodiments of the present disclosure. The one or more of the example thresholds of the second set may correspond to a new value, although the example thresholds are not limited as such. Further, FIG. 16 shows example features that are scored by importance (e.g., by F- score). In some embodiments, network node 16 may be configured to perform one or more steps such as correlation analysis, threshold determination, what-if analysis (based on a forecasted energy model), and acceptable threshold recommendations. FIG. 17 shows an example process for recommending thresholds, where one or more of the following steps are performed (e.g., by network node 16 and/or processing circuitry 68 and/or any other component of network node 16):

S200: determining and/or identifying KPIs correlating to PRB and RRC (e.g., PRB resources and RRC connections), which may be part of a correlation analysis;

S202: determining a list of thresholds for coverage and capacity RRCs (i.e., RRC connections for cells such as coverage and/or capacity cells 18, 19), which may be part of a threshold determination process. Thresholds may start with static values and/or be interactively incremented.

Step S204: A cell sleep opportunity is determined with respect to associated coverage cells 18. This step may be part of a what-if analysis that may be performed based on a forecasted energy model.

S206: If cell sleep opportunity is found, the PRB and RRC (e.g., PRB resource and RRC connection) is added at the coverage layer. For example, PRB and RRC at capacity (i.e., capacity cell) is added to PRB and RRC at coverage (i.e., coverage cell).

S208: If cell opportunity is not found, the method includes rolling back to iteration 2 thresholds (i.e., rolling back to a threshold corresponding to an iteration such as a second iteration)

S210: Thresholds and/or list of thresholds are validated for one or more capacity cells 19 (e.g., all capacity cells 19). This step may be part of a what-if analysis that may be performed based on the forecasted energy model.

S212: One or more energy consumptions (i.e., energy consumption values) are forecasted. For example, a first energy consumption (using NBEATs model) with sleep mode and/or a second energy consumption (using NBEATS model) without sleep mode may be forecasted. This step may be part of a what-if analysis that may be performed based on a forecasted energy model.

S214: Forecasted energy with sleep is compared to forecasted energy without sleep. If the forecasted energy with sleep is less than the forecasted energy without sleep, capacity cell 19 thresholds are validated and/or recommended and/or values for a sleep start time and sleep end time are recommended.

S216: Forecasted energy with sleep is compared to forecasted energy without sleep. If the forecasted energy with sleep is greater than the forecasted energy without sleep, capacity cell 19 thresholds are invalidated (i.e., not recommended).

Any of the steps described above (e.g., S204, S210, S212) may be performed based on a forecasted energy model. Any of the steps described above (e.g., any one of S204-S216) may be performed/automated using machine learning.

In some other embodiments, network node 16 (and/or processing circuitry 68) may be configured to perform one or more steps such as correlation analysis, threshold determination, what-if analysis, and acceptable threshold recommendations. The steps associated with the what-if analysis and the acceptable threshold recommendations may be part of (or performed by) a cell sleep mode recommendation engine. The cell sleep mode recommendation engine may be included in (and/or part of and/or performed by) processing circuitry 68 of network node 16. FIG. 18 shows another example process to recommend thresholds, where one or more of the following steps are performed:

S300: determining and/or identifying KPIs correlating to PRB and RRC (e.g., PRB resources and RRC connections), which may be part of a correlation analysis;

S302: determining a list of thresholds for coverage and capacity RRCs (i.e., RRC connections for cells such as coverage and/or capacity cells 18, 19), which may be part of a threshold determination process. Thresholds may start with static values and/or be interactively incremented.

S304: A cell sleep opportunity is determined with respect to associated coverage cells 18. This step may be part of a what-if analysis that may be performed based on a forecasted energy model.

S306: If cell sleep opportunity is found, the PRB and RRC (e.g., PRB resource and RRC connection) is added at coverage layer. For example, PRB and RRC at capacity (i.e., capacity cell) is added to PRB and RRC at coverage (i.e., coverage cell). This step may be part of a what-if analysis that may be performed based on a forecasted energy model. S308: If cell opportunity is not found, the method includes rolling back to iteration 2 thresholds, (i.e., rolling back to a threshold corresponding to an iteration such as a second iteration). This step may be part of a recommended acceptable threshold process.

S310: Thresholds and/or list of thresholds are validated for one or more capacity cells 19 (e.g., all capacity cells 19). This step may be part of a what-if analysis that may be performed based on a forecasted energy model.

S312: One or more energy consumptions (i.e., energy consumption values) are forecasted. For example, a first energy consumption (using NBEATs model) with sleep mode and/or a second energy consumption (using NBEATS model) without sleep mode may be forecasted.

S314: Forecasted energy with sleep is compared to forecasted energy without sleep. If the forecasted energy with sleep is less than the forecasted energy without sleep, capacity cell 19 thresholds are validated and/or recommended and/or values for a sleep start time and sleep end time are recommended.

S316: Forecasted energy with sleep is compared to forecasted energy without sleep. If the forecasted energy with sleep is greater than the forecasted energy without sleep, capacity cell 19 thresholds are invalidated (i.e., not recommended).

In some embodiments, a first energy consumption at both first and second cell may be forecasted using a first mode of operation. A second energy consumption of both first and second cell may be forecasted using a second mode of operation. The first mode and the second mode of operation may be different (e.g., the second mode of operation may be a mode of operation that does not use/include the first mode of operation).

Any of the steps described above (e.g., S304, S306, S310, S312) may be performed based on a forecasted energy model. Any of the steps described above (e.g., any one of S304-S316) may be performed/automated using machine learning and/or cell sleep mode recommendation engine.

FIG. 19 shows an example objective framework 102. The example objective framework may include processing circuitry 68 (e.g., performing steps associated with an energy recommendation engine) and domain expertise and logic 104. Network node 16, via processing circuitry 68 (e.g., performing steps associated with the energy recommendation engine), may be configured to determine energy and KPI predictors and/or use case specific models. Network node 16, via processing circuitry 68, may further be configured to execute functions associated with a recommendation engine. Further, network node 16, via processing circuitry 68, may be configured to determine configuration and action recommendations and domain expertise based at least in part on at least one of raw data and/or feature engineering.

Domain expertise and logic 104 may include a plurality of objectives 106 and a plurality of actions 108 (i.e., changes). One or more actions of the plurality of actions may be performed by processing circuitry 68 of network node 16 based at least in part on one or more objectives of the plurality of objectives. Objectives 106 may include tuning a threshold and/or other parameters. For example, an objective 106 which may be optimized using rules may include tuning coverage thresholds for coverage cells 18 based on average PRB and RRC KPIs observed, e.g., to wake up a capacity cell 19 in normal and/or high traffic scenarios. A corresponding action 108 (e.g., performed by processing circuitry 68) may include determining a recommendation such as a controlled deviations-based threshold recommendation on a coverage cell RRC and coverage cell PRB related to wake-up. Another objective 106 may include tuning capacity thresholds for capacity cell sleep based on average PRB and RRC KPIs, e.g., in accordance with coverage cell load and/or behavior studied after coverage threshold changes. Another action 108 (e.g., performed by processing circuitry 68) corresponding to tuning capacity thresholds may include determining controlled deviations-based threshold recommendations on capacity cell PRB and RRC thresholds for capacity cell sleep instances.

One objective 106, associated with automation using ML, may include tuning monitoring duration-based thresholds for cell sleep and cell wakeup based on average PRB and RRC KPIs. One corresponding action 108 may include determining controlled deviation-based threshold recommendations on wakeup monitoring durations (e.g., covCellWakeUpMonitorDur) for normal and/or high traffic. Yet, another objective 106 may include tuning cell level optimal sleep start and/or sleep end window per band/site, e.g., to validate the instances of capacity cell sleep and wakeup across a period such as twenty-four hours. An action 108 corresponding to tuning cell level optimal sleep start and/or sleep end window may include threshold recommendation for sleep start and/or sleep end window based on capacity and/or coverage cell KPIs across the period.

In some embodiments, a method for determining thresholds (i.e., a method of optimization) is described. The thresholds determinations (or any other step) may use neural basis expansion analysis for interpretable time series (NBEATS). NBEATS is a pure deep learning model for time series forecasting which uses residual skip connections (forward and backward) and fully connected layers. An NBEATS model may be optimized (i.e., determined) per each bandwidth across the network. The optimization may be performed using gradient descent over Mean Absolute error of a forecasted value with respect to an observed energy value from historical data. In one or more embodiments, using NBEATS is advantageous to address/process multiple timeseries (each one being multi-dimensional). Further, the method may be used to estimate one or more thresholds (e.g., optimal thresholds) across various parameters. In another embodiment, the method ensures maximum energy gain through a greedy search approach.

As will be appreciated by one of skill in the art, the concepts described herein may be embodied as a method, data processing system, computer program product and/or computer storage media storing an executable computer program. Accordingly, the concepts described herein may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects all generally referred to herein as a “circuit” or “module.” Any process, step, action and/or functionality described herein may be performed by, and/or associated to, a corresponding module, which may be implemented in software and/or firmware and/or hardware. Furthermore, the disclosure may take the form of a computer program product on a tangible computer usable storage medium having computer program code embodied in the medium that can be executed by a computer. Any suitable tangible computer readable medium may be utilized including hard disks, CD-ROMs, electronic storage devices, optical storage devices, or magnetic storage devices. Some embodiments are described herein with reference to flowchart illustrations and/or block diagrams of methods, systems and computer program products. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer (to thereby create a special 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 block or blocks.

These computer program instructions may also be stored in a computer readable memory or storage medium that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture including instruction means which implement the function/act specified in the flowchart and/or block diagram block or blocks.

The computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.

It is to be understood that the functions/acts noted in the blocks may occur out of the order noted in the operational illustrations. For example, two blocks shown in succession may in fact be executed substantially concurrently or the blocks may sometimes be executed in the reverse order, depending upon the functionality/acts involved. Although some of the diagrams include arrows on communication paths to show a primary direction of communication, it is to be understood that communication may occur in the opposite direction to the depicted arrows.

Computer program code for carrying out operations of the concepts described herein may be written in an object oriented programming language such as Python, Java® or C++. However, the computer program code for carrying out operations of the disclosure may also be written in conventional procedural programming languages, such as the “C” programming language. The program code may execute entirely on the user’s computer, partly on the user’s computer, as a stand-alone software package, partly on the user’s computer and partly on a remote computer or entirely on the remote computer. In the latter scenario, the remote computer may be connected to the user’s computer through a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider).

Many different embodiments have been disclosed herein, in connection with the above description and the drawings. It will be understood that it would be unduly repetitious and obfuscating to literally describe and illustrate every combination and subcombination of these embodiments. Accordingly, all embodiments can be combined in any way and/or combination, and the present specification, including the drawings, shall be construed to constitute a complete written description of all combinations and subcombinations of the embodiments described herein, and of the manner and process of making and using them, and shall support claims to any such combination or subcombination.

Abbreviations that may be used in the preceding description include:

ML: Machine Learning

DL: Deep Learning

CU-CP: Central Unit Control Plane

CU-UP: Central Unit User Plane

O-DU: Open-Radio Access Network Distributed Unit nRT-RIC - Non-Real Time Radio Intelligence Controller RT-RIC - Real -Time Intelligence Controller

It will be appreciated by persons skilled in the art that the embodiments described herein are not limited to what has been particularly shown and described herein above. In addition, unless mention was made above to the contrary, it should be noted that all of the accompanying drawings are not to scale. A variety of modifications and variations are possible in light of the above teachings without departing from the scope of the following claims.