Intelligent band combination selection

Abstract

A method, apparatus, and system for intelligent band combination selection may be provided, the method including calculating a weighted metric for each band combination of a plurality of band combinations in a network based on one or more network parameters; sending a list of band combinations to a node of the network based on the calculated weighted metric, wherein the list is ordered based on priority of usage by a user equipment (UE) of the network; detecting whether there is a status change in a cell of the network; and evaluating whether to adjust a timer for re-evaluating the list based on detecting a status change in the cell.

Description

INTELLIGENT BAND COMBINATION SELECTIONFIELD

[0001] The present disclosure relates to intelligent band combination selection.BACKGROUND

[0002] The information disclosed in this background section is only for enhancement of understanding of the general background of the disclosure and should not be taken as an acknowledgement or any form of suggestion that this information forms the prior art already known to a person skilled in the art.

[0003] A radio access network (RAN) is an important component in a telecommunications system, as it connects end-user devices (or user equipment (UE)) to other parts of the network. The RAN includes a combination of various network elements (NEs) that connect the end-user devices to a core network. Traditionally, hardware and / or software of a particular RAN is vendor specific.

[0004] Recently, the evolution of telco technologies enables many telco services to be realized virtually, in the form of software. For instance, RANs such as Open RAN (0-RAN) architectures, disaggregate one network component into multiple functional elements. By way of example, a baseband unit (BBU) or base station (i.e., eNB or gNB) is disaggregated into a number of functional elements including a distributed unit (DU) and a centralized unit (CU), wherein the CU can be further disaggregated into Centralized Unit-Control Plane (CU-CP) and Centralized Unit-User Plane (CU-UP). The disaggregation of network elements enables the telco services and the associated functions to be defined and provided in software-based form or virtual networkservices, such as Virtualized Network Functions (VNFs), Cloud-native Network Functions (CNFs) or Software Defined Networking (SDN), among others.

[0005] RAN functions in the O-RAN architecture are controlled and optimized by a RIC. The RIC is a software-defined component that implements modular applications to facilitate the multivendor operability required in the O-RAN system, as well as to automate and optimize RAN operations. The RIC is divided into two types: a non-real-time RIC (Non-RT RIC) and a near-realtime RIC (Near-RT RIC).

[0006] The Non-RT RIC is the control point of a non-real-time control loop and operates on a timescale greater than 1 second within the Service Management and Orchestration (SMO) framework. Its functionalities are implemented through modular applications called rApps, and include: providing policy based guidance and enrichment across the Al interface, which is the interface that enables communication between the Non-RT RIC and the Near-RT RIC; performing data analytics; Artificial Intelligence / Machine Learning (AI / ML) training and inference for RAN optimization; and / or recommending configuration management actions over the 01 interface, which is the interface that connects the SMO to RAN managed elements (e.g., Near-RT RIC, O-RAN centralized Unit (O-CU), O-RAN Distributed Unit (O-DU), etc.).

[0007] The SMO framework manages and orchestrates RAN elements. Specifically, the SMO includes a Federated O-Cloud Orchestration and Management (FOCOM), a Network Function Orchestrator (NFO) that manages Virtual Machines (VM) based VNF and container (i.e., instance) based VNF, and the Operation and Management (0AM) as a part of the SMO that manages and orchestrates what is referred to as the O-Ran Cloud (O-Cloud).

[0008] In related art networks such as brown-field networks, there are a variety of configurations, which may include multiple bands of operation, different carriers, Channel Bandwidths, time / frequency division duplex (TDD / FDD) configurations, different radio access technologies (RAT) (LTE, NR), standalone / non-standalone modes (SA / NSA) of operation, etc.

[0009] Whenever a UE latches to a cell, it is up to the operator to provide the best services to the UE according to the subscriptions and UE capabilities, and also achieving the optimum network utilization and capacity.

[0010] To this end, the UE should be configured to use the optimum band combination (carrier aggregation (CA), CA+ Evolved-Universal Terrestrial Radio Access New Radio Dual Connectivity (ENDC) etc.) that provides the best user experience to the UE in the cell or choose another cell that provides an even better experience based on factors including, but not limited to: UE Capabilities including ENDC Support, nCC (n-numbered component carriers, n= 1,2, 3, 4, 5...) CA support, supported band combinations, channel Bandwidth, load, and better coverage overlapSUMMARY

[0011] Meanwhile, related art algorithms in 0-RAN architecture for Multi-Layer Managements (MLM) may not be able to consider all the above factors into consideration and thus may be sub-optimal in making band selection.

[0012] Accordingly, there is a need for a better method for identifying optimal / prioritized band combinations / bands for the UE based on the above-mentioned factors.

[0013] According to embodiments, a method, apparatus, and system for intelligent band combination selection may be provided, the method including calculating a weighted metric for each band combination of a plurality of band combinations in a network based on one or morenetwork parameters; sending a list of band combinations to a node of the network based on the calculated weighted metric, wherein the list is ordered based on priority of usage by a user equipment (UE) of the network; detecting whether there is a status change in a cell of the network; and evaluating whether to adjust a timer for re-evaluating the list based on detecting a status change in the cell.

[0014] Based on above embodiments, it can be understood that the above embodiments provide a weighted metric list which may enable the RIC to take a more holistic view of real-time load performance when making decisions. Accordingly, optimized performance for the UE may be achieved. For example, by using weighted metrics based on handover metrics, performance may be further improved for the UE. In addition, policies may be centralized to support all possible band combinations (e.g., supported by 3GPP) and policies can be updated whenever there is a need.

[0015] According to embodiments, an apparatus may be provided, the apparatus is configured to: calculate a weighted metric for each band combination of a plurality of band combinations in a network based on one or more network parameters; send a list of band combinations to a node of the network based on the calculated weighted metric, wherein the list is ordered based on priority of usage by a user equipment (UE) of the network; detect whether there is a status change in a cell of the network; and evaluate whether to adjust a timer for re-evaluating the list based on detecting a status change in the cell.

[0016] According to embodiments, a non-transitory computer-readable recording medium may be provided. It may have recorded thereon instructions to perform a method including: calculating a weighted metric for each band combination of a plurality of band combinations in a network based on one or more network parameters; sending a list of band combinations to a nodeof the network based on the calculated weighted metric, wherein the list is ordered based on priority of usage by a user equipment (UE) of the network; detecting whether there is a status change in a cell of the network; and evaluating whether to adjust a timer for re-evaluating the list based on detecting a status change in the cell.

[0017] Additional aspects will be set forth in part in the description that follows and, in part, will be apparent from the description, or may be realized by practice of the presented embodiments of the disclosure.BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Features, aspects and advantages of certain exemplary embodiments of the disclosure will be described below with reference to the accompanying drawings, in which like reference numerals denote like elements, and wherein:

[0019] FIG. 1 illustrates a flowchart diagram for common list and UE-specific band combination selection, according to an embodiment;

[0020] FIG. 2 illustrates a callflow diagram for carrier aggregation band combination prioritization in RAN according to an embodiment;

[0021] FIG. 3 illustrates a flowchart of an example method for evaluating a list of band combinations according to an embodiment;

[0022] FIG. 4 is a diagram of an implementation environment in which systems and / or methods, described herein, may be implemented; and

[0023] FIG. 5 is a diagram of example components of a device for evaluating a list of band combinations according to an embodiment.DETAILED DESCRIPTION

[0024] The following detailed description of example embodiments refers to the accompanying drawings. The foregoing disclosure provides illustration and description, but is not intended to be exhaustive or to limit the implementations to the precise form disclosed. Modifications and variations are possible in light of the above disclosure or may be acquired from practice of the implementations. Further, one or more features or components of one embodiment may be incorporated into or combined with another embodiment (or one or more features of another embodiment). Additionally, in the flowcharts and descriptions of operations provided below, it is understood that one or more operations may be omitted, one or more operations may be added, one or more operations may be performed simultaneously (at least in part), and the order of one or more operations may be switched.

[0025] It will be apparent that systems and / or methods, described herein, may be implemented in different forms of hardware, firmware, or a combination of hardware and software. The actual specialized control hardware or software code used to implement these systems and / or methods is not limiting of the implementations. Thus, the operation and behavior of the systems and / or methods were described herein without reference to specific software code. It is understood that software and hardware may be designed to implement the systems and / or methods based on the description herein.

[0026] Even though particular combinations of features are recited in the claims and / or disclosed in the specification, these combinations are not intended to limit the disclosure of possible implementations. In fact, many of these features may be combined in ways not specifically recited in the claims and / or disclosed in the specification. Although each dependentclaim listed below may directly depend on only one claim, the disclosure of possible implementations includes each dependent claim in combination with every other claim in the claim set.

[0027] No element, act, or instruction used herein should be construed as critical or essential unless explicitly described as such. Also, as used herein, the articles “a” and “an” are intended to include one or more items, and may be used interchangeably with “one or more.” Where only one item is intended, the term “one” or similar language is used. Also, as used herein, the terms “has,” “have,” “having,” “include,” “including,” or the like are intended to be open-ended terms. Further, the phrase “based on” is intended to mean “based, at least in part, on” unless explicitly stated otherwise. Furthermore, expressions such as “at least one of [A] and [B]” or “at least one of [A] or [B]” are to be understood as including only A, only B, or both A and B.

[0028] In the present disclosure, specific tasks may be performed using AI / ML (Artificial Intelligence / Machine Learning) models. An AI / ML model is a model generated using one or more Al technologies, one or more ML algorithm or both, and generates output data based on input data. This output data is used to perform tasks. Tasks performed using AI / ML models include those generally referred to as intellectual tasks, such as classification, prediction, natural language processing, etc.

[0029] Although Al and ML are explained separately, ML is a technology included in Al. In ML, instead of being explicitly programmed for a specific task, systems can improve their performance over time by identifying patterns and making inferences from training data. Typically, the generation of ML models includes data collection, model training, and model inference. Data collection involves gathering and preprocessing data to be used for training and inference. Modeltraining involves developing and validating models using the collected data. Model inference involves applying the trained models to new data to generate new output data and perform tasks.

[0030] Machine learning includes various types of learning methods such as supervised learning, unsupervised learning, reinforcement learning, semi-supervised learning, self-supervised learning, transductive learning, transfer learning, meta learning, and the like. These types of learning methods can be appropriately selected according to the embodiments. Unless otherwise specified, the application of types not mentioned in this description is not precluded. Additionally, the structure of ML models may vary depending on the embodiments and learning methods, and is not limited to the methods disclosed. Furthermore, ML includes deep learning, which uses models that include neural networks. Deep learning models may include, for example, deep neural networks (DNNs), convolutional neural networks (CNNs), etc.

[0031] It should be noted that the AI / ML models presented hereinafter are examples and are not limited to the illustrated AI / ML models. They can be modified or altered by using different Al or ML algorithms. The configuration of the neural network is not limited to the configuration disclosed in the present disclosure and can be modified.

[0032] The term “band combinations” as used herein (which may be abbreviated as “BC” herein) may refer to a set of possible bands used by a carrier for communication between a base station and a radio unit such as a user equipment (UE).

[0033] According to embodiments, a method, apparatus, and system for intelligent band combination selection may be provided, the method including calculating a weighted metric for each band combination of a plurality of band combinations in a network based on one or more network parameters; sending a list of band combinations to a node of the network based on thecalculated weighted metric, wherein the list is ordered based on priority of usage by a user equipment (UE) of the network; detecting whether there is a status change in a cell of the network; and evaluating whether to adjust a timer for re-evaluating the list based on detecting a status change in the cell.

[0034] Based on above embodiments, it can be understood that the above embodiments allow for a weighted metric list which may allow for the RIC to take a more holistic view of realtime load performance when making decisions, thereby allowing for more optimized performance for the UE. For example, by using weighted metrics based on handover metrics, performance may be further improved for the UE. In addition, policies may be centralized to support all possible band combinations (e g., supported by 3GPP) and policies can be updated whenever there is a need.

[0035] FIG. 1 illustrates a flowchart diagram for common list and UE-specific band combination selection, according to an embodiment.

[0036] At operation 100, the system may decide whether or not a common list should be used for all UE’s in the network. In the case where a common list is used for all UE’s, a node of the network such as the eNB / gNB may filter the bands / band combinations based on the UE’s capability. Otherwise, a UE specific list may be processed and shared by the RIC.

[0037] At operation 110 where a common list is used by all the UE’s, the system may firstly check whether or not a switch for artificial Intelligence / Machine Learning (AI / ML) Carrier Aggregation (CA) usage metric identifier (id) is enabled. If yes, then the system may simply share the prioritized list based on the AI / ML evaluated data for the CA usage metric (111), until the expiry of a timer (112). If no, the system may proceed the use an algorithm to calculate theweighted metric based on parameters (113), (the algorithm is described further below), and subsequently generate and send a prioritized list based on the weighted metric to eNB / gNB (114).

[0038] At operation 121 where the list is UE-specific, all the bands which are supported by the UE are firstly evaluated by the RIC or the Node eNB / gNB based on the UE’s capability, thereby filtering the supported bands. At operation 122, any carriers which are not currently active in the site / sector may also be filtered out of the UE supported bands (in order to reduce congestion, etc.). At 123, the filtered list of band combinations may be obtained accordingly (e.g., from a master combination list which includes all the possible band combinations). At operation 124, the algorithm may then be used to calculate a weighted metric based on parameters, and at operation 125, the prioritized list may be generated and sent to eNB / gNB.

[0039] At operation 130, after the prioritized list of band / band combinations is received, the system may evaluate whether there is a change in the status of the network, such as whether or not there is an overload in any of the cells, and / or if any cell load criteria has changed. This can be performed periodically based on a maximum and minimum value of a timer. In particular, if a status change has occurred, the periodic evaluation may be set to the minimum value and reevaluate the prioritized list after the timer expires at operation 132. Based on this, the evaluation will be more frequent and the prioritized list can be better updated. On the other hand, if the overload has not occurred, the periodic evaluation may be set to the maximum value at operation 131 and re-evaluate the prioritized list after the timer expires. Based on this, the evaluation will be more infrequent and avoid having to change the prioritized list as often, thereby reducing processing resources spent by RIC.

[0040] It should be appreciated the evaluation timers can be adjusted by the RIC considering other parameters (for example, time of day, overload prediction mechanisms incorporated into RIC, etc.)

[0041] The above-mentioned algorithm is described herein. Specifically, the algorithm may calculate a weighted metric for each of the band combinations such that band / band combinations can be prioritized according to an aggregate weight. Network-related parameters which the weighted metric may take into account may include, but is not necessarily limited to, composite load metrics, ENDC support and nCC CA support, CA Usage based on AI / ML model s / monitoring, channel bandwidth and number of layers, band of operation (low-band / high-band), ad-hoc operator requirements. nCC CA support may refer to support for n-numbered component carriers for CA, wherein n is a number of 1, 2, 3, 4, or 5 aggregated carriers. It should be appreciated that the above list is merely an example, and is non-exhaustive, and that a person skilled in the art may include other network-related parameters depending on the specific implementation.

[0042] FIG. 2 illustrates a callflow diagram for carrier aggregation band combination prioritization in RAN according to an embodiment. Operations and Maintenance (0AM), non-RT RIC, near-RT RIC, and E2 Node may be provided.

[0043] Operations 201-204 may fall under operations for performing data collection. In operation 201, the 01 interface may be used between 0AM and E2 Node to exchange RAN data and configuration collection.

[0044] In operation 202, data collection can be performed by the near-RT RIC making a subscription request for measurement metrics to the E2 Node.

[0045] In operation 203 the E2 Node may provide an RIC indication to the near-RT RIC

[0046] In operation 204, the near-RT RIC amy provide the measurement metrics to the non-RT RIC. This may be performed via the E2 interface. E2 Node may provide data including, but not limited to: (1) Load information of the cells based on a composite load metric. The composite load metric can be a factor of RRC connected users, PRB utilization, control channel load etc. left to NodeB implementation; (2) CA band combination usage metric to evaluate the usage based on AI / ML; and (3) List of bands and other details supported by the UE (Based on UE capability information).

[0047] Operations 205-207 may fall under operations for applying additional policy settings by the RIC. At operation 205, the 0AM may send the data collected from the E2 Node back to the non-RT RIC in order to perform AI / ML evaluation in operation 206. The AI / ML evaluation may be performed based on C A usage data. The AI / ML evaluation may be implemented in the RIC (e g., the non-RT RIC and near-RT RIC). The data may have been collected in relation to CA usage for the UE’s in the cells. The AI / ML model may be trained based on pre-processing of historical data, such that the AI / ML model is optimized to get priority band combinations. Accordingly, the AI / ML model may be applied to new data for predictions or decisions in relation to band / band combination selection. Methods which may be used to update the model may include incremental learning, batch training with windowing, but other methods are possible depending on the specific implementation.

[0048] At operation 207, based on the AI / ML evaluation in operation 206, the non-RT RIC may configure a policy setup / update.

[0049] Operations 208-209 may fall under operations for data evaluation and CA / BC prioritization, which is done after the policy settings were configured by the RIC above. At operation 208, the near-RT RIC may perform an evaluation based on weighted metrics calculated by the near-RT RIC from network-related parameters in order to generate a prioritized list (CA / BC prioritized list).

[0050] At operation 209, the near-RT RIC may send the prioritized list via a RIC control request, to the E2 node, based on the CA / BC prioritized list. Configurations may also be provided within the RIC control request to set the RIC policies according to different weighted metrics and adjustment of weights.

[0051] It should be appreciated that an updated master list of band combinations (e g., based on relevant 3GPP supported list and eNB supported list) will be available to the RIC, and that any filtering / band combinations selected are based on this maser list. In addition, that although the above-described methods may be performed to obtain the prioritized list during a UE attach, service request or handover procedure, the above-described methods may be implemented in other network procedures depending on the scenario.

[0052] FIG. 3 illustrates a flowchart of an example method 300 for evaluating a list of band combinations according to an embodiment.

[0053] At operation S310, the weighted metric for each band combination (BC) may be calculated based on one or more network parameters. Network parameters may include at least one of composite load metrics, Evolved-Universal Terrestrial Radio Access New Radio Dual Connectivity (ENDC) support, n-numbered component carrier (nCC) carrier aggregation (CA) support, CA Usage based on artificial intelligence / machine learning models / monitoring, channelbandwidth and number of layers, band of operation, and ad-hoc operator requirements. The one or more network parameters may be based on metric data received by a near realtime radio access network (RAN) intelligent controller (near-RT RIC) of the network, from an E2 node

[0054] At operation S320, the list of BC’s may be sent to the node (e.g., E2Node) based on a calculated weighted metric from operation S310. The list may be ordered based on priority of usage by a user equipment (UE) of the network. According to some embodiments, the list may be UE-specific and the plurality of band combinations may be based on supported bands from the UE’s capability (e.g., by filtering). The near-RT RIC is configured to send the list to the E2 node based on a RIC control request.

[0055] At operation S330, it may be detected as to whether there is a status change in a cell of the network. For example, this may be based on detecting an overload condition for a carrier.

[0056] At operation S340, the timer for re-evaluation may be adjusted based on detecting the status change in operation S330. The timer for re-evaluating the list may include a minimum evaluation time and a maximum evaluation time, wherein if the status change has occurred, the re-evaluation of the list is scheduled based on the minimum evaluation time, wherein if the status change has not occurred, the re-evaluation of the list is scheduled based on the maximum evaluation time.

[0057] Based on above embodiments, it can be understood that the above embodiments allow for a weighted metric list which may allow for the RIC to take a more holistic view of realtime load performance when making decisions, thereby allowing for more optimized performance for the UE. For example, by using weighted metrics based on handover metrics, performance maybe further improved for the UE. In addition, policies may be centralized to support all possible band combinations (e.g., supported by 3GPP) and policies can be updated whenever there is a need.

[0058] FIG. 4 is a diagram of an example of implementation environment 400 in which systems and / or methods, described herein, may be implemented. The implementation environment 400 includes a UE (User equipment) 410, a service environment 420, and a network 430. The service environment 420 include one or more sub-environments 421. To illustrate this, FIG. 4 shows, for convenience, examples of a 1st sub-environment 421-1, a 2nd sub-environment 421-2, and an N-th sub-environment 421-N (where N is any natural number).

[0059] The UE 410 is connected to the network 430, and the network 430 is connected to the service environment 420. The connections may be wired, wireless, or a combination of both wired and wireless. The UE 410 and the service environment 420 are connected via the network 430.

[0060] The UE 410 is a device that communicates with the service environment 420. The UE A10 receives information from the service environment 420 and / or sends information to the service environment 420. Also, the UE 410 may generate and / or store information to be transmitted, as necessary. Also, the UE 410 may store and / or process information that is received, as necessary.

[0061] The example FIG. 4 refers to the “UE”. However, it should be understood by those skilled in the art that general terms such as “user device,” “terminal,” “terminal device,” “communication device,” and “communication terminal” can be used interchangeably with the term “UE.”

[0062] For example, the UE 410 may include a computing device (e.g., a desktop computer, a laptop computer, a tablet computer, a handheld computer, a smart speaker, a server, etc.), a mobile phone (e.g., a smart phone, a radiotelephone, etc.), a wearable device (e.g., a pair of smart glasses or a smart watch), or a similar device.

[0063] The service environment 420 is an environment that communicates with the UE 410 to provide one or more services. The service environment 420 receives information from the UE 410 and / or sends information to the UE 410. Also, the service environment 420 may generate and / or store information to be transmitted, as necessary. Also, the service environment 420 may store and / or process information that is received, as necessary. For example, the service environment 420 may provide computing resources as one of the services. It should be noted that the service is not limited to being provided to the UE; it may also be provided to devices other than the UE. For example, based on communication from the UE, the service may perform processes such as anomaly detection or traffic analysis and notify the results to a predetermined destination.

[0064] The example FIG. 4 refers to the “service environment”. The term "service environment" is used to refer to the broader context within which services operate. For example, cloud environments, platforms, computing systems, network systems, and cloud systems generally represent the environments in which services are conducted, and these are included within the "service environment." However, the "service environment" is not limited to these examples. Additionally, the specific types of environments within the "service environment" are not restricted. For instance, cloud environments and cloud systems can be categorized as private cloud, public cloud, hybrid cloud, or multi-cloud, all of which are included within the "service environment."

[0065] The one or more services provided by the service environment 420 is not specifically limited and can be adjusted according to the embodiments. For example, the services may include a service that provides information to the UE 410, a service that stores information from the UE 410, or a service that performs processing based on information from the UE 410 and returns the results of the processing.

[0066] In an embodiment, the Service Environments 420 may also provide computing resources as the service. The computing resources can be hardware resources and / or software resources. For example, applications, processors, memory, and storage can be included in the provided computing resources. Each computing resource can communicate with other computing resources via wired connections, wireless connections, or a combination of wired and wireless connections.

[0067] The provided computing resources can be actual resources (also referred to as physical resources) and / or virtual resources. Furthermore, means of virtualization for virtual resources can be selected as appropriate. That is, in this disclosure, the use of adjectives such as "Virtual" or "Virtualized" to describe names does not imply that they are virtualized by a specific means of virtualization. For example, “virtual machine” refers to software that operates like an actual computer, realized through means of virtualization, and it is not intended to exclude those realized by specific means of virtualization such as Hypervisors or Containers. Conversely, when means of virtualization such as Hypervisors or containers are mentioned in this disclosure, it is merely cited as a general method of implementation. It should also be interpreted that embodiments implemented with other virtualization means are also disclosed. Also, the services may also be provided using resources virtualized by different means.

[0068] The service environment 420 includes one or more devices, such as servers and network devices, which provide services or perform processes. The placement of these devices within the service environment 420 can be determined as appropriate. Additionally, if the service environment 420 includes one or more sub-environments 421, the placement of devices can be determined based on predetermined policies for each sub-environment 421. For example, devices related to the first service may be placed in the 1st sub-environment 421-1, and devices related to the second service may be placed in the 2nd sub-environment 421-2. In another example, devices expected to have a higher load than a predetermined threshold may be placed in the 1st subenvironment 421-1, while devices expected to have a lower load than the predetermined threshold may be placed in the 2nd sub -environment 421-2. In this way, specific devices can be placed in specific sub -environments 421. Conversely, each sub-environment 421 can be specialized for a particular purpose.

[0069] In an embodiment, all processes executed in a single service may run within a single service environment, or in multiple service environments. Multiple processes executed in a single service could be provided by different service environments.

[0070] The network 430 is a network that exchanges information between the UE 410 and the service environment 420. The network 430 includes one or more wired and / or wireless networks.

[0071] For example, the network 430 may include a cellular network (e.g., a fifth generation (5G) network, a long-term evolution (LTE) network, a third generation (3G) network, a code division multiple access (CDMA) network, etc.), a public land mobile network (PLMN), a local area network (LAN), a wide area network (WAN), a metropolitan area network (MAN), atelephone network (e.g., the Public Switched Telephone Network (PSTN)), a private network, an ad hoc network, an intranet, the Internet, a fiber optic-based network, or the like, a non-terrestrial network (NTN), and / or a combination of these or other types of networks.

[0072] The network 430 can be a part of a network. For example, in a 4G network that includes a RAN, a transport network, and a core network, the network 430 can be at least one of the RAN, the transport network, or the core network. For example, the service environment 420 could be in the core network, in which case the network 430 could correspond to a network that is a combination of a RAN and a transport network and is part of the 4G network.

[0073] The number and arrangement of devices and networks shown in FIG. 7 are provided as an example. It should be understood that any changes that may be implemented by those skilled in the art, such as the addition or rearrangement of well-known devices or networks at the time of implementation, are included in this disclosure.

[0074] FIG. 5 illustrates an embodiment of example components of a device 500 for evaluating a list of band combinations. As shown in FIG. 5, the device 500 includes processor 510, a memory 520, a storage component 530, an input component 540, an output component 550, a communication interface 560, and a bus 570.

[0075] The processor 510, as used herein, means any type of computational circuit that may comprise hardware elements and software elements. The processor 510 may be embodied as a multi-core processor, a single core processor, or a combination of one or more multi-core processors and / or one or more single core processors, a distributed processing system, or the like. The processor 510 may be a Central Processing Unit (CPU), a graphics processing unit (GPU),an accelerated processing unit (APU), an application-specific integrated circuit (ASIC), or another type of processing component.

[0076] Memory 520 includes a non-transitory computer readable medium. Memory 520 includes a random-access memory (RAM), a read only memory (ROM), and / or another type of dynamic or static storage device (e.g., a flash memory, a magnetic memory, and / or an optical memory) that stores information and / or instructions for use by processor 510. The memory 520 comprises machine-readable instructions which are executable by the processor 510. These machine-readable instructions when executed by the processor 510 cause the processor 510 to perform one or more method steps of an embodiment described above.

[0077] Storage component 530 stores information and / or software related to the operation and use of the device 500. For example, storage component 530 may include a hard disk (e.g., a magnetic disk, an optical disk, a magneto-optic disk, and / or a solid-state disk), a compact disc (CD), a digital versatile disc (DVD), a floppy disk, a cartridge, a magnetic tape, and / or another type of non-transitory computer-readable medium, along with a corresponding drive.

[0078] Input component 540 is configured to receive information, such as user input. For example, the input component 540 may include, but not be limited to, a touch screen display, a keyboard, a keypad, a mouse, a button, a switch, and / or a microphone. Additionally, or alternatively, the input component 540 may include a sensor for sensing information (e.g., a global positioning system (GPS), an accelerometer, a gyroscope, and / or an actuator).

[0079] Output component 550 is configured to provide output information from the device 500. For example, the output component 550 may be, but not limited to, a display, a speaker, an instruction device to an external device, and / or one or more light-emitting diodes (LEDs).

[0080] Communication interface 560 is an interface that provides a communication connection to other devices, such as external devices and internal devices. The connection by the communication interface 560 can be a wired connection, a wireless connection, or a combination of wired and wireless connections, and can be a direct connection or an indirect connection via a communication network that exists between the device 500 and other devices. In other words, the standard of the communication interface 560 is not limited.

[0081] The bus 570 acts as an interconnect between the processor 510, the memory 520, the storage component 530, the input component 540, the output component 550, and the communication interface 560 of the device 500. The bus 570 may include a wired interconnection or a wireless interconnection.

[0082] The number and arrangement of components shown in FIG. 5 are provided as an example. In practice, device 500 may include additional components, fewer components, different components, or differently arranged components than those shown in FIG. 5. Additionally, or alternatively, a set of components (e.g., one or more components) of device 500 may perform one or more functions described as being performed by another set of components of device 500. Further, one or more method steps described in any of the embodiments may be performed utilizing a plurality of devices 500 in communication with one another.

[0083] The foregoing disclosure provides illustration and description, but is not intended to be exhaustive or to limit the implementations to the precise form disclosed. Modifications and variations are possible in light of the above disclosure or may be acquired from practice of the implementations.

[0084] Some embodiments may relate to a system, a method, and / or a computer readablemedium at any possible technical detail level of integration. Further, one or more of the above components described above may be implemented as instructions stored on a computer readable medium and executable by at least one processor (and / or may include at least one processor). The computer readable medium may include a computer-readable non-transitory storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out operations.

[0085] The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.

[0086] Computer readable program instructions described herein can be downloaded torespective computing / processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and / or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and / or edge servers. A network adapter card or network interface in each computing / processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing / processing device.

[0087] Computer readable program code / instructions for carrying out operations may be assembler instructions, instmction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, configuration data for integrated circuitry, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++, or the like, and procedural programming languages, such as the "C" programming language or similar programming languages. The computer readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a standalone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects or operations.

[0088] These computer readable program instructions may be provided to a processor of a general-purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and / or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function / act specified in the flowchart and / or block diagram block or blocks.

[0089] The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0090] The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer readable media according to various embodiments. In this regard, each block in the flowchart orblock diagrams may represent a microservice(s), module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). The method, computer system, and computer readable medium may include additional blocks, fewer blocks, different blocks, or differently arranged blocks than those depicted in the Figures. In some alternative implementations, the functions noted in the blocks may occur out of the order noted in the Figures. For example, two blocks shown in succession may, in fact, be executed concurrently or substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and / or flowchart illustration, and combinations of blocks in the block diagrams and / or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions.

[0091] It will be apparent that systems and / or methods, described herein, may be implemented in different forms of hardware, firmware, or a combination of hardware and software. The actual specialized control hardware or software code used to implement these systems and / or methods is not limiting of the implementations. Thus, the operation and behavior of the systems and / or methods were described herein without reference to specific software code — it being understood that software and hardware may be designed to implement the systems and / or methods based on the description herein.

[0092] Various further respective aspects and features of embodiments of the present disclosure may be defined by the following items:Item [1]: A method, including calculating a weighted metric for each band combination of a plurality of band combinations in a network based on one or more network parameters; sending a list of band combinations to a node of the network based on the calculated weighted metric, wherein the list is ordered based on priority of usage by a user equipment (UE) of the network; detecting whether there is a status change in a cell of the network; and evaluating whether to adjust a timer for re-evaluating the list based on detecting a status change in the cell.Item [2]: The method according to Item [1], wherein the method further comprises: determining whether a switch for artificial intelligence / machine learning carrier aggregation (CA) usage metrics is enabled; and based on determining the switch is enabled, sharing a prioritized list based on artificial learning / machine learning evaluated data for the CA usage metric.Item [3]: The method according to any one of Items [l]-[2], wherein the list is UE-specific, and the plurality of band combinations is based on supported bands from the UE's capability.Item [4]: The method according to any one of Items

[0001] -[3] wherein the timer for re-evaluating the list comprises a minimum evaluation time and a maximum evaluation time, wherein if the status change has occurred, the re-evaluation of the list is scheduled based on the minimum evaluation time, wherein if the status change has not occurred, the re-evaluation of the list is scheduled based on the maximum evaluation time.Item [5]: The method according to any one of Items [l]-[4], wherein the one or more network parameters comprise at least one of composite load metrics, Evolved-Universal Terrestrial Radio Access New Radio Dual Connectivity (ENDC) support, n-numbered component carrier (nCC) carrier aggregation (CA) support, CA Usage based on artificial intelligence / machine learning model s / monitoring, channel bandwidth and number of layers, band of operation, and ad-hoc operator requirements.Item [6]: The method according to Item [5], wherein the node is an E2 node, wherein the one or more network parameters are based on metric data received by a near realtime radio access network (RAN) intelligent controller (near-RT RIC) of the network, from the E2 node.Item [7]: The method according to Item [6], wherein the near-RT RIC is configured to send the list to the E2 node based on a RIC control request.Item [8]: An apparatus configured to: calculate a weighted metric for each band combination of a plurality of band combinations in a network based on one or more network parameters; send a list of band combinations to a node of the network based on the calculated weighted metric, wherein the list is ordered based on priority of usage by a user equipment (UE) of the network; detect whether there is a status change in a cell of the network; and evaluate whether to adjust a timer for re-evaluating the list based on detecting a status change in the cell.Item [9]: The apparatus according to Item [8], further configured to: determine whether a switch for artificial intelligence / machine learning carrier aggregation (CA) usage metrics is enabled; and based on determining the switch is enabled, share a prioritized list based on artificial learning / machine learning evaluated data for the CA usage metric.Item

[0010] : The apparatus according to any one of Items [8]-[9], wherein the list is UE-specific, and the plurality of band combinations is based on supported bands from the UE's capability.Item

[0011] : The apparatus according to any one of Items [8]-

[0010] , wherein the timer for reevaluating the list comprises a minimum evaluation time and a maximum evaluation time, wherein if the status change has occurred, the re-evaluation of the list is scheduled based on the minimum evaluation time, wherein if the status change has not occurred, the re-evaluation of the list is scheduled based on the maximum evaluation time.Item

[0012] : The apparatus according to any one of Items [8]-[l 1], wherein the one or more network parameters comprise at least one of composite load metrics, Evolved-Universal Terrestrial Radio Access New Radio Dual Connectivity (ENDC) support, n-numbered component carrier (nCC) carrier aggregation (CA) support, CA Usage based on artificial intelligence / machine learning model s / monitoring, channel bandwidth and number of layers, band of operation, and ad-hoc operator requirements.Item

[0013] : The apparatus according to Item

[0012] , wherein the node is an E2 node, wherein the one or more network parameters are based on metric data received by a near realtime radio access network (RAN) intelligent controller (near-RT RIC) of the network, from the E2 node.Item

[0014] : The apparatus according to Item

[0013] , wherein the near-RT RIC is configured to send the list to the E2 node based on a RIC control request.Item

[0015] : A non-transitory computer-readable recording medium having recorded thereon instructions to perform a method comprising: calculating a weighted metric for each band combination of a plurality of band combinations in a network based on one or more network parameters; sending a list of band combinations to a node of the network based on the calculated weighted metric, wherein the list is ordered based on priority of usage by a user equipment (UE) of the network; detecting whether there is a status change in a cell of the network; and evaluating whether to adjust a timer for re-evaluating the list based on detecting a status change in the cell.Item

[0016] : The non-transitory computer-readable recording medium according to Item

[0015] , wherein the method further comprises: determining whether a switch for artificial intelligence / machine learning carrier aggregation (CA) usage metrics is enabled; and based on determining the switch is enabled, sharing a prioritized list based on artificial leaming / machine learning evaluated data for the CA usage metric.Item

[0017] : The non-transitory computer-readable recording medium according to any one of Items

[0015] -

[0016] , wherein the list is UE-specific, and the plurality of band combinations is based on supported bands from the UE's capability.Item

[0018] : The non-transitory computer-readable recording medium according to any one of Items

[0015] -

[0017] , wherein the timer for re-evaluating the list comprises a minimum evaluation time and a maximum evaluation time, wherein if the status change has occurred, the re-evaluation of the list is scheduled based on the minimum evaluation time, wherein if the status change has not occurred, the re-evaluation of the list is scheduled based on the maximum evaluation time.Item

[0019] : The non-transitory computer-readable recording medium according to any one of Items

[0015] -

[0018] , wherein the one or more network parameters comprise at least one of composite load metrics, Evolved-Universal Terrestrial Radio Access New Radio Dual Connectivity (ENDC) support, n-numbered component carrier (nCC) carrier aggregation (CA) support, CA Usage based on artificial intelligence / machine learning models / monitoring, channel bandwidth and number of layers, band of operation, and ad-hoc operator requirements.Item

[0020] : The non-transitory computer-readable recording medium according to Item

[0019] , wherein the node is an E2 node, wherein the one or more network parameters are based on metric data received by a near realtime radio access network (RAN) intelligent controller (near-RT RIC) of the network, from the E2 node, wherein the near-RT RIC is configured to send the list to the E2 node based on a RIC control request.

[0093] It can be understood that numerous modifications and variations of the present disclosure are possible in light of the above teachings. It will be apparent that within the scope of the appended clauses, the present disclosures may be practiced otherwise than as specifically described herein.

Claims

WHAT IS CLAIMED IS1. A method, comprising:calculating a weighted metric for each band combination of a plurality of band combinations in a network based on one or more network parameters;sending a list of band combinations to a node of the network based on the calculated weighted metric, wherein the list is ordered based on priority of usage by a user equipment (UE) of the network;detecting whether there is a status change in a cell of the network; andevaluating whether to adjust a timer for re-evaluating the list based on detecting a status change in the cell.

2. The method as claimed in claim 1, wherein the method further comprises: determining whether a switch for artificial intelligence / machine learning carrier aggregation (CA) usage metrics is enabled; andbased on determining the switch is enabled, sharing a prioritized list based on artificial learning / machine learning evaluated data for the CA usage metric.

3. The method as claimed in claim 1, wherein the list is UE-specific, and the plurality of band combinations is based on supported bands from the UE's capability.

4. The method as claimed in claim 1, wherein the timer for re-evaluating the list comprises a minimum evaluation time and a maximum evaluation time, wherein if the status change has occurred, the re-evaluation of the list is scheduled based on the minimum evaluation time, wherein if the status change has not occurred, the re-evaluation of the list is scheduled based on the maximum evaluation time.

5. The method as claimed in claim 1, wherein the one or more network parameters comprise at least one of composite load metrics, Evolved-Universal Terrestrial Radio Access New Radio Dual Connectivity (ENDC) support, n-numbered component carrier (nCC) carrier aggregation (CA) support, CA Usage based on artificial intelligence / machine learning model s / monitoring, channel bandwidth and number of layers, band of operation, and ad-hoc operator requirements.

6. The method as claimed in claim 5, wherein the node is an E2 node, wherein the one or more network parameters are based on metric data received by a near realtime radio access network (RAN) intelligent controller (near-RT RIC) of the network, from the E2 node.

7. The method as claimed in claim 6, wherein the near-RT RIC is configured to send the list to the E2 node based on a RIC control request.

8. An apparatus configured to:calculate a weighted metric for each band combination of a plurality of band combinations in a network based on one or more network parameters;send a list of band combinations to a node of the network based on the calculated weighted metric, wherein the list is ordered based on priority of usage by a user equipment (UE) of the network;detect whether there is a status change in a cell of the network; andevaluate whether to adjust a timer for re-evaluating the list based on detecting a status change in the cell.

9. The apparatus as claimed in claim 8, further configured to:determine whether a switch for artificial intelligence / machine learning carrier aggregation (CA) usage metrics is enabled; andbased on determining the switch is enabled, share a prioritized list based on artificial learning / machine learning evaluated data for the CA usage metric.

10. The apparatus as claimed in claim 8, wherein the list is UE-specific, and the plurality of band combinations is based on supported bands from the UE's capability.

11. The apparatus as claimed in claim 8, wherein the timer for re-evaluating the list comprises a minimum evaluation time and a maximum evaluation time, wherein if the status change has occurred, the re-evaluation of the list is scheduled based on the minimum evaluation time, wherein if the status change has not occurred, the re-evaluation of the list is scheduled based on the maximum evaluation time.

12. The apparatus as claimed in claim 8, wherein the one or more network parameters comprise at least one of composite load metrics, Evolved-Universal Terrestrial Radio Access New Radio Dual Connectivity (ENDC) support, n-numbered component carrier (nCC) carrier aggregation (CA) support, CA Usage based on artificial intelligence / machine learning model s / monitoring, channel bandwidth and number of layers, band of operation, and ad-hoc operator requirements.

13. The apparatus as claimed in claim 12, wherein the node is an E2 node, wherein the one or more network parameters are based on metric data received by a near realtime radio access network (RAN) intelligent controller (near-RT RIC) of the network, from the E2 node.

14. The apparatus as claimed in claim 13, wherein the near-RT RIC is configured to send the list to the E2 node based on a RIC control request.

15. A non-transitory computer-readable recording medium having recorded thereon instructions to perform a method comprising:calculating a weighted metric for each band combination of a plurality of band combinations in a network based on one or more network parameters;sending a list of band combinations to a node of the network based on the calculated weighted metric, wherein the list is ordered based on priority of usage by a user equipment (UE) of the network;detecting whether there is a status change in a cell of the network; andevaluating whether to adjust a timer for re-evaluating the list based on detecting a status change in the cell.

16. The non-transitory computer-readable recording medium as claimed in claim 15, wherein the method further comprises:determining whether a switch for artificial intelligence / machine learning carrier aggregation (CA) usage metrics is enabled; andbased on determining the switch is enabled, sharing a prioritized list based on artificial learning / machine learning evaluated data for the CA usage metric.

17. The non-transitory computer-readable recording medium as claimed in claim 15, wherein the list is UE-specific, and the plurality of band combinations is based on supported bands from the UE's capability.

18. The non-transitory computer-readable recording medium as claimed in claim 15, wherein the timer for re-evaluating the list comprises a minimum evaluation time and a maximum evaluation time, wherein if the status change has occurred, the re-evaluation of the list is scheduled based on the minimum evaluation time, wherein if the status change has not occurred, the re-evaluation of the list is scheduled based on the maximum evaluation time.

19. The non-transitory computer-readable recording medium as claimed in claim 15, wherein the one or more network parameters comprise at least one of composite load metrics, Evolved-Universal Terrestrial Radio Access New Radio Dual Connectivity (ENDC) support, n-numbered component carrier (nCC) carrier aggregation (CA) support, CA Usage based on artificial intelligence / machine learning models / monitoring, channel bandwidth and number of layers, band of operation, and ad-hoc operator requirements.

20. The non-transitory computer-readable recording medium as claimed in claim 19, wherein the node is an E2 node, wherein the one or more network parameters are based on metric data received by a near realtime radio access network (RAN) intelligent controller (near-RT RIC) of the network, from the E2 node, wherein the near-RT RIC is configured to send the list to the E2 node based on a RIC control request.

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