Enhanced reporting procedure

Abstract

Embodiments of the disclosure describe a method for enhanced reporting procedure. The method includes configuring, by a source Next Generation Radio Access Network (NG-RAN) node, a data collection request message for a target NG-RAN node to indicate performance data for a User Equipment (UE) required. The data collection request message comprises a plurality of Information Elements (IEs), the plurality of IEs comprising at least one of a report filter for data collection, a report metric to filter map, and a measurement list for data collection. The method further includes receiving, by the source NG-RAN node, the performance data for the UE required from the target NG-RAN node based on the configured data collection request message.

Description

ENHANCED REPORTING PROCEDURECROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to India Provisional Application No. 202411090213, filed on November 20, 2024, and India Non-Provi si onal Application No. 202411090213, filed on April 29, 2025, the entire contents of which are incorporated herein by reference.FIELD

[0002] The present disclosure relates to enhanced reporting procedure.BACKGROUND

[0003] 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.

[0004] The field of wireless communications has made significant progress with the introduction of 5G networks, particularly in enhancing network slicing capabilities through Artificial Intelligence (Al) and Machine Learning (ML) technologies. These advancements facilitate more efficient resource allocation, improved service differentiation, and optimized network management, for meeting the diverse requirements of modem applications and services. A fundamental aspect of sustaining optimal network performance lies in the assessment and feedback mechanisms concerning User Equipment (UE) performance metrics during handover processesbetween network nodes. This includes transitions between source nodes and target nodes, where maintaining service continuity and minimizing latency are paramount. Effective handover management is crucial as it directly impacts user experience and overall network reliability.

[0005] According to the 3rdGeneration Partnership Project (3GPP) Technical Specification (TS) 38.743, the optimization of AI / ML-driven network slicing models necessitates a systematic collection of targeted feedback from gNodeBs (gNBs). This feedback may be used for optimizing network operations and encompasses several key metrics, as detailed hereinafter.

[0006] Firstly, a metric associated with measured radio resource status per slice provides insights into availability and utilization of radio resources allocated to each network slice. Secondly, a measured slice available capacity, which includes understanding the capacity constraints of individual slices may be used for ensuring that Service Level Agreements (SLAs) are met. Lastly, performance feedback from UEs post-handover, which includes gathering data on LEE performance following handover events, most importantly, this feedback is a validation of the AIML based decisions made at the source node. The specification further emphasizes the importance of obtaining more granular LEE performance feedback. Such detailed insights may be helpful for accurately evaluating the performance of UEs associated with specific slices in operation. This level of granularity enables the entities involved in network slicing to make informed decisions regarding resource allocation, slice management, and overall network optimization strategies.SUMMARY

[0007] This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the disclosure. This summary is neither intendedto identify key or essential inventive concepts of the disclosure nor is it intended for determining the scope of the disclosure.

[0008] According to one embodiment of the present disclosure, a method is disclosed. The method includes configuring, by a source Next Generation Radio Access Network (NG-RAN) node, a data collection request message for a target NG-RAN node to indicate performance data for a User Equipment (UE) required. The data collection request message comprises a plurality of Information Elements (IES), the plurality of IES comprising at least one of a report filter for data collection, a report metric to filter map, and a measurement list for data collection. The method further includes receiving, by the source NG-RAN node, the performance data for the UE required from the target NG-RAN node based on the configured data collection request message.

[0009] According to one embodiment of the present disclosure, an apparatus is disclosed. The apparatus may configure a data collection request message for a target Next Generation Radio Access Network (NG-RAN) node to indicate performance data for a User Equipment (UE) required. The data collection request message comprises a plurality of Information Elements (IEs), the plurality of IEs comprising at least one of a report filter for data collection, a report metric to filter map, and a measurement list for data collection. The apparatus may receive the performance data for the UE required from the target NG-RAN node based on the configured data collection request message.

[0010] According to one embodiment of the present disclosure, a non-transitory computer- readable medium storing instructions. The one or more instructions are executed by an apparatus which comprises one or more processors. The one or more processors may configure a data collection request message to a target Next Generation Radio Access Network (NG-RAN) node toindicate performance data for a User Equipment (UE) required. The data collection request message comprises a plurality of Information Elements (IES), the plurality of IES comprising at least one of a report filter for data collection, a report metric to filter map, and a measurement list for data collection. The one or more processors may receive the performance data for the UE required from the target NG-RAN node based on the configured data collection request message.

[0011] To further clarify the advantages and features of the present disclosure, a more particular description of the disclosure will be rendered by reference to specific embodiments thereof, which are illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the disclosure and are therefore not to be considered limiting of its scope. The disclosure will be described and explained with additional specificity and detail in the accompanying drawings.BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Features, aspects, and advantages of embodiments of the disclosure will be described below with reference to the accompanying drawings, in which like reference numerals denote like elements, and wherein:FIG. 1 is a diagram of an example of an implementation environment in which systems and / or methods, described herein, may be implemented, according to an embodiment as disclosed herein;FIG. 2 is a sequence flow diagram illustrating a method for filtering mechanism to optimize a reporting process, according to an embodiment as disclosed herein;FIG. 3 is a flow diagram illustrating a method for evaluating and validating, based on the optimized data collection framework, an accuracy of at least one Artificial Intelligencemodel utilized in a handover decision-making process, according to an embodiment as disclosed herein;FIG. 4 is a flow diagram illustrating a method for applying a report filter for data collection, according to an embodiment as disclosed herein;FIG. 5 is a flow diagram illustrating a method for applying a report metric to filter map, according to an embodiment as disclosed herein; andFIG. 6 illustrates a diagram of example components of an apparatus, according to an embodiment as disclosed herein.DETAILED DESCRIPTION

[0013] 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, the flowchart and description of operations provided below relate to one of the various embodiments. It should be noted that it is possible to make other embodiments that do not exactly match the flowchart and its description. It is understood that in other embodiments 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).

[0014] It will be apparent that systems and / or methods, described herein, may be implemented in different forms of hardware, software, 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 are 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.

[0015] 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 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 dependent claim listed below may directly depend on only one claim, the disclosure of implementations includes each dependent claim in combination with every other claim in the claim set.

[0016] 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.” 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],” “[A] and / or [B],” or “at least one of [A] or [B]” are to be understood as including only A, only B, or both A and B.

[0017] 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.

[0018] In the present disclosure, specific tasks may be performed using Artificial Intelligence / Machine Learning (AI / ML) models. An AI / ML model is a model generated using one or more Al technologies, one or more ML algorithms 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.

[0019] 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. Model training 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.

[0020] Machine learning includes various types of learning methods such as supervised learning, unsupervised learning, reinforcement learning, semi-supervised learning, self-supervised learning, transudative 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, thestructure 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.

[0021] 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.

[0022] Specifications outlined during the RAN3#125-bis meeting facilitate the comprehensive calculation of critical performance metrics within mobile networks. These metrics comprise average packet delay for both uplink and downlink communications, average User Equipment (UE) throughput, and average packet loss specifically for downlink traffic. The calculations are structured to be performed as per Public Land Mobile Network Identity (PLMN) ID, delineating performance based on distinct Quality of Service (QoS) levels and supported Single Network Slice Selection Assistance Information (S-NSSAI). This framework allows for a nuanced understanding of network performance across various operational conditions and service requirements.

[0023] To achieve a detailed assessment of UE performance, three distinct granularity options have been proposed. Option 1 focuses on performance evaluation per S-NSSAI for each Protocol Data Unit (PDU) session, Option 2 aggregates metrics per S-NSSAI, and Option 3 organizes metrics according to QoS flow groups. This stratification serves the purpose of comparing the UE performance metrics at different granularities.

[0024] A collaborative working agreement has been established whereby UE performance metrics for each PDU session associated with a requested S-NSSAI are systematically reported from a target node (e.g., target gNB) back to a source node (e.g., source gNB). This feedback loop identifies whether additional granularities of UE performance measurement are viable for future implementations. The feedback loop is related to receiving, by the source gNB, a data collection update message from the target NG-RAN node, in response to transmitting a data collection request message. Furthermore, prior discussions have culminated in an agreement that posthandover (HO) from the source node to the target node, the target node may relay UE performance metrics back to the source node. This process is instrumental in enabling the source node to assess the effectiveness of its mobility decisions. The feedback mechanism not only aids in performance evaluation but also serves as a critical input for refining the artificial intelligence and machine learning models that underpin predictive analytics in network management. This iterative process enhances the overall robustness and reliability of network operations.

[0025] In the context of the data collection request message, the data collection request (refer to Section 9.1.3.26 in 3GPP TS 38.423) specifies that a report characteristic for data collection information Element (IE) is represented as a 32-bit string. A bitmap established by a source NG RAN node communicates to a target NG RAN node, which measurements (e.g., cell level or UE level measurements ) may be required. The cell level measurements, for example, the predicted radio resource status are by default at a per-cell granularity. But as mentioned earlier, some are UE-level measurements and some are cell-level measurements. The standard may delineate these metrics at a per-cell granularity by default. For example, nine bits may be identified, as detailed inTable 1 below.Table 1

[0026] It is anticipated that additional bits may be designated to accommodate new metrics or to represent existing metrics at varying granularities, addressing the needs of use cases, such asNetwork Slicing and Capacity and Coverage Optimization, as well as future applications.

[0027] This disclosure outlines several strategies / options (e g., option-1, option-2, and option-3) to ensure that data collection requests effectively reflect one or more requirements of both current and future applications of AI / ML, as described in conjunction with FIG. 2, FIG. 3, FIG. 4, and FIG. 5. In other words, the disclosed method / system introduces a filtering mechanism to optimize a reporting process. This filter is designed to ascertain the granularity at which one or more performance metrics may be reported by the target node (e.g., NG-RAN node2). The filter parameters may adhere to the specifications outlined in 3GPP TS 28.558. Furthermore, the chosen filter may consist of a combination of multiple filters to obtain the necessary sub-counters at the source node (e.g., source NG-RAN node).

[0028] Referring now to the drawings, and more particularly to FIGS. 1 to 6, where similar reference characters denote corresponding features consistently throughout the figures, there are shown preferred embodiments.

[0029] FIG. 1 is a diagram of an example of implementation environment 100 in which systems and / or methods, described herein, may be implemented, according to an embodiment as disclosed herein. The implementation environment 100 includes a User Equipment (UE) 110, a service environment 120, and a network 130. The service environment 120 includes one or more subenvironments 121-1 to 121-N (collectively and / or interchangeably referred hereinafter as 121). To illustrate this, FIG. 1 shows, for convenience, examples of a 1st sub-environment 121-1, a 2nd sub-environment 121-2, and an N111sub-environment 121-N (where N is any natural number).

[0030] The UE 110 is connected to the network 130, and the network 130 is connected to the service environment 120. The connections may be wired, wireless, or a combination of both wired and wireless. The UE 110 and the service environment 120 are connected via the network 130.

[0031] The UE 110 is a device that communicates with the service environment 120. The UE 110 receives information from the service environment 120 and / or sends information to the service environment 120. Also, the UE 110 may generate and / or store information to be transmitted, as necessary. Also, the UE 110 may store and / or process information that is received, as necessary.

[0032] The example FIG. 1 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.”

[0033] For example, the UE 110 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 smartphone, a radiotelephone, etc.), a wearable device (e g., a pair of smart glasses or a smart watch), or a similar device.

[0034] The service environment 120 is an environment that communicates with the UE 110 to provide one or more services. The service environment 120 receives information from the UE 110 and / or sends information to the UE 110. Also, the service environment 120 may generate and / or store information to be transmitted, as necessary. Also, the service environment 120 may store and / or process information that is received, as necessary. For example, the service environment 120 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 110; it may also be provided to devices other than the UE110. For example, based on communication from the UE 110, the service may perform processes such as anomaly detection or traffic analysis and notify the results to a predetermined destination.

[0035] The example FIG. 1 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”.

[0036] The one or more services provided by the service environment 120 is not specifically limited and can be adjusted according to the embodiments. For example, the one or more services may include a service that provides information to the UE 110, a service that stores information from the UE 110, or a service that performs processing based on information from the UE 110 and returns the results of the processing.

[0037] In an embodiment, the service environment 120 may also provide computing resources such 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.

[0038] 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.

[0039] The service environment 120 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 120 can be determined as appropriate. Additionally, if the service environment 120 includes one or more sub-environments 121, the placement of devices can be determined based on predetermined policies for each sub-environment 121. For example, devices related to the first service may be placed in the 1st sub-environment 121-1, and devices related to the second service may be placed in the 2nd sub -environment 121-2. In another example, devices expected to have a higher load than a predetermined threshold may be placed in the 1st subenvironment 121-1, while devices expected to have a lower load than the predetermined threshold may be placed in the 2nd sub-environment 121-2. In this way, specific devices can be placed inspecific sub -environments 121. Conversely, each sub-environment 121 can be specialized for a particular purpose.

[0040] 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.

[0041] The network 130 is a network that exchanges information between the UE 110 and the service environment 120. The network 130 includes one or more wired and / or wireless networks.

[0042] For example, the network 130 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), a telephone 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.

[0043] The network 130 can be a part of a network. For example, in a 5G network that includes a RAN, a transport network, and a core network, the network 130 can be at least one of the RAN, the transport network, or the core network. For example, the service environment 120 could be in the core network, in which case the network 130 could correspond to a network that is a combination of a RAN and a transport network and is part of the 5G network.

[0044] The number and arrangement of devices and networks shown in FIG. 1 are provided as an example. It should be understood that any changes that may be implemented by those skilled inthe art, such as the addition or rearrangement of well-known devices or networks at the time of implementation, are included in this disclosure.

[0045] FIG. 2 is a sequence flow diagram illustrating a method 200 for a filtering mechanism to optimize the reporting process, according to an embodiment as disclosed herein. The sequence flow diagram includes several operations outlined as follows.

[0046] At operation 203, the method 200 includes transmitting, by the source NG-RAN node 201 (NG-RAN nodei), a request message (e.g., data collection request message) to the target NG-RAN node 202 (NG-RAN node2) to indicate performance data for the UE. In one embodiment, the source NG-RAN node 201 and the target NG-RAN node 202 are associated with the network 130 (not shown in FIG). The request message may include a plurality of Information Elements (IES). The plurality of IEs may include, for example, a report characteristic for data collection, a report filter for data collection (as option- 1), a report metric to filter map (as option-2), a measurement list for data collection (as option-3), and a predefined IE (as defined in the existing standard). At operation 204, the method 200 includes processing, by the target NG-RAN node 202, one or more operations for collecting the performance data based on the request message (e.g., when one or more exit conditions / trigger conditions are met). At operation 205, the method 200 includes receiving, by the source NG-RAN node 201, a response message (e.g., data collection update message) from the target NG-RAN node 202 based on the transmitted request message.

[0047] In one or more embodiments, the disclosed method introduces the addition of a new IE, the report filter for data collection (as option-1), to the data collection request, as illustrated in Table 2 below. This report filter for data collection IE aims to specify filters applicable to the metrics delineated in the report characteristics for data collection.Table 2

[0048] For instance, in the current context of network slicing, if the UE performance feedback may be desired at the PDU session level, the fdter bitmap may be represented as 0000010000000000. Conversely, if the UE performance feedback is desired at the DRB per PDU session level, the corresponding filter bitmap maybe 0000110000000000. The selected filter is then applied to the metrics specified in the report characteristics for data collection and communicated to the source NG-RAN node 201.

[0049] In one or more embodiments, in the context of an enhanced filtering mechanism (as option-2), to ensure that the filter is only applied to the relevant metrics selected in the reportcharacteristics for data collection, the disclosed method proposes the implementation of an additional bitmap. This bitmap may indicate which specific metrics the filter applies to, as shown in the Table 3 below. This proposal aims to maintain backward compatibility while enhancing accuracy.Table 3

[0050] To illustrate this, consider the following scenario: If the report characteristics for data collection specify bits 2-7 for reporting: a. Second Bit = Predicted number of active UEs;b. Third Bit = Predicted RRC Connections; c. Fourth Bit = Average UE throughput DL; d. Fifth Bit = Average UE throughput UL; e. Sixth Bit = Average packet delay; and f. Seventh Bit = Average packet loss DL.

[0051] If the NG-RAN requests information at the QoS level, the report filter for data collection IE maybe 0001000000000000. Given that this filter is not universally applicable to all metrics requested in the report characteristics for data collection, the proposed IE, i.e., the report metric to filter map, may be utilized to indicate which specific metrics (bits) of the report characteristics bitmap for data collection pertains to. In this example, if the metric is relevant to bits 4-7 of the report characteristics for data collection, the report metric to filter map bitmap may be represented as 0001111000000000...0000.

[0052] In one or more embodiments, in the context of a comprehensive measurement list approach (as option 3), the disclosed method proposes the adoption of a measurement list comprising two components: the first to specify the metric of interest, and the second to indicate the applicable filter or granularity of reporting, as depicted in Table 4 below.

[0053] In one or more embodiments, the measurement list for data collection identifies a measurement list to be reported for data collection. The measurement list for data collection may include a collection of measurement items, each comprising a measurement identifier and a corresponding filter.Table 4

[0054] This approach is designed to be future-proof and provides an accurate representation of the requirements from the source NG-RAN node 201 to the target NG-RAN node 202. The measurement list may consist of multiple measurement items, each containing a measurement identifier alongside a corresponding filter.

[0055] For example, the measurements that may be required at the cell level include a predicted number of active UEs and predicted RRC connections, while the measurements needed at the DRBlevel encompass average UE throughput DL, average packet delay, and average packet loss DL.The resulting measurement list may be structured as shown in Table 5 below.Table 5

[0056] The advantages of utilizing this IE include enhanced flexibility in selecting the granularity of cell and UE performance metric feedback based on specific use case requirements. Furthermore, it accommodates one or more future use cases without restrictions on the number of metrics to report. If backward compatibility is deemed critical, the report characteristics for data collection can be preserved for reporting cell-level measurements. Overall, the implementation of option-3 to enhance the data collection request, as it represents a forward-looking and adaptable solution.

[0057] In one or more embodiments, the proposed options (e.g., option-1, option-2, and option-3) for enhancing the data collection request offer several advantages, for example, which are mentioned herein. The option-1 introduces a standardized report filter for data collection, providing clarity and simplicity in applying filters to metrics. The option-2 enhances this by allowing targeted filtering, ensuring that filters are applied only to relevant metrics, thereby improving data accuracy while maintaining backward compatibility. This option-2 minimizes irrelevant data reporting and offers flexibility in reporting granularity.

[0058] In addition, the option-3 presents a comprehensive measurement list approach, designed to be future-proof and scalable. This method accommodates varying use cases without limitations onthe number of metrics reported, allowing for tailored granularity of UE performance feedback. Each measurement item may be clearly defined with its corresponding filter, reducing ambiguity and optimizing resource utilization by ensuring only relevant data is collected, ultimately contributing to a more optimized reporting process.

[0059] FIG. 3 is a flow diagram illustrating a method 300 for evaluating and validating, based on the optimized data collection framework, an accuracy of at least one Artificial Intelligence (Al) model utilized in a handover decision-making process, according to an embodiment as disclosed herein.

[0060] The method 300 may execute multiple operations to evaluate and validate the accuracy of the at least one Al model utilized in the handover decision-making process, which are given below.

[0061] At operation 301, the method 300 includes configuring, by the source NG-RAN node 201, the data collection request message for the target NG-RAN node 202 to indicate performance data for the UE required, which may relate to operation 203. The collection request message comprises a plurality of Information Elements (IES). The plurality of IES may include, for example, at least one of the report filter for data collection, the report metric to filter map, the measurement list for data collection, as described in conjunction with FIG. 2.

[0062] At operation 302, the method 300 includes receiving, by the source NG-RAN node 201, the performance data for the UE required from the target NG-RAN node 202 based on the configured data collection request message, which may relate to operation 205.

[0063] At operation 303, the method 300 includes prior to performing at least one of operation (e.g., 304 and 305), wherein one or more UE performance metrics may be evaluated by the targetNG-RAN node, based on the performance data included in the data collection update message.

[0064] At operation 304, the method 300 includes determining, based on the one or more evaluatedUE performance metrics, whether a handover decision process for the UE from the source NG-RAN node 201 to the target NG-RAN node 202 is appropriate. At operation 305, the method 300 includes validating, based on the one or more evaluated UE performance metrics, the accuracy of the Al model utilized for the handover decision process.

[0065] FIG. 4 is a flow diagram illustrating a method 400 for applying the report fdter for data collection, according to an embodiment as disclosed herein.

[0066] At operation 401, the method 400 includes determining a granularity of UE performance feedback (e.g., QoS, DRB, PDU, etc.) that may be required at the source NG-RAN node 201. At operation 402, the method 400 includes enabling one or more bits corresponding to the predefined bit assignment (refer to Table 2 “Report filter for data collection”), based on the granularity of determined UE performance feedback, to identify the one or more filters. The predefined bit assignment may include, for example, a first bit which represents a granularity, a second bit which corresponds to the UE, a third bit which corresponds to a 5G Quality Indicator (5QI), a fourth bit which corresponds to a quality of service (QoS). In addition, a fifth bit which corresponds to the DRB, a sixth bit corresponds to the PDU, a seventh bit which corresponds to the SNSSAI. Moreover, an eighth bit which corresponds to a cell, a ninth bit which corresponds to the PLMN, and any bits beyond the ninth are disregarded by the target NG-RAN node 202. At operation 403, the method 400 includes applying the one or more identified filters to each metric represented within the report characteristics for data collection.

[0067] In one embodiment, as described in conjunction with FIG. 2, the report filter for data collection identifies one or more filters to be applied to one or more metrics represented within thereport characteristics for data collection. In other words, the disclosed method 400 utilizes the same filter, which may be uniformly applied across all metrics identified in the report characteristics for data collection.

[0068] In addition, the report filter for data collection indicates one or more measurements to be reported to the source NG-RAN node 201 by utilizing one or more filters (refer to Table 2). A size of the report filter for data collection comprises a 16-bit string with a predefined bit assignment.

[0069] FIG. 5 is a flow diagram illustrating a method 500 for applying the report metric to filter map, according to an embodiment as disclosed herein.

[0070] At operation 501, the method 500 includes determining the granularity of UE performance feedback that may be required at the source NG-RAN node 201. At operation 502, the method 500 includes enabling one or more bits corresponding to the predefined bit assignment, based on the granularity of determined UE performance feedback, to identify the one or more filters. At operation 503, the method 500 includes applying the one or more identified filters to the performance metric represented within the report characteristics for data collection by utilizing the report metric to filter map.

[0071] In one embodiment, as described in conjunction with FIG. 2, the report metric to filter map indicates that each position within the bitmap corresponds to the performance metric (e g., UE performance metrics) to which the report filter for data collection is to be applied by the target NG-RAN node 202 during the reporting process (refer to Table 3).

[0072] In one embodiment, a value of zero within the bitmap is indicative of a default state related to no filter being applied.

[0073] In one embodiment, a value of 1 within the bitmap indicates that the corresponding selected filter has been applied.

[0074] In one embodiment, a size of the report metric to filter map comprises a 32-bit string with the predefined bit assignment.

[0075] FIG. 6 illustrates a diagram of example components of an apparatus 600, according to an embodiment as disclosed herein. As shown in FIG. 6, the apparatus 600 comprises a processor 610, a memory 620, a storage component 630, an input component 640, an output component 650, a communication interface 660, and a bus 670. In one embodiment, the apparatus 600 may relate to at least one of the source NG-RAN node 201, the target NG-RAN node 202, or any other network device.

[0076] The processor 610, as used herein, means any type of computational circuit that may comprise hardware elements and software elements. The processor 610 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 610 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.

[0077] The memory 620 includes a non-transitory computer readable medium. Memory 620 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 610. The memory 620 comprises machine-readable instructions which are executable by the processor 610. Thesemachine-readable instructions when executed by the processor 610 cause the processor 610 to perform one or more method steps of an embodiment described above.

[0078] The storage component 630 stores information and / or software related to the operation and use of the apparatus 600. For example, the storage component 630 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.

[0079] The input component 640 is configured to receive information, such as user input. For example, the input component 640 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 640 may include a sensor for sensing information (e.g., a global positioning system (GPS), an accelerometer, a gyroscope, and / or an actuator).

[0080] The output component 650 is configured to provide output information from the apparatus 600. For example, the output component 650 may be, but is not limited to, a display, a speaker, instructions to an external device, and / or one or more light-emitting diodes (LEDs).

[0081] The communication interface 660 is an interface that provides a communication connection to other devices, such as external devices and internal devices. The connection by the communication interface 660 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 apparatus 600 and other devices. In other words, the standard of the communication interface 660 is not limited.

[0082] The bus 670 acts as an interconnect between the processor 610, the memory 620, the storage component 630, the input component 640, the output component 650, and the communication interface 660 of the apparatus 600. The bus 670 may include a wired interconnection or a wireless interconnection.

[0083] The number and arrangement of components shown in FIG. 6 are provided as an example. In practice, the apparatus 600 may include additional components, fewer components, different components, or differently arranged components than those shown in FIG. 6. Additionally, or alternatively, a set of components (e.g., one or more components) of the apparatus 600 may perform one or more functions described as being performed by another set of components of the apparatus 600. Further, one or more method steps described in any of the embodiments may be performed utilizing the apparatus 600 in communication with one another.

[0084] Examples of the techniques and apparatus described herein include, but are not limited to, the following enumerated embodiments:[1] A method comprising: configuring, by a source Next Generation Radio Access Network (NG-RAN) node, a data collection request message for a target NG-RAN node to indicate performance data for a User Equipment (UE) required, wherein the data collection request message comprises a plurality of Information Elements (TEs), the plurality of IES comprising at least one of a report filter for data collection, a report metric to filter map, and a measurement list for data collection; andreceiving, by the source NG-RAN node, the performance data for the UE required from the target NG-RAN node based on the configured data collection request message.[2] The method as described in [1], further comprising: wherein one or more UE performance metrics is evaluated by the target NG-RAN node, based on the performance data from the source NG RAN node.[3] The method as described in any of [l]-[2], further comprising: performing, by the source NG-RAN node, the at least one of: determining, based on the one or more evaluated UE performance metrics, whether a handover decision process for the UE from the source NG-RAN node to the target NG-RAN node is appropriate, or validating, based on the one or more evaluated UE performance metrics, an accuracy of at least one Artificial Intelligence model utilized for the handover decision process.[4] The method as described in any of [l]-[3], wherein the report filter for data collection indicates one or more measurements to be reported to the source NG-RAN node by utilizing one or more filters.[5] The method as described in any of [l]-[4],wherein a size of the report filter for data collection comprises a 16-bit string with a predefined bit assignment.[6] The method as described in any of [l]-[5], wherein the predefined bit assignment comprises at least one of: a first bit which represents a granularity , a second bit which corresponds to the UE, a third bit which corresponds to a 5G Quality Indicator (5QI), a fourth bit which corresponds to a quality of service (QoS), a fifth bit which corresponds to a Data Radio Bearer (DRB), a sixth bit corresponds to a Protocol Data Unit (PDU), a seventh bit which corresponds to a Single Network Slice Selection Assistance Information (SNSSAI), an eighth bit which corresponds to a cell, or a ninth bit which corresponds to a Public Land Mobile Network (PLMN).[7] The method as described in any of [l]-[6], wherein the report filter for data collection identifies one or more filters to be applied to one or more metrics represented within the report characteristics for data collection.[8] The method as described in any of [l]-[7], wherein the one or more filters to be applied to the one or more metrics represented within the report characteristics for data collection comprises: determining a granularity of UE performance feedback may require at the source NG-RAN node; andenabling one or more bits corresponding to a predefined bit assignment, based on the granularity of determined UE performance feedback, to identify the one or more filters, wherein the one or more identified filters applies to each metric represented within the report characteristics for data collection.[9] The method as described in any of [l]-[8], wherein the report metric to filter map indicates that each position within a bitmap corresponds to a performance metric to which the report filter for data collection is to be applied by the target NG-RAN node during a reporting process; wherein a value of zero within the bitmap is indicative of a default state related to no filter being applied; wherein a value of 1 within the bitmap indicates that the corresponding selected filter has been applied; and wherein a size of the report metric to filter map comprises a 32-bit string with a predefined bit assignment.

[0010] The method as described in any of [l]-[9], wherein the performance metric to which the report filter for data collection is to be applied by the target NG-RAN node during the reporting process comprises: determining a granularity of UE performance feedback may require at the sourceNG-RAN node;enabling one or more bits corresponding to the predefined bit assignment, based on the granularity of determined UE performance feedback, to identify the one or more filters; and applying the one or more identified filters to the performance metric represented within the report characteristics for data collection by utilizing the report metric to filter map.

[0011] The method as described in any of [l]-

[0010] , wherein the measurement list for data collection identifies a report measurement list to be reported for data collection; and wherein the measurement list for data collection comprises a collection of measurement items, each measurement item comprising a measurement identifier and a corresponding filter.

[0012] An apparatus configured to: configure a data collection request message for a target Next Generation Radio Access Network (NG-RAN) node to indicate performance data for a User Equipment (UE) required, wherein the data collection request message comprises a plurality of Information Elements (IES), the plurality of IES comprising at least one of a report filter for data collection, a report metric to filter map, and a measurement list for data collection; andreceive the performance data for the UE required from the target NG-RAN node based on the configured data collection request message.

[0013] The apparatus as described in

[0012] , the apparatus is further configured to: wherein one or more UE performance metrics is evaluated by the target NG-RAN node, based on the performance data from the source NG RAN node.

[0014] The apparatus as described in

[0012] , the apparatus is further configured to: perform at least one of: determine, based on the one or more evaluated UE performance metrics, whether a handover decision process for the UE from the source NG-RAN node to the target NG-RAN node is appropriate, or validate, based on the one or more evaluated UE performance metrics, an accuracy of at least one Artificial Intelligence (Al) model utilized for the handover decision process.

[0015] The apparatus as described in any of

[0012] -

[0014] , wherein the report filter for data collection indicates one or more measurements to be reported to the source NG-RAN node by utilizing one or more filters.

[0016] The apparatus as described in any of

[0012] -

[0015] ,wherein a size of the report filter for data collection comprises a 16-bit string with a predefined bit assignment.

[0017] The apparatus as described in any of

[0012] -

[0016] , wherein the predefined bit assignment comprises at least one of: a first bit which represents a granularity, a second bit which corresponds to the UE, a third bit which corresponds to a 5G Quality Indicator (5QI), a fourth bit which corresponds to a quality of service (QoS), a fifth bit which corresponds to a Data Radio Bearer (DRB), a sixth bit which corresponds to a Protocol Data Unit (PDU), a seventh bit which corresponds to a Single Network Slice Selection Assistance Information (SNSSAI), an eighth bit which corresponds to a cell, a ninth bit corresponds to a Public Land Mobile Network (PLMN).

[0018] The apparatus as described in any of

[0012] -

[0017] , wherein the report filter for data collection identifies one or more filters to be applied to one or more metrics represented within a report characteristics for data collection.

[0019] The apparatus as described in any of

[0012] -

[0018] , wherein the one or more filters to be applied to the one or more metrics represented within the report characteristics for data collection, the apparatus is configured to: determine a granularity of UE performance feedback may require at the source NG- RAN node; andenable one or more bits corresponding to a predefined bit assignment, based on the granularity of determined UE performance feedback, to identify the one or more filters, wherein the one or more identified filters applies to each metric represented within the report characteristics for data collection.

[0020] The apparatus as described in any of

[0012] -

[0019] , wherein the report metric to filter map indicates that each position within a bitmap corresponds to a performance metric to which the report filter for data collection is to be applied by the target NG-RAN node during a reporting process; wherein a value of zero within the bitmap is indicative of a default state related to no filter being applied; wherein a value of 1 within the bitmap indicates that the corresponding selected filter has been applied; and wherein a size of the report metric to filter map comprises a 32-bit string with a predefined bit assignment.

[0021] The apparatus as described in any of

[0012] -

[0020] , wherein the performance metric to which the report filter for data collection is to be applied by the target NG-RAN node during the reporting process, the apparatus is configured to: determine a granularity of UE performance feedback may require at the source NG-RAN node;enable one or more bits corresponding to the predefined bit assignment, based on the granularity of determined UE performance feedback, to identify one or more filters; and apply the one or more identified filters to the performance metric represented within the report characteristics for data collection by utilizing the report metric to filter map.

[0022] The apparatus as described in any of

[0012] -

[0021] , wherein the measurement list for data collection identifies a report measurement list to be reported for data collection; and wherein the measurement list for data collection comprises a collection of measurement items, each measurement item comprising a measurement identifier and a corresponding filter.

[0023] A non-transitory computer-readable medium storing instructions, the instructions comprising: one or more instructions that, when executed by an apparatus, the apparatus comprising one or more processors, cause the one or more processors to: configure a data collection request message for a target Next GenerationRadio Access Network (NG-RAN) node to indicate performance data for a User Equipment (UE) required, wherein the data collection request message comprises a plurality ofInformation Elements (lEs), the plurality of IES comprising at least one ofa report filter for data collection, a report metric to filter map, and a measurement list for data collection; and receive the performance data for the UE required from the target NG-RAN node based on the configured data collection request message.

[0085] The embodiments disclosed herein can be implemented through at least one software program running on at least one hardware device and performing network management functions to control the elements. The elements can be at least one of a hardware device or a combination of hardware devices and software modules.

[0086] While specific language has been used to describe the disclosure, any limitations arising on account of the same are not intended. As would be apparent to a person in the art, various working modifications may be made to the method in order to implement the inventive concept as taught herein.

[0087] The drawings and the forgoing description give examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, orders of processes described herein may be changed and are not limited to the manner described herein.

[0088] Moreover, the actions of any flow diagram need not be implemented in the order shown; nor do all of the acts necessarily need to be performed. Also, those acts that are not dependent on other acts may be performed in parallel with the other acts. The scope of embodiments is by no means limited by these specific examples. Numerous variations, whether explicitly given in thespecification or not, such as differences in structure, dimension, and use of material, are possible. The scope of embodiments is at least as broad as given by the following claims.

[0089] Benefits, other advantages, and solutions to problems have been described above with regard to specific embodiments. However, the benefits, advantages, solutions to problems, and any component(s) that may cause any benefit, advantage, or solution to occur or become more pronounced are not to be construed as a critical, required, or essential feature or component of any or all the claims.

[0090] The foregoing description of the specific embodiments will so fully reveal the general nature of the embodiments herein that others can, by applying current knowledge, readily modify and / or adapt for various applications such specific embodiments without departing from the generic concept, and, therefore, such adaptations and modifications should and are intended to be comprehended within the meaning and range of equivalents of the disclosed embodiments. It is to be understood that the phraseology or terminology employed herein is for the purpose of description and not of limitation. Therefore, while the embodiments herein have been described in terms of at least one embodiment, those skilled in the art will recognize that the embodiments herein can be practiced with modification within the spirit and scope of the embodiments as described herein.

Claims

CLAIMSWe claim:

1. A method comprising: configuring, by a source Next Generation Radio Access Network (NG-RAN) node, a data collection request message for a target NG-RAN node to indicate performance data for a User Equipment (UE) required, wherein the data collection request message comprises a plurality of Information Elements (IES), the plurality of IES comprising at least one of a report filter for data collection, a report metric to filter map, or a measurement list for data collection; and receiving, by the source NG-RAN node, the performance data for the UE required from the target NG-RAN node based on the configured data collection request message.

2. The method as claimed in claim 1, further comprising: wherein one or more UE performance metrics is evaluated by the target NG-RAN node, based on the performance data from the source NG RAN node.

3. The method as claimed in claim 1, further comprising: performing, by the source NG-RAN node, at least one of: determining, based on the one or more evaluated UE performance metrics, whether a handover decision process for the UE from the source NG-RAN node to the target NG-RAN node is appropriate, orvalidating, based on the one or more evaluated UE performance metrics, an accuracy of at least one Artificial Intelligence model utilized for the handover decision process.

4. The method as claimed in claim 1, wherein the report filter for data collection indicates one or more measurements to be reported to the source NG-RAN node by utilizing one or more filters.

5. The method as claimed in claim 1, wherein a size of the report filter for data collection comprises a 16-bit string with a predefined bit assignment.

6. The method as claimed in claim 5, wherein the predefined bit assignment comprises at least one of a first bit which represents a granularity, a second bit which corresponds to the UE, a third bit which corresponds to a 5G Quality Indicator (5QI), a fourth bit which corresponds to a quality of service (QoS), a fifth bit which corresponds to a Data Radio Bearer (DRB), a sixth bit corresponds to a Protocol Data Unit (PDU), a seventh bit which corresponds to a Single Network Slice Selection Assistance Information (SNSSAI), an eighth bit which corresponds to a cell, or a ninth bit which corresponds to a Public LandMobile Network (PLMN).

7. The method as claimed in claim 1, wherein the report fdter for data collection identifies one or more filters to be applied to one or more metrics represented within a report characteristics for data collection.

8. The method as claimed in claim 7, wherein the one or more filters to be applied to the one or more metrics represented within the report characteristics for data collection comprises: determining a granularity of UE performance feedback required at the source NG- RAN node; and enabling one or more bits corresponding to a predefined bit assignment, based on the granularity of determined UE performance feedback, to identify the one or more filters, wherein the one or more identified filters apply to each metric represented within the report characteristics for data collection.

9. The method as claimed in claim 1, wherein the report metric to filter map indicates that each position within a bitmap corresponds to a performance metric to which the report filter for data collection is to be applied by the target NG-RAN node during a reporting process; wherein a value of zero within the bitmap is indicative of a default state related to no filter being applied; wherein a value of 1 within the bitmap indicates that the corresponding selected filter has been applied; andwherein a size of the report metric to filter map comprises a 32-bit string with a predefined bit assignment.

10. The method as claimed in claim 9, wherein the performance metric to which the report filter for data collection is to be applied by the target NG-RAN node during the reporting process comprises: determining a granularity of UE performance feedback required at the source NG- RAN node; enabling one or more bits corresponding to the predefined bit assignment, based on the granularity of determined UE performance feedback, to identify one or more filters; and applying the one or more identified filters to the performance metric represented within the report characteristics for data collection by utilizing the report metric to filter map.

11. The method as claimed in claim 1, wherein the measurement list for data collection identifies a report measurement list to be reported for data collection; and wherein the measurement list for data collection comprises a collection of measurement items, each measurement item comprising a measurement identifier and a corresponding filter.

12. An apparatus configured to: configure a data collection request message for a target Next Generation Radio Access Network (NG-RAN) node to indicate performance data for a User Equipment (UE) required, wherein the data collection request message comprises a plurality of Information Elements (IES), the plurality of IES comprising a report characteristics for data collection, and one or more of a report filter for data collection, a report metric to filter map, and a measurement list for data collection; and receive the performance data for the UE required from the target NG-RAN node based on the configured data collection request message.

13. The apparatus as claimed in claim 12, the apparatus is further configured to: prior to perform at least one of, wherein one or more UE performance metrics is evaluated by the target NG-RAN node, based on the performance data from the source NG- RAN node; perform the at least one of : determine, based on the one or more evaluated UE performance metrics, whether a handover decision process for the UE from the source NG-RAN node to the target NG-RAN node is appropriate, or validate, based on the one or more evaluated UE performance metrics, an accuracy of at least one Artificial Intelligence (Al) model utilized for the handover decision process.

14. The apparatus as claimed in claim 12, wherein the report filter for data collection indicates one or more measurements to be reported to the source NG-RAN node by utilizing one or more filters; and wherein a size of the report filter for data collection comprises a 16-bit string with a predefined bit assignment.

15. The apparatus as claimed in claim 14, wherein the predefined bit assignment comprises at least one of: a first bit which represents a granularity, a second bit which corresponds to the UE, a third bit which corresponds to a 5G Quality Indicator (5QI), a fourth bit which corresponds to a quality of service (QoS), a fifth bit which corresponds to a Data Radio Bearer (DRB), a sixth bit which corresponds to a Protocol Data Unit (PDU), a seventh bit which corresponds to a Single Network Slice Selection Assistance Information (SNSSAI), an eighth bit which corresponds to a cell, or a ninth bit corresponds to a Public Land Mobile Network (PLMN).

16. The apparatus as claimed in claim 12, wherein the report filter for data collection identifies one or more filters to be applied to one or more metrics represented within the report characteristics for data collection.

17. The apparatus as claimed in claim 16, wherein to identify the one or more filters to be applied to the one or more metrics represented within the report characteristics for data collection, the apparatus is configured to: determine a granularity of UE performance feedback required at the source NG- RAN node; and enable one or more bits corresponding to a predefined bit assignment, based on the granularity of determined UE performance feedback, to identify the one or more filters, wherein the one or more identified filters apply to each metric represented within the report characteristics for data collection.

18. The apparatus as claimed in claim 12, wherein the report metric to filter map indicates that each position within a bitmap corresponds to a performance metric to which the report filter for data collection is to be applied by the target NG-RAN node during a reporting process; wherein a value of zero within the bitmap is indicative of a default state related to no filter being applied; wherein a value of 1 within the bitmap indicates that the corresponding selected filter has been applied; and wherein a size of the report metric to filter map comprises a 32-bit string with a predefined bit assignment.

19. The apparatus as claimed in claim 18, wherein the performance metric to which the report filter for data collection is to be applied by the target NG-RAN node during the reporting process, the apparatus is configured to: determine a granularity of UE performance feedback required at the source NG- RAN node; enable one or more bits corresponding to the predefined bit assignment, based on the granularity of determined UE performance feedback, to identify one or more filters; and apply the one or more identified filters to the performance metric represented within the report characteristics for data collection by utilizing the report metric to filter map.

20. The apparatus as claimed in claim 12, wherein the measurement list for data collection identifies a report measurement list to be reported for data collection; and wherein the measurement list for data collection comprises a collection of measurement items, each measurement item comprising a measurement identifier and a corresponding filter.

21. A non-transitory computer-readable medium storing instructions, the instructions comprising: one or more instructions that, when executed by an apparatus, the apparatus comprising one or more processors, cause the one or more processors to:configure a data collection request message for a target Next Generation Radio Access Network (NG-RAN) node to indicate performance data for a User Equipment (UE) required, wherein the data collection request message comprises a plurality of Information Elements (IES), the plurality of IES comprising at least one of a report filter for data collection, a report metric to filter map, and a measurement list for data collection; and receive the performance data for the UE required from the target NG-RAN node based on the configured data collection request message.

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