WO2022221495A1

WO2022221495A1 - Machine learning support for management services and management data analytics services

Info

Publication number: WO2022221495A1
Application number: PCT/US2022/024758
Authority: WO
Inventors: Yizhi Yao; Joey Chou
Original assignee: Intel Corporation
Priority date: 2021-04-15
Filing date: 2022-04-14
Publication date: 2022-10-20

Abstract

This disclosure describes systems, methods, and devices related to machine learning (ML) for management service (MnS). A device may obtain input data related to network and service within a 5G system (5GS) to provide MDA capability to an MnS consumer within the 5GS. The device may generate one or more MDA reports, wherein the one or more MDA reports comprise one or more common information elements, at least one MDA type associated with the MDA capability, and one or more MDA type specific information elements. The device may cause to send the one or more MDA reports to the MnS consumer.

Description

MACHINE LEARNING SUPPORT FOR MANAGEMENT SERVICES AND MANAGEMENT DATA ANALYTICS SERVICES

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application claims the benefit of U.S. Provisional Application No. 63/175,482, filed April 15, 2021, and U.S. Provisional Application No. 63/177,757, filed April 21, 2021, all disclosures of which are incorporated herein by reference as if set forth in full.

TECHNICAL FIELD

This disclosure generally relates to systems and methods for wireless communications and, more particularly, to machine learning (ML) for management service (MnS) and management data analytics service (MDAS).

BACKGROUND

Wireless devices are becoming widely prevalent and are increasingly requesting access to wireless channels. In a 5G system (5GS), management data analytics (MDA) provides capabilities of processing the analytics inputs related to network and service.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a network diagram illustrating an example network environment for machine learning (ML) for management service (MnS), in accordance with one or more example embodiments of the present disclosure.

FIGs. 2A-2C depict illustrative schematic diagrams for ML for MnS, in accordance with one or more example embodiments of the present disclosure.

FIGs. 3-5 depict illustrative schematic diagrams for ML for MnS, in accordance with one or more example embodiments of the present disclosure.

FIG. 6 depicts an illustrative schematic diagram for ML for MnS, in accordance with one or more example embodiments of the present disclosure.

FIG. 7 illustrates a flow diagram of a process for an illustrative ML for MnS system, in accordance with one or more example embodiments of the present disclosure.

FIG. 8 illustrates an example network architecture, in accordance with one or more example embodiments of the present disclosure.

FIG. 9 schematically illustrates a wireless network, in accordance with one or more example embodiments of the present disclosure.

FIG. 10 illustrates components of a computing device, in accordance with one or more example embodiments of the present disclosure. PET ATT, ED DESCRIPTION

The following detailed description refers to the accompanying drawings. The same reference numbers may be used in different drawings to identify the same or similar elements. In the following description, for purposes of explanation and not limitation, specific details are set forth such as particular structures, architectures, interfaces, techniques, etc. in order to provide a thorough understanding of the various aspects of various embodiments. However, it will be apparent to those skilled in the art having the benefit of the present disclosure that the various aspects of the various embodiments may be practiced in other examples that depart from these specific details. In certain instances, descriptions of well-known devices, circuits, and methods are omitted so as not to obscure the description of the various embodiments with unnecessary detail. For the purposes of the present document, the phrases “A or B” and “A/B” mean (A), (B), or (A and B).

Machine learning (ML) capabilities may be used to support one or more management services (MnSs), the generic aspects (including scenarios, requirements, and solutions) of ML capabilities for supporting the various MnSs are missing in 3GPP TSs.

Specifically, ML capabilities may be used for management data analytics service (MDAS). However, the relation between ML and MDAS, requirements and solutions to enable the ML for MDAS need to be defined. Accordingly, some embodiments of the present disclosure define methods and solutions to enable ML for MnS and MDAS.

The above descriptions are for purposes of illustration and are not meant to be limiting. Numerous other examples, configurations, processes, algorithms, etc., may exist, some of which are described in greater detail below. Example embodiments will now be described with reference to the accompanying figures.

FIG. 1 depicts an illustrative schematic diagram for ML for MnS, in accordance with one or more example embodiments of the present disclosure.

In one or more embodiments, a ML for MnS system may facilitate general aspects of ML support for MnS.

In one or more embodiments, an MnS (e.g., MDAS) may be supported by ML capabilities.

In one or more embodiments, a ML for MnS system may provide ML capabilities for

MnS.

In some embodiments, when ML is used for an MnS, the following ML capabilities are provided for supporting the MnS as illustrated in FIG. 1. In some embodiments, the ML capabilities may be provided by a producer to a consumer, and the ML model needs to be deployed in the ML capability producer. How the ML model is deployed is not addressed in the present document.

In one or more embodiments, in ML model training, ML capability producer trains the ML model (e.g., to train the algorithm of the ML model) to be able to provide the expected output when processing the input for an MnS. The ML capability producer may train the ML model based on the training data (including the training input and the expected output) provided by the consumer, and provide the training report to the consumer. The ML capability producer may re-train the ML model based on the validation feedback, including training report validation feedback and processing output validation feedback, provided by the consumer.

In one or more embodiments, in data processing, the ML capability producer processes the input data using the trained ML model and generates the processing output for an MnS. The ML capability producer provides the processing output to the consumer.

In one or more embodiments, in validation, the ML capability consumer may validate the training report and/or the processing output related to an MnS and provides validation feedback to the producer. The training report validation feedback may indicate whether or not the training has met the expectation, and the processing output validation feedback may indicate whether the output is erroneous or accurate.

It is understood that the above descriptions are for purposes of illustration and are not meant to be limiting.

In one or more embodiments, a ML for MnS system may facilitate a relation between ML and MnS. The ML capabilities may be provided to support MnS in the following possible ways:

1) MnS producer acts as ML capability consumer. The MnS producer acts as ML capability consumer as illustrated in FIG. 2A, and does not expose the ML capabilities to an MnS consumer.

2) MnS producer acts as ML capability producer and exposes ML capabilities to an MnS consumer. The MnS producer acts as ML capability producer as illustrated in FIG. 2B, and exposes the ML capabilities to an MnS consumer (e.g., the MnS consumer also acts as ML capability consumer).

3) MnS producer uses ML capabilities privately and does not expose ML capabilities to MnS consumer. The MnS producer uses ML capabilities privately (e.g., the MnS producer acts as both ML capability producer and ML capability consumer) as illustrated in FIG. 2C, and does not exposes the ML capabilities to MnS consumer.

In one or more embodiments, a ML for MnS system may facilitate specific aspects of ML support for MDA.

In one or more embodiments, MDA may be supported by ML capabilities. The generics aspects of ML capabilities for supporting MnS provided are applicable to MDA. The following provides the specific aspects of how MDA can be supported by ML and the possible relations between ML and MDA.

In one or more embodiments, in cases where MDA is supported by ML capabilities, the generic relation between ML and MnS is applicable to MDA. This provides the relation between ML and MDAS specifically. The MDA may be supported by ML capabilities in the following ways:

1) MDAS producer acts as ML capability consumer. The MDAS producer acts as an ML capability consumer as illustrated in FIG. 3, and does not expose the ML capabilities to an MDAS consumer.

2) MDAS producer acts as ML capability producer and exposes ML capabilities to MDAS consumer. The MDAS producer acts as an ML capability producer as illustrated in FIG. 4, and exposes the ML capabilities to an MDAS consumer (e.g., the MDAS consumer also acts as an ML capability consumer).

3) MDAS producer uses ML capabilities privately and does not expose ML capabilities to MDAS consumer. The MDAS producer uses ML capabilities privately (e.g., the MDAS producer acts as both ML capability producer and ML capability consumer) as illustrated in FIG. 5, and does not expose the ML capabilities to an MDAS consumer.

When ML capabilities are used to support MDA, the MDAS producer may or may not expose the ML capabilities to the MDAS consumer. In case the ML capabilities are exposed to the MDAS consumer, the MDAS producer may train the ML model based on training data for MDA (including the training input and the expected output) provided by the MDAS consumer, and provide the training report for MDA to the consumer. The MDAS producer may re-train the ML model based on the validation feedback, including training report validation feedback and MDA report validation feedback, provided by the MDAS consumer.

In case the ML capabilities are not exposed to the MDAS consumer, the MDAS producer may: 1) train (or re-train) the ML model when the ML capabilities are supported by an MD AS producer, or 2) request to train (or re-train) the ML model when the MD AS producer consumes the ML capabilities from an external entity (e.g., 3rd party), without MDAS consumer’s involvement but with the MDA report validation feedback provided by the consumer taken into account.

In one or more embodiments, a ML for MnS may provide the following requirements.

REQ-MDA ML-FUN-l: The MDAS producer should have a capability allowing the consumer to provide ML model training data for MDA and to train the ML model according to the training data provided by the consumer.

REQ-MDA ML-FUN-2: The MDAS producer should have the capability to train (or re-train) the ML model for MDA with the MDA report validation feedback provided by the consumer taken into account.

REQ-MDA ML-FUN-3 : The MDAS producer should have a capability to provide the ML model training (including re-training) report to the consumer.

REQ-MDA ML-FUN-4: The MDAS producer should have a capability allowing the consumer to provide ML model training report validation feedback for MDA, and to re-train the ML model based on the training report.

FIG. 6 depicts an illustrative schematic diagram for the MDA process, in accordance with one or more example embodiments of the present disclosure.

In one or more embodiments, MDA provides capabilities of processing the analytics inputs (historical and current) related to network and service (e.g., performance measurements, Trace/MDT/RLF/RCEF reports, quality of service (QoS) and experience (QoE) reports, alarms, configuration data, network analytics data, etc.) to generate analytics output, and MDAS producer provides the MDA reports (containing the analytics outputs) to the consumer.

MDA has capabilities for analysis of various issues and provides analytics outputs respectively. MDA may discover new issues, track the status and provide updates on existing issues. It could be possible to provide the analytics outputs for multiple relevant issues in one MDA report.

The common information elements of the MDA reports. The details of MDA capabilities are defined below, including description, MDA type, analytics inputs, and specific analytics output for each MDA capability.

Moreover, the MDAS producer allows the consumer to provide MDA report validation feedback, and may use the feedback to optimize the MDA process (e.g., ML model training in case ML capabilities are used for MDA) in order to provide more accurate analytics outputs. Table 1: Common information elements of MDA reports.

In one or more embodiments, common information elements of MDA reports may be available and common to MDA reports. Some information elements are common for MDA reports, e.g., these common information elements are provided in various MDA reports. The common information elements of the MDA reports are defined in Table 1. In one or more embodiments, the RAN coverage issue may cause UEs out of service or result in a downgrade of network performance offered to the UEs, such as failure of random access, paging, RRC connection establishment or handover, low data throughput, abnormal releases of RRC connection orUE context, and dissatisfied QoE.

There are various types of coverage issues, e.g., weak coverage, a coverage hole, pilot pollution, an overshoot coverage, or a DL and UL channel coverage mismatch, etc, caused by different sorts of reasons, such as insufficient weak transmission power, blocked by constructions, restricted by terrain. The 5G related coverage issue may exist in NR, inE-UTRA or both.

To unravel a coverage issue, it is necessary for an MDAS consumer to know the details about when and where the issue occurred, and the type and cause of the issue. Therefore, it is desirable for MDA to correlate and anal 1 Oe multifold data (such as performance measurements, MDT reports, Radio Link Failure (RLF) reports, RRC connection establishment failure (RCEF) reports, UE location reports, together with the geographical, terrain and configuration data of the RAN) to detect and describe the issue with this detailed information. To help an MDAS consumer to solve the coverage issue as quickly as possible, MDA may also provide the recommended remedy actions (e.g., reconfigure or add some cells, beams, antennas, etc.) along with the description of the issue.

The MDA type for coverage issue analysis is CoverageAnalysis.Coveragelssue.

The analytics inputs for coverage issue analysis are provided in Table 2. Table 2: Analytics inputs for coverage issue analysis.

In one or more embodiments, the specific information elements of the analytics output for coverage issue analysis, in addition to the common information elements of the MDA reports (see clause 7.2), are provided in Table 3.

Table 3: Analytics output for coverage issue analysis.

In one or more embodiments, the validation feedback is associated with validating the training report related to an ML capability. The validation feedback is associated with validating the MD A report related to an MDAS producer. The MDA capability is for coverage problem analysis.

In one or more embodiments, the common information element of MDA reports is for one or more of the following information:

-The identifier that uniquely identifies the MDA report between the MDAS producer and consumer;

-The time when the MDA report was generated;

-Indication of the type of MDA capability for analysis of the corresponding issue;

-The identifier of the issue described in the MDA report;

-Cause of the issue described in the MDA report;

-Severity level of the issue described in the MDA report;

-The time when the issue described in the MDA report started;

-The time when the issue described in the MDA report was lately updated;

-The time when the issue described in the MDA report stopped;

-The MOIs that are affected by the issue described in the MDA report;

-The recommended actions to solve the issue described in the MDA report. The recommended actions could be creating, modifying, and/or deleting of 3GPP MOI(s), and/or invoking one or more non-3GPP (such as ETSI ISG NFV) operations.

In one or more embodiments, the analytics input includes at least one of the following: -Performance measurements of:

— SS-RSRP distribution per SSB (beam) of serving NR cell;

— SS-RSRP distribution per SSB (beam) of neighbor NR cell;

— RSRP distribution of neighbor E-UTRA cell for an NR cell;

—Power headroom distribution for NR cell;

—Wideband CQI distribution for NR cell;

—Timing Advance distribution for NR cell;

—Number of UE Context Release Request (gNB-DU initiated);

—Number of UE Context Release Request per SSB (gNB-DU initiated);

—Number of UE Context Release Requests (gNB-CU initiated);

—Number of UE Context Release Requests per SSB (gNB-CU initiated);

—Average RSRP per TA bin per SSB (beam);

—RSRP related measurements for ng-eNB; — UE power headroom related measurements for ng-eNB;

—Wideband CQI distribution for ng-eNB;

—Average sub-band CQI for ng-eNB;

— UE Rx - Tx time difference related measurements for ng-eNB;

—AO A related measurements for ng-eNB;

—Timing Advance distribution for ng-eNB;

—Number of UE CONTEXT Release Request initiated by ng-eNodeB.

-MDA reports containing RSRPs of the serving cell and neighbour cells, and UE location;

-RLF reports containing RSRPs of the last serving cell and neighbour cells, and UE location;

-UE location information provided by the LCS which can be used to correlate with the MDT reports;

-The geographical information (longitude, latitude, altitude) of the deployed RAN (NG- RAN and E-UTRAN).

-The terrain data for RAN (NG-RAN and E-UTRAN);

-The NRMs containing the attributes affecting the coverage (for NG-RAN and E- UTRAN).

In one or more embodiments, the MDA type specific information elements contain for at least one of the following information:

-Indication that the type of coverage issue;

-Geographical location areas where the coverage issue occurred;

-Indication of the RAT(s) where the coverage issue occurred.

In one or more embodiments, the NRMs containing the attributes affecting the coverage (for NG-RAN and E-UTRAN) contain at least one of the following:

-NRCellDU IOC, NRSectorCarrier IOC, BWP IOC, CommonBeamformingFunction - IOC, and Beam IOC in TS 28.541;

-EUtranGenericCell IOC in TS 28.658;

-SectorEquipmentFunction IOC, AntennaFunction IOC, and TMAFunction IOC in TS

28.662.

In one or more embodiments, the type of coverage issue is one of the following:

-WeakCoverage, -CoverageHole,

-PilotPollution,

-Overshoot coverage,

-DIUlChannelCoverageMismatch.

In one or more embodiments, the geographical location area is represented by 1) the coordinates (longitude and latitude) of the location points that form the lines of the boundary of the area, and 2) the altitude of the area.

In one or more embodiments, the RAT(s) where the coverage issue occurred is NR, E- UTRA, or both.

FIG. 7 illustrates a flow diagram of illustrative process 700 for a ML for MnS system, in accordance with one or more example embodiments of the present disclosure.

At block 702, a device of a management service (MnS) producer may obtain input data related to network and service within a 5G system (5GS) to provide MDA capability to an MnS consumer within the 5GS. The MnS may be a management data analytics service (MDAS). The one or more common information elements may comprise information that is common to a plurality of management data analytics (MDA) reports. The validation feedback may be associated with validating the training report related to an ML capability. The MnS producer may be capable of acting as an machine learning (ML) capabilities producer to provide ML capabilities to an ML capability consumer. The MnS producer may be capable of acting as a machine learning (ML) capability consumer to receive ML capabilities from an ML capability producer.

At block 704, the device may generate one or more MDA reports, wherein the one or more MDA reports comprise one or more common information elements, at least one MDA type associated with the MDA capability, and one or more MDA type specific information elements. An ML capability producer may be configured to support ML for one or more MnSs in the 5GS by being configured to: receive training data from the ML capability consumer; train an ML model; and establish an ML capability in the ML capability producer based on the training data. The ML capability producer may be further configured to send a training report to the ML capability consumer; and identify a validation feedback received from the ML capability consumer. The ML capability producer is further configured to re-train the ML model based on the validation feedback. At block 706, the device may cause to send the one or more MDA reports to the MnS consumer.

FIGs. 8-10 illustrate various systems, devices, and components that may implement aspects of disclosed embodiments.

FIG. 8 illustrates an example network architecture 800 according to various embodiments. The network 800 may operate in a manner consistent with 3 GPP technical specifications for LTE or 5G/NR systems. However, the example embodiments are not limited in this regard and the described embodiments may apply to other networks that benefit from the principles described herein, such as future 3GPP systems, or the like.

The network 800 includes a UE 802, which is any mobile or non-mobile computing device designed to communicate with a RAN 804 via an over-the-air connection. The UE 802 is communicatively coupled with the RAN 804 by a Uu interface, which may be applicable to both LTE and NR systems. Examples of the UE 802 include, but are not limited to, a smartphone, tablet computer, wearable computer, desktop computer, laptop computer, in- vehicle infotainment system, in-car entertainment system, instrument cluster, head-up display (HUD) device, onboard diagnostic device, dashtop mobile equipment, mobile data terminal, electronic engine management system, electronic/engine control unit, electronic/engine control module, embedded system, sensor, microcontroller, control module, engine management system, networked appliance, machine-type communication device, machine-to-machine (M2M), device-to-device (D2D), machine-type communication (MTC) device, Internet of Things (IoT) device, and/or the like. The network 800 may include a plurality of UEs 802 coupled directly with one another via a D2D, ProSe, PC5, and/or sidelink (SL) interface. These UEs 802 may be M2M/D2D/MTC/IoT devices and/or vehicular systems that communicate using physical sidelink channels such as, but not limited to, PSBCH, PSDCH, PSSCH, PSCCH, PSFCH, etc. The UE 802 may perform blind decoding attempts of SL channel s/links according to the various embodiments herein.

In some embodiments, the UE 802 may additionally communicate with an AP 806 via an over-the-air (OTA) connection. The AP 806 manages a WLAN connection, which may serve to offload some/all network traffic from the RAN 804. The connection between the UE 802 and the AP 806 may be consistent with any IEEE 802.11 protocol. Additionally, the UE 802, RAN 804, and AP 806 may utilize cellular- WLAN aggregation/integration (e.g., LWA/LWIP). Cellular- WLAN aggregation may involve the UE 802 being configured by the RAN 804 to utilize both cellular radio resources and WLAN resources.

The RAN 804 includes one or more access network nodes (ANs) 808. The ANs 808 terminate air-interface(s) for the UE 802 by providing access stratum protocols including RRC, PDCP, RLC, MAC, and PHY/Ll protocols. In this manner, the AN 808 enables data/voice connectivity between CN 820 and the UE 802. The ANs 808 may be a macrocell base station or a low power base station for providing femtocells, picocells or other like cells having smaller coverage areas, smaller user capacity, or higher bandwidth compared to macrocells; or some combination thereof. In these implementations, an AN 808 be referred to as a BS, gNB, RAN node, eNB, ng-eNB, NodeB, RSU, TRxP, etc.

One example implementation is a “CU/DU split” architecture where the ANs 808 are embodied as a gNB-Central Unit (CU) that is communicatively coupled with one or more gNB- Distributed Units (DUs), where each DU may be communicatively coupled with one or more Radio Units (RUs) (also referred to as RRHs, RRUs, or the like) (see e.g., 3GPP TS 38.401 vl6.1.0 (2020-03)). In some implementations, the one or more RUs may be individual RSUs. In some implementations, the CU/DU split may include an ng-eNB-CU and one or more ng- eNB-DUs instead of, or in addition to, the gNB-CU and gNB-DUs, respectively. The ANs 808 employed as the CU may be implemented in a discrete device or as one or more software entities running on server computers as part of, for example, a virtual network including a virtual Base Band Unit (BBU) or BBU pool, cloud RAN (CRAN), Radio Equipment Controller (REC), Radio Cloud Center (RCC), centralized RAN (C-RAN), virtualized RAN (vRAN), and/or the like (although these terms may refer to different implementation concepts). Any other type of architectures, arrangements, and/or configurations can be used.

The plurality of ANs may be coupled with one another via an X2 interface (if the RAN 804 is an LTE RAN or Evolved Universal Terrestrial Radio Access Network (E-UTRAN) 810) or an Xn interface (if the RAN 804 is a NG-RAN 814). The X2/Xn interfaces, which may be separated into control/user plane interfaces in some embodiments, may allow the ANs to communicate information related to handovers, data/context transfers, mobility, load management, interference coordination, etc.

The ANs of the RAN 804 may each manage one or more cells, cell groups, component carriers, etc. to provide the UE 802 with an air interface for network access. The UE 802 may be simultaneously connected with a plurality of cells provided by the same or different ANs 808 of the RAN 804. For example, the UE 802 and RAN 804 may use carrier aggregation to allow the UE 802 to connect with a plurality of component carriers, each corresponding to a Pcell or Scell. In dual connectivity scenarios, a first AN 808 may be a master node that provides an MCG and a second AN 808 may be secondary node that provides an SCG. The first/second ANs 808 may be any combination of eNB, gNB, ng-eNB, etc.

The RAN 804 may provide the air interface over a licensed spectrum or an unlicensed spectrum. To operate in the unlicensed spectrum, the nodes may use LAA, eLAA, and/or feLAA mechanisms based on CA technology with PCells/Scells. Prior to accessing the unlicensed spectrum, the nodes may perform medium/carrier-sensing operations based on, for example, a listen-before-talk (LBT) protocol.

In V2X scenarios the UE 802 or AN 808 may be or act as a roadside unit (RSU), which may refer to any transportation infrastructure entity used for V2X communications. An RSU may be implemented in or by a suitable AN or a stationary (or relatively stationary) UE. An RSU implemented in or by: a UE may be referred to as a “UE-type RSU”; an eNB may be referred to as an “eNB-type RSU”; a gNB may be referred to as a “gNB-type RSU”; and the like. In one example, an RSU is a computing device coupled with radio frequency circuitry located on a roadside that provides connectivity support to passing vehicle UEs. The RSU may also include internal data storage circuitry to store intersection map geometry, traffic statistics, media, as well as applications/software to sense and control ongoing vehicular and pedestrian traffic. The RSU may provide very low latency communications required for high speed events, such as crash avoidance, traffic warnings, and the like. Additionally or alternatively, the RSU may provide other cellular/WLAN communications services. The components of the RSU may be packaged in a weatherproof enclosure suitable for outdoor installation, and may include a network interface controller to provide a wired connection (e.g., Ethernet) to a traffic signal controller or a backhaul network.

In some embodiments, the RAN 804 may be an E-UTRAN 810 with one or more eNBs 812. The an E-UTRAN 810 provides an LTE air interface (Uu) with the following characteristics: SCS of 15 kHz; CP-OFDM waveform for DL and SC-FDMA waveform for UL; turbo codes for data and TBCC for control; etc. The LTE air interface may rely on CSI- RS for CSI acquisition and beam management; PDSCH/PDCCH DMRS for PDSCH/PDCCH demodulation; and CRS for cell search and initial acquisition, channel quality measurements, and channel estimation for coherent demodulation/detection at the UE. The LTE air interface may operating on sub-6 GHz bands.

In some embodiments, the RAN 804 may be an next generation (NG)-RAN 814 with one or more gNB 816 and/or on or more ng-eNB 818. The gNB 816 connects with 5G-enabled UEs 802 using a 5G NR interface. The gNB 816 connects with a 5GC 840 through an NG interface, which includes an N2 interface or an N3 interface. The ng-eNB 818 also connects with the 5GC 840 through an NG interface, but may connect with a UE 802 via the Uu interface. The gNB 816 and the ng-eNB 818 may connect with each other over an Xn interface.

In some embodiments, the NG interface may be split into two parts, an NG user plane (NG-U) interface, which carries traffic data between the nodes of the NG-RAN 814 and a UPF 848 (e.g., N3 interface), and an NG control plane (NG-C) interface, which is a signaling interface between the nodes of the NG-RAN 814 and an AMF 844 (e.g., N2 interface).

The NG-RAN 814 may provide a 5G-NR air interface (which may also be referred to as a Uu interface) with the following characteristics: variable SCS; CP-OFDM for DL, CP- OFDM and DFT-s-OFDM for UL; polar, repetition, simplex, and Reed-Muller codes for control and LDPC for data. The 5G-NR air interface may rely on CSI-RS, PDSCH/PDCCH DMRS similar to the LTE air interface. The 5G-NR air interface may not use a CRS, but may use PBCH DMRS for PBCH demodulation; PTRS for phase tracking for PDSCH; and tracking reference signal for time tracking. The 5G-NR air interface may operating on FR1 bands that include sub-6 GHz bands or FR2 bands that include bands from 24.25 GHz to 52.6 GHz. The 5G-NR air interface may include an SSB that is an area of a downlink resource grid that includes PSS/SSS/PBCH.

The 5G-NR air interface may utilize BWPs for various purposes. For example, BWP can be used for dynamic adaptation of the SCS. For example, the UE 802 can be configured with multiple BWPs where each BWP configuration has a different SCS. When a BWP change is indicated to the UE 802, the SCS of the transmission is changed as well. Another use case example of BWP is related to power saving. In particular, multiple BWPs can be configured for the UE 802 with different amount of frequency resources (e.g., PRBs) to support data transmission under different traffic loading scenarios. A BWP containing a smaller number of PRBs can be used for data transmission with small traffic load while allowing power saving at the UE 802 and in some cases at the gNB 816. A BWP containing a larger number of PRBs can be used for scenarios with higher traffic load.

The RAN 804 is communicatively coupled to CN 820 that includes network elements and/or network functions (NFs) to provide various functions to support data and telecommunications services to customers/subscribers (e.g., UE 802). The components of the CN 820 may be implemented in one physical node or separate physical nodes. In some embodiments, NFV may be utilized to virtualize any or all of the functions provided by the network elements of the CN 820 onto physical compute/storage resources in servers, switches, etc. A logical instantiation of the CN 820 may be referred to as a network slice, and a logical instantiation of a portion of the CN 820 may be referred to as a network sub-slice.

The CN 820 may be an LTE CN 822 (also referred to as an Evolved Packet Core (EPC) 822). The EPC 822 may include MME 824, SGW 826, SGSN 828, HSS 830, PGW 832, and PCRF 834 coupled with one another over interfaces (or “reference points”) as shown. The NFs in the EPC 822 are briefly introduced as follows.

The MME 824 implements mobility management functions to track a current location of the UE 802 to facilitate paging, bearer activation/deactivation, handovers, gateway selection, authentication, etc.

The SGW 826 terminates an SI interface toward the RAN 810 and routes data packets between the RAN 810 and the EPC 822. The SGW 826 may be a local mobility anchor point for inter-RAN node handovers and also may provide an anchor for inter-3 GPP mobility. Other responsibilities may include lawful intercept, charging, and some policy enforcement.

The SGSN 828 tracks a location of the UE 802 and performs security functions and access control. The SGSN 828 also performs inter-EPC node signaling for mobility between different RAT networks; PDN and S-GW selection as specified by MME 824; MME 824 selection for handovers; etc. The S3 reference point between the MME 824 and the SGSN 828 enable user and bearer information exchange for inter-3GPP access network mobility in idle/active states.

The HSS 830 includes a database for network users, including subscription-related information to support the network entities’ handling of communication sessions. The HSS 830 can provide support for routing/roaming, authentication, authorization, naming/addressing resolution, location dependencies, etc. An S6a reference point between the HSS 830 and the MME 824 may enable transfer of subscription and authentication data for authenticating/authorizing user access to the EPC 820.

The PGW 832 may terminate an SGi interface toward a data network (DN) 836 that may include an application (app)/content server 838. The PGW 832 routes data packets between the EPC 822 and the data network 836. The PGW 832 is communicatively coupled with the SGW 826 by an S5 reference point to facilitate user plane tunneling and tunnel management. The PGW 832 may further include a node for policy enforcement and charging data collection (e.g., PCEF). Additionally, the SGi reference point may communicatively couple the PGW 832 with the same or different data network 836. The PGW 832 may be communicatively coupled with a PCRF 834 via a Gx reference point. The PCRF 834 is the policy and charging control element of the EPC 822. The PCRF 834 is communicatively coupled to the app/content server 838 to determine appropriate QoS and charging parameters for service flows. The PCRF 832 also provisions associated rules into a PCEF (via Gx reference point) with appropriate TFT and QCI.

The CN 820 may be a 5GC 840 including an AUSF 842, AMF 844, SMF 846, UPF 848, NSSF 850, NEF 852, NRF 854, PCF 856, UDM 858, and AF 860 coupled with one another over various interfaces as shown. The NFs in the 5GC 840 are briefly introduced as follows.

The AUSF 842 stores data for authentication of UE 802 and handle authentication- related functionality. The AUSF 842 may facilitate a common authentication framework for various access types.

The AMF 844 allows other functions of the 5GC 840 to communicate with the UE 802 and the RAN 804 and to subscribe to notifications about mobility events with respect to the UE 802. The AMF 844 is also responsible for registration management (e.g., for registering UE 802), connection management, reachability management, mobility management, lawful interception of AMF-related events, and access authentication and authorization. The AMF 844 provides transport for SM messages between the UE 802 and the SMF 846, and acts as a transparent pro7 for routing SM messages. AMF 844 also provides transport for SMS messages between UE 802 and an SMSF. AMF 844 interacts with the AUSF 842 and the UE 802 to perform various security anchor and context management functions. Furthermore, AMF 844 is a termination point of a RAN-CP interface, which includes the N2 reference point between the RAN 804 and the AMF 844. The AMF 844 is also a termination point of NAS (Nl) signaling, and performs NAS ciphering and integrity protection.

AMF 844 also supports NAS signaling with the UE 802 over an N3IWF interface. The N3IWF provides access to untrusted entities. N3IWF may be a termination point for the N2 interface between the (R)AN 804 and the AMF 844 for the control plane, and may be a termination point for the N3 reference point between the (R)AN 814 and the 848 for the user plane. As such, the AMF 844 handles N2 signalling from the SMF 846 and the AMF 844 for PDU sessions and QoS, encapsulate/de-encapsulate packets for IPSec and N3 tunnelling, marks N3 user-plane packets in the uplink, and enforces QoS corresponding to N3 packet marking taking into account QoS requirements associated with such marking received over N2. N3IWF may also relay UL and DL control-plane NAS signalling between the UE 802 and AMF 844 via an Nl reference point between the UE 802and the AMF 844, and relay uplink and downlink user-plane packets between the UE 802 and UPF 848. The N3IWF also provides mechanisms for IPsec tunnel establishment with the UE 802. The AMF 844 may exhibit an Namf service-based interface, and may be a termination point for an N14 reference point between two AMFs 844 and an N17 reference point between the AMF 844 and a 5G-EIR (not shown by FIG. 8).

The SMF 846 is responsible for SM (e.g., session establishment, tunnel management between UPF 848 and AN 808); UE IP address allocation and management (including optional authorization); selection and control of UP function; configuring traffic steering at UPF 848 to route traffic to proper destination; termination of interfaces toward policy control functions; controlling part of policy enforcement, charging, and QoS; lawful intercept (for SM events and interface to LI system); termination of SM parts of NAS messages; downlink data notification; initiating AN specific SM information, sent via AMF 844 over N2 to AN 808; and determining SSC mode of a session. SM refers to management of a PDU session, and a PDU session or “session” refers to a PDU connectivity service that provides or enables the exchange of PDUs between the UE 802 and the DN 836.

The UPF 848 acts as an anchor point for intra-RAT and inter-RAT mobility, an external PDU session point of interconnect to data network 836, and a branching point to support multihomed PDU session. The UPF 848 also performs packet routing and forwarding, packet inspection, enforces user plane part of policy rules, lawfully intercept packets (UP collection), performs traffic usage reporting, perform QoS handling for a user plane (e.g., packet filtering, gating, UL/DL rate enforcement), performs uplink traffic verification (e.g., SDF-to-QoS flow mapping), transport level packet marking in the uplink and downlink, and performs downlink packet buffering and downlink data notification triggering. UPF 848 may include an uplink classifier to support routing traffic flows to a data network.

The NSSF 850 selects a set of network slice instances serving the UE 802. The NSSF 850 also determines allowed NSSAI and the mapping to the subscribed S-NSSAIs, if needed. The NSSF 850 also determines an AMF set to be used to serve the UE 802, or a list of candidate AMFs 844 based on a suitable configuration and possibly by querying the NRF 854. The selection of a set of network slice instances for the UE 802 may be triggered by the AMF 844 with which the UE 802 is registered by interacting with the NSSF 850; this may lead to a change of AMF 844. The NSSF 850 interacts with the AMF 844 via an N22 reference point; and may communicate with another NSSF in a visited network via an N31 reference point (not shown).

The NEF 852 securely exposes services and capabilities provided by 3GPP NFs for third party, internal exposure/re-exposure, AFs 860, edge computing or fog computing systems (e.g., edge compute node, etc. In such embodiments, the NEF 852 may authenticate, authorize, or throttle the AFs. NEF 852 may also translate information exchanged with the AF 860 and information exchanged with internal network functions. For example, the NEF 852 may translate between an AF-Service-Identifier and an internal 5GC information. NEF 852 may also receive information from other NFs based on exposed capabilities of other NFs. This information may be stored at the NEF 852 as structured data, or at a data storage NF using standardized interfaces. The stored information can then be re-exposed by the NEF 852 to other NFs and AFs, or used for other purposes such as analytics.

The NRF 854 supports service discovery functions, receives NF discovery requests from NF instances, and provides information of the discovered NF instances to the requesting NF instances. NRF 854 also maintains information of available NF instances and their supported services. The NRF 854 also supports service discovery functions, wherein the NRF 854 receives NF Discovery Request from NF instance or an SCP (not shown), and provides information of the discovered NF instances to the NF instance or SCP.

The PCF 856 provides policy rules to control plane functions to enforce them, and may also support unified policy framework to govern network behavior. The PCF 856 may also implement a front end to access subscription information relevant for policy decisions in a UDR of the UDM 858. In addition to communicating with functions over reference points as shown, the PCF 856 exhibit an Npcf service-based interface.

The UDM 858 handles subscription-related information to support the network entities’ handling of communication sessions, and stores subscription data of UE 802. For example, subscription data may be communicated via an N8 reference point between the UDM 858 and the AMF 844. The UDM 858 may include two parts, an application front end and a UDR. The UDR may store subscription data and policy data for the UDM 858 and the PCF 856, and/or structured data for exposure and application data (including PFDs for application detection, application request information for multiple UEs 802) for the NEF 852. The Nudr service- based interface may be exhibited by the UDR 221 to allow the UDM 858, PCF 856, and NEF 852 to access a particular set of the stored data, as well as to read, update (e.g., add, modify), delete, and subscribe to notification of relevant data changes in the UDR. The UDM may include a UDM-FE, which is in charge of processing credentials, location management, subscription management and so on. Several different front ends may serve the same user in different transactions. The UDM-FE accesses subscription information stored in the UDR and performs authentication credential processing, user identification handling, access authorization, registration/mobility management, and subscription management. In addition to communicating with other NFs over reference points as shown, the UDM 858 may exhibit the Nudm service-based interface.

AF 860 provides application influence on traffic routing, provide access to NEF 852, and interact with the policy framework for policy control. The AF 860 may influence UPF 848 (re)selection and traffic routing. Based on operator deployment, when AF 860 is considered to be a trusted entity, the network operator may permit AF 860 to interact directly with relevant NFs. Additionally, the AF 860 may be used for edge computing implementations,

The 5GC 840 may enable edge computing by selecting operator/3rd party services to be geographically close to a point that the UE 802 is attached to the network. This may reduce latency and load on the network. In edge computing implementations, the 5GC 840 may select a UPF 848 close to the UE 802 and execute traffic steering from the UPF 848 to DN 836 via the N6 interface. This may be based on the UE subscription data, UE location, and information provided by the AF 860, which allows the AF 860 to influence UPF (re)selection and traffic routing.

The data network (DN) 836 may represent various network operator services, Internet access, or third party services that may be provided by one or more servers including, for example, application (app)/content server 838. The DN 836 may be an operator external public, a private PDN, or an intra-operator packet data network, for example, for provision of IMS services. In this embodiment, the app server 838 can be coupled to an IMS via an S-CSCF or the I-CSCF. In some implementations, the DN 836 may represent one or more local area DNs (LADNs), which are DNs 836 (or DN names (DNNs)) that is/are accessible by a UE 802 in one or more specific areas. Outside of these specific areas, the UE 802 is not able to access the LADN/DN 836.

Additionally or alternatively, the DN 836 may be an Edge DN 836, which is a (local) Data Network that supports the architecture for enabling edge applications. In these embodiments, the app server 838 may represent the physical hardware systems/devices providing app server functionality and/or the application software resident in the cloud or at an edge compute node that performs server function(s). In some embodiments, the app/content server 838 provides an edge hosting environment that provides support required for Edge Application Server's execution.

In some embodiments, the 5GS can use one or more edge compute nodes to provide an interface and offload processing of wireless communication traffic. In these embodiments, the edge compute nodes may be included in, or co-located with one or more RAN810, 814. For example, the edge compute nodes can provide a connection between the RAN 814 and UPF 848 in the 5GC 840. The edge compute nodes can use one or more NFV instances instantiated on virtualization infrastructure within the edge compute nodes to process wireless connections to and from the RAN 814 and UPF 848.

The interfaces of the 5GC 840 include reference points and service-based itnterfaces. The reference points include: N1 (between the UE 802 and the AMF 844), N2 (between RAN 814 and AMF 844), N3 (between RAN 814 and UPF 848), N4 (between the SMF 846 and UPF 848), N5 (between PCF 856 and AF 860), N6 (between UPF 848 and DN 836), N7 (between SMF 846 and PCF 856), N8 (between UDM 858 and AMF 844), N9 (between two UPFs 848), N10 (between the UDM 858 and the SMF 846), Ni l (between the AMF 844 and the SMF 846), N12 (between AUSF 842 and AMF 844), N13 (between AUSF 842 and UDM 858), N14 (between two AMFs 844; not shown), N15 (between PCF 856 and AMF 844 in case of a nonroaming scenario, or between the PCF 856 in a visited network and AMF 844 in case of a roaming scenario), N16 (between two SMFs 846; not shown), and N22 (between AMF 844 and NSSF 850). Other reference point representations not shown in FIG. 8 can also be used. The service-based representation of FIG. 8 represents NFs within the control plane that enable other authorized NFs to access their services. The service-based interfaces (SBIs) include: Namf (SBI exhibited by AMF 844), Nsmf (SBI exhibited by SMF 846), Nnef (SBI exhibited by NEF 852), Npcf (SBI exhibited by PCF 856), Nudm (SBI exhibited by the UDM 858), Naf (SBI exhibited by AF 860), Nnrf (SBI exhibited by NRF 854), Nnssf (SBI exhibited by NSSF 850), Nausf (SBI exhibited by AUSF 842). Other service-based interfaces (e.g., Nudr, N5g- eir, and Nudsf) not shown in FIG. 8 can also be used. In some embodiments, the NEF 852 can provide an interface to edge compute nodes 836x, which can be used to process wireless connections with the RAN 814.

In some implementations, the system 800 may include an SMSF, which is responsible for SMS subscription checking and verification, and relaying SM messages to/from the UE 802 to/from other entities, such as an SMS-GMSC/IWMSC/SMS-router. The SMS may also interact with AMF 842 and UDM 858 for a notification procedure that the UE 802 is available for SMS transfer (e.g., set a UE not reachable flag, and notifying UDM 858 when UE 802 is available for SMS).

The 5GS may also include an SCP (or individual instances of the SCP) that supports indirect communication (see e.g., 3GPP TS 23.501 section 7.1.1); delegated discovery (see e.g., 3GPP TS 23.501 section 7.1.1); message forwarding and routing to destination NF/NF service(s), communication security (e.g., authorization of the NF Service Consumer to access the NF Service Producer API) (see e.g., 3GPP TS 33.501), load balancing, monitoring, overload control, etc.; and discovery and selection functionality for UDM(s), AUSF(s), UDR(s), PCF(s) with access to subscription data stored in the UDR based on UE's SUPI, SUCI or GPSI (see e.g., 3GPP TS 23.501 section 6.3). Load balancing, monitoring, overload control functionality provided by the SCP may be implementation specific. The SCP may be deployed in a distributed manner. More than one SCP can be present in the communication path between various NF Services. The SCP, although not an NF instance, can also be deployed distributed, redundant, and scalable.

FIG. 9 schematically illustrates a wireless network 900 in accordance with various embodiments. The wireless network 900 may include a UE 902 in wireless communication with an AN 904. The UE 902 and AN 904 may be similar to, and substantially interchangeable with, like-named components described with respect to FIG. 8.

The UE 902 may be communicatively coupled with the AN 904 via connection 906. The connection 906 is illustrated as an air interface to enable communicative coupling, and can be consistent with cellular communications protocols such as an LTE protocol or a 5G NR protocol operating at mmWave or sub-6GHz frequencies.

The UE 902 may include a host platform 908 coupled with a modem platform 910. The host platform 908 may include application processing circuitry 912, which may be coupled with protocol processing circuitry 914 of the modem platform 910. The application processing circuitry 912 may run various applications for the UE 902 that source/sink application data. The application processing circuitry 912 may further implement one or more layer operations to transmit/receive application data to/from a data network. These layer operations may include transport (for example UDP) and Internet (for example, IP) operations.

The protocol processing circuitry 914 may implement one or more of layer operations to facilitate transmission or reception of data over the connection 906. The layer operations implemented by the protocol processing circuitry 914 may include, for example, MAC, RLC, PDCP, RRC and NAS operations.

The modem platform 910 may further include digital baseband circuitry 916 that may implement one or more layer operations that are “below” layer operations performed by the protocol processing circuitry 914 in a network protocol stack. These operations may include, for example, PHY operations including one or more of HARQ acknowledgement (ACK) functions, scrambling/descrambling, encoding/decoding, layer mapping/de-mapping, modulation symbol mapping, received symbol/bit metric determination, multi-antenna port precoding/decoding, which may include one or more of space-time, space-frequency or spatial coding, reference signal generation/detection, preamble sequence generation and/or decoding, synchronization sequence generation/detection, control channel signal blind decoding, and other related functions.

The modem platform 910 may further include transmit circuitry 918, receive circuitry 920, RF circuitry 922, and RF front end (RFFE) 924, which may include or connect to one or more antenna panels 926. Briefly, the transmit circuitry 918 may include a digital-to-analog converter, mixer, intermediate frequency (IF) components, etc.; the receive circuitry 920 may include an analog-to-digital converter, mixer, IF components, etc.; the RF circuitry 922 may include a low-noise amplifier, a power amplifier, power tracking components, etc.; RFFE 924 may include filters (for example, surface/bulk acoustic wave filters), switches, antenna tuners, beamforming components (for example, phase-array antenna components), etc. The selection and arrangement of the components of the transmit circuitry 918, receive circuitry 920, RF circuitry 922, RFFE 924, and antenna panels 926 (referred generically as “transmit/receive components”) may be specific to details of a specific implementation such as, for example, whether communication is TDM or FDM, in mmWave or sub-6 gHz frequencies, etc. In some embodiments, the transmit/receive components may be arranged in multiple parallel transmit/receive chains, may be disposed in the same or different chips/modules, etc.

In some embodiments, the protocol processing circuitry 914 may include one or more instances of control circuitry (not shown) to provide control functions for the transmit/receive components.

A UE 902 reception may be established by and via the antenna panels 926, RFFE 924, RF circuitry 922, receive circuitry 920, digital baseband circuitry 916, and protocol processing circuitry 914. In some embodiments, the antenna panels 926 may receive a transmission from the AN 904 by receive-beamforming signals received by a plurality of antennas/antenna elements of the one or more antenna panels 926.

A UE 902 transmission may be established by and via the protocol processing circuitry 914, digital baseband circuitry 916, transmit circuitry 918, RF circuitry 922, RFFE 924, and antenna panels 926. In some embodiments, the transmit components of the UE 904 may apply a spatial filter to the data to be transmitted to form a transmit beam emitted by the antenna elements of the antenna panels 926.

Similar to the UE 902, the AN 904 may include a host platform 928 coupled with a modem platform 930. The host platform 928 may include application processing circuitry 932 coupled with protocol processing circuitry 934 of the modem platform 930. The modem platform may further include digital baseband circuitry 936, transmit circuitry 938, receive circuitry 940, RF circuitry 942, RFFE circuitry 944, and antenna panels 946. The components of the AN 904 may be similar to and substantially interchangeable with like-named components of the UE 902. In addition to performing data transmission/reception as described above, the components of the AN 908 may perform various logical functions that include, for example, RNC functions such as radio bearer management, uplink and downlink dynamic radio resource management, and data packet scheduling.

FIG. 10 illustrates components of a computing device 1000 according to some example embodiments, able to read instructions from a machine-readable or computer-readable medium (e.g., a non-transitory machine-readable storage medium) and perform any one or more of the methodologies discussed herein. Specifically, FIG. 10 shows a diagrammatic representation of hardware resources 1000 including one or more processors (or processor cores) 1010, one or more memory/storage devices 1020, and one or more communication resources 1030, each of which may be communicatively coupled via a bus 1040 or other interface circuitry. For embodiments where node virtualization (e.g., NFV) is utilized, a hypervisor 1002 may be executed to provide an execution environment for one or more network slices/sub-slices to utilize the hardware resources 1000.

The processors 1010 include, for example, processor 1012 and processor 1014. The processors 1010 include circuitry such as, but not limited to one or more processor cores and one or more of cache memory, low drop-out voltage regulators (LDOs), interrupt controllers, serial interfaces such as SPI, I2C or universal programmable serial interface circuit, real time clock (RTC), timer-counters including interval and watchdog timers, general purpose I/O, memory card controllers such as secure digital/multi-media card (SD/MMC) or similar, interfaces, mobile industry processor interface (MIPI) interfaces and Joint Test Access Group (JTAG) test access ports. The processors 1010 may be, for example, a central processing unit (CPU), reduced instruction set computing (RISC) processors, Acorn RISC Machine (ARM) processors, complex instruction set computing (CISC) processors, graphics processing units (GPUs), one or more Digital Signal Processors (DSPs) such as a baseband processor, Application-Specific Integrated Circuits (ASICs), an Field-Programmable Gate Array (FPGA), a radio-frequency integrated circuit (RFIC), one or more microprocessors or controllers, another processor (including those discussed herein), or any suitable combination thereof. In some implementations, the processor circuitry 1010 may include one or more hardware accelerators, which may be microprocessors, programmable processing devices (e.g., FPGA, complex programmable logic devices (CPLDs), etc.), or the like.

The memory/ storage devices 1020 may include main memory, disk storage, or any suitable combination thereof. The memory/storage devices 1020 may include, but are not limited to, any type of volatile, non-volatile, or semi-volatile memory such as random access memory (RAM), dynamic RAM (DRAM), static RAM (SRAM), synchronous DRAM (SDRAM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), Flash memory, solid-state storage, phase change RAM (PRAM), resistive memory such as magnetoresistive random access memory (MRAM), etc., and may incorporate three-dimensional (3D) cross-point (XPOINT) memories from Intel® and Micron®. The memory/storage devices 1020 may also comprise persistent storage devices, which may be temporal and/or persistent storage of any type, including, but not limited to, non-volatile memory, optical, magnetic, and/or solid state mass storage, and so forth.

The communication resources 1030 may include interconnection or network interface controllers, components, or other suitable devices to communicate with one or more peripheral devices 1004 or one or more databases 1006 or other network elements via a network 1008. For example, the communication resources 1030 may include wired communication components (e.g., for coupling via USB, Ethernet, Ethernet, Ethernet over GRE Tunnels, Ethernet over Multiprotocol Label Switching (MPLS), Ethernet over USB, Controller Area Network (CAN), Local Interconnect Network (LIN), DeviceNet, ControlNet, Data Highway+, PROFIBUS, or PROFINET, among many others), cellular communication components, NFC components, Bluetooth® (or Bluetooth® Low Energy) components, WiFi® components, and other communication components. Network connectivity may be provided to/from the computing device 1000 via the communication resources 1030 using a physical connection, which may be electrical (e.g., a “copper interconnect”) or optical. The physical connection also includes suitable input connectors (e.g., ports, receptacles, sockets, etc.) and output connectors (e.g., plugs, pins, etc.). The communication resources 1030 may include one or more dedicated processors and/or FPGAs to communicate using one or more of the aforementioned network interface protocols.

Instructions 1050 may comprise software, a program, an application, an applet, an app, or other executable code for causing at least any of the processors 1010 to perform any one or more of the methodologies discussed herein. The instructions 1050 may reside, completely or partially, within at least one of the processors 1010 (e.g., within the processor’s cache memory), the memory/storage devices 1020, or any suitable combination thereof. Furthermore, any portion of the instructions 1050 may be transferred to the hardware resources 1000 from any combination of the peripheral devices 1004 or the databases 1006. Accordingly, the memory of processors 1010, the memory/storage devices 1020, the peripheral devices 1004, and the databases 1006 are examples of computer-readable and machine- readable media.

For one or more embodiments, at least one of the components set forth in one or more of the preceding figures may be configured to perform one or more operations, techniques, processes, and/or methods as set forth in the example section below. For example, the baseband circuitry as described above in connection with one or more of the preceding figures may be configured to operate in accordance with one or more of the examples set forth below. For another example, circuitry associated with a UE, base station, network element, etc., as described above in connection with one or more of the preceding figures may be configured to operate in accordance with one or more of the examples set forth below in the example section.

Additional examples of the presently described embodiments include the following, non-limiting implementations. Each of the following non-limiting examples may stand on its own or may be combined in any permutation or combination with any one or more of the other examples provided below or throughout the present disclosure.

For one or more embodiments, at least one of the components set forth in one or more of the preceding figures may be configured to perform one or more operations, techniques, processes, and/or methods as set forth in the example section below. For example, the baseband circuitry as described above in connection with one or more of the preceding figures may be configured to operate in accordance with one or more of the examples set forth below. For another example, circuitry associated with a UE, base station, network element, etc. as described above in connection with one or more of the preceding figures may be configured to operate in accordance with one or more of the examples set forth below.

The following examples pertain to further embodiments.

Example 1 may include an apparatus of a management service (MnS) producer comprising obtain input data related to network and service within a 5G system (5GS) to provide MDA capability to an MnS consumer within the 5GS; generate one or more MDA reports, wherein the one or more MDA reports comprise one or more common information elements, at least one MDA type associated with the MDA capability, and one or more MDA type specific information elements; and cause to send the one or more MDA reports to the MnS consumer.

Example 2 may include the apparatus of example 1 and/or some other example herein, wherein the MnS producer may be capable of acting as an machine learning (ML) capabilities producer to provide ML capabilities to an ML capability consumer. Example 3 may include the apparatus of example 1 and/or some other example herein, wherein the MnS producer may be capable of acting as an machine learning (ML) capability consumer to receive ML capabilities from an ML capability producer.

Example 4 may include the apparatus of example 3 and/or some other example herein, wherein an ML capability producer may be configured to support ML for one or more MnSs in the 5GS by being configured to: receive training data from the ML capability consumer; train an ML model; and establish an ML capability in the ML capability producer based on the training data.

Example 5 may include the apparatus of example 4 and/or some other example herein, wherein the ML capability producer may be further configured to: send a training report to the ML capability consumer; and identify a validation feedback received from the ML capability consumer.

Example 6 may include the apparatus of example 5 and/or some other example herein, wherein the ML capability producer may be further configured to re-train the ML model based on the validation feedback.

Example 7 may include the apparatus of example 1 and/or some other example herein, wherein the MnS may be management data analytics service (MDAS).

Example 8 may include the apparatus of example 1 and/or some other example herein, wherein the one or more common information elements comprise information that may be common to a plurality of management data analytics (MDA) reports.

Example 9 may include the apparatus of example 5 and/or some other example herein, wherein the validation feedback may be associated with validating the training report related to an ML capability.

Example 10 may include the apparatus of example 8 and/or some other example herein, wherein the one or more common information elements are for at least one or more of an identifier that uniquely identifies the MDA report between an MDAS producer and MDAS consumer, a time when the MDA report was generated, indication type of MDA capability for analysis of a corresponding issue, an identifier of an issue described in an MDA report, Cause of the issue described in the MDA report, severity level of the issue described in the MDA report, a time when the issue described in the MDA report started, a time when the issue described in the MDA report was lately updated, a time when the issue described in the MDA report stopped, managed object instances (MOIs) that are affected by the issue described in the MDA report, or recommended actions to solve the issue described in the MDA report. Example 11 may include a computer-readable storage medium comprising instructions to cause processing circuitry, upon execution of the instructions by the processing circuitry, to: obtain, by a management service (MnS) producer, input data related to network and service within a 5G system (5GS) to provide MDA capability to an MnS consumer within the 5GS; generate one or more MDA reports, wherein the one or more MDA reports comprise one or more common information elements, at least one MDA type associated with the MDA capability, and one or more MDA type specific information elements; and cause to send the one or more MDA reports to the MnS consumer.

Example 12 may include the computer-readable storage medium of example 11 and/or some other example herein, wherein the MnS producer may be capable of acting as an machine learning (ML) capabilities producer to provide ML capabilities to an ML capability consumer.

Example 13 may include the computer-readable storage medium of example 11 and/or some other example herein, wherein the MnS producer may be capable of acting as an machine learning (ML) capability consumer to receive ML capabilities from an ML capability producer.

Example 14 may include the computer-readable storage medium of example 13 and/or some other example herein, wherein an ML capability producer may be configured to support ML for one or more MnSs in the 5GS by being configured to: receive training data from the ML capability consumer; train an ML model; and establish an ML capability in the ML capability producer based on the training data.

Example 15 may include the computer-readable storage medium of example 14 and/or some other example herein, wherein the ML capability producer may be further configured to: send a training report to the ML capability consumer; and identify a validation feedback received from the ML capability consumer.

Example 16 may include the computer-readable storage medium of example 15 and/or some other example herein, wherein the ML capability producer may be further configured to re-train the ML model based on the validation feedback.

Example 17 may include the computer-readable storage medium of example 11 and/or some other example herein, wherein the MnS may be management data analytics service (MDAS).

Example 18 may include the computer-readable storage medium of example 11 and/or some other example herein, wherein the one or more common information elements comprise information that may be common to a plurality of management data analytics (MDA) reports. Example 19 may include the computer-readable storage medium of example 15 and/or some other example herein, wherein the validation feedback may be associated with validating the training report related to an ML capability.

Example 20 may include the computer-readable storage medium of example 18 and/or some other example herein, wherein the one or more common information elements are for at least one or more of an identifier that uniquely identifies the MDA report between an MDAS producer and MDAS consumer, a time when the MDA report was generated, indication type of MDA capability for analysis of a corresponding issue, an identifier of an issue described in an MDA report, Cause of the issue described in the MDA report, severity level of the issue described in the MDA report, a time when the issue described in the MDA report started, a time when the issue described in the MDA report was lately updated, a time when the issue described in the MDA report stopped, managed object instances (MOIs) that are affected by the issue described in the MDA report, or recommended actions to solve the issue described in the MDA report.

Example 21 may include the computer-readable storage medium of example 20 and/or some other example herein, wherein the recommended actions could be creating, modifying, and/or deleting of 3GPP MOI(s), and/or invoking one or more non-3GPP operations.

Example 22 may include a method comprising: obtaining, by one or more processors of a management service (MnS) producer, input data related to network and service within a 5G system (5GS) to provide MDA capability to an MnS consumer within the 5GS; generating one or more MDA reports, wherein the one or more MDA reports comprise one or more common information elements, at least one MDA type associated with the MDA capability, and one or more MDA type specific information elements; and causing to send the one or more MDA reports to the MnS consumer.

Example 23 may include the method of example 22 and/or some other example herein, wherein the MnS producer may be capable of acting as an machine learning (ML) capabilities producer to provide ML capabilities to an ML capability consumer.

Example 24 may include the method of example 22 and/or some other example herein, wherein the MnS producer may be capable of acting as an machine learning (ML) capability consumer to receive ML capabilities from an ML capability producer.

Example 25 may include the method of example 24 and/or some other example herein, wherein an ML capability producer may be configured to support ML for one or more MnSs in the 5GS by being configured to: receive training data from the ML capability consumer; train an ML model; and establish an ML capability in the ML capability producer based on the training data.

Example 26 may include the method of example 25 and/or some other example herein, wherein the ML capability producer may be further configured to: send a training report to the ML capability consumer; and identify a validation feedback received from the ML capability consumer.

Example 27 may include the method of example 26 and/or some other example herein, wherein the ML capability producer may be further configured to re-train the ML model based on the validation feedback.

Example 28 may include the method of example 22 and/or some other example herein, wherein the MnS may be management data analytics service (MDAS).

Example 29 may include the method of example 22 and/or some other example herein, wherein the one or more common information elements comprise information that may be common to a plurality of management data analytics (MDA) reports.

Example 30 may include the method of example 26 and/or some other example herein, wherein the validation feedback may be associated with validating the training report related to an ML capability.

Example 31 may include the method of example 29 and/or some other example herein, wherein the one or more common information elements are for at least one or more of an identifier that uniquely identifies the MDA report between an MDAS producer and MDAS consumer, a time when the MDA report was generated, indication type of MDA capability for analysis of a corresponding issue, an identifier of an issue described in an MDA report, Cause of the issue described in the MDA report, severity level of the issue described in the MDA report, a time when the issue described in the MDA report started, a time when the issue described in the MDA report was lately updated, a time when the issue described in the MDA report stopped, managed object instances (MOIs) that are affected by the issue described in the MDA report, or recommended actions to solve the issue described in the MDA report.

Example 32 may include the method of example 31 and/or some other example herein, wherein the recommended actions could be creating, modifying, and/or deleting of 3GPP MOI(s), and/or invoking one or more non-3GPP operations.

Example 33 may include an apparatus comprising means for performing any of the methods of examples 1-32. Example 34 may include a network node comprising a communication interface and processing circuitry connected thereto and configured to perform the methods of examples 1- 32.

Example 35 may include an apparatus comprising means to perform one or more elements of a method described in or related to any of examples 1-32, or any other method or process described herein.

Example 36 may include one or more non-transitory computer-readable media comprising instructions to cause an electronic device, upon execution of the instructions by one or more processors of the electronic device, to perform one or more elements of a method described in or related to any of examples 1-32, or any other method or process described herein.

Example 37 may include an apparatus comprising logic, modules, or circuitry to perform one or more elements of a method described in or related to any of examples 1-32, or any other method or process described herein.

Example 38 may include a method, technique, or process as described in or related to any of examples 1-32, or portions or parts thereof.

Example 39 may include an apparatus comprising: one or more processors and one or more computer-readable media comprising instructions that, when executed by the one or more processors, cause the one or more processors to perform the method, techniques, or process as described in or related to any of examples 1-32, or portions thereof.

Example 40 may include a signal as described in or related to any of examples 1-32, or portions or parts thereof.

Example 41 may include a datagram, packet, frame, segment, protocol data unit (PDU), or message as described in or related to any of examples 1-32, or portions or parts thereof, or otherwise described in the present disclosure.

Example 42 may include a signal encoded with data as described in or related to any of examples 1-32, or portions or parts thereof, or otherwise described in the present disclosure.

Example 43 may include a signal encoded with a datagram, packet, frame, segment, protocol data unit (PDU), or message as described in or related to any of examples 1-32, or portions or parts thereof, or otherwise described in the present disclosure.

Example 44 may include an electromagnetic signal carrying computer-readable instructions, wherein execution of the computer-readable instructions by one or more processors is to cause the one or more processors to perform the method, techniques, or process as described in or related to any of examples 1-32, or portions thereof. Example 48 may include a computer program comprising instructions, wherein execution of the program by a processing element is to cause the processing element to carry out the method, techniques, or process as described in or related to any of examples 1-32, or portions thereof. Example 45 may include a signal in a wireless network as shown and described herein.

Example 46 may include a method of communicating in a wireless network as shown and described herein.

Example 47 may include a system for providing wireless communication as shown and described herein. Example 48 may include a device for providing wireless communication as shown and described herein.

An example implementation is an edge computing system, including respective edge processing devices and nodes to invoke or perform the operations of the examples above, or other subject matter described herein. Another example implementation is a client endpoint node, operable to invoke or perform the operations of the examples above, or other subject matter described herein. Another example implementation is an aggregation node, network hub node, gateway node, or core data processing node, within or coupled to an edge computing system, operable to invoke or perform the operations of the examples above, or other subject matter described herein. Another example implementation is an access point, base station, road-side unit, street-side unit, or on-premise unit, within or coupled to an edge computing system, operable to invoke or perform the operations of the examples above, or other subject matter described herein. Another example implementation is an edge provisioning node, service orchestration node, application orchestration node, or multi-tenant management node, within or coupled to an edge computing system, operable to invoke or perform the operations of the examples above, or other subject matter described herein. Another example implementation is an edge node operating an edge provisioning service, application or service orchestration service, virtual machine deployment, container deployment, function deployment, and compute management, within or coupled to an edge computing system, operable to invoke or perform the operations of the examples above, or other subject matter described herein. Another example implementation is an edge computing system operable as an edge mesh, as an edge mesh with side car loading, or with mesh-to-mesh communications, operable to invoke or perform the operations of the examples above, or other subject matter described herein. Another example implementation is an edge computing system including aspects of network functions, acceleration functions, acceleration hardware, storage hardware, or computation hardware resources, operable to invoke or perform the use cases discussed herein, with use of the examples above, or other subject matter described herein. Another example implementation is an edge computing system adapted for supporting client mobility, vehicle-to-vehicle (V2V), vehicle-to-everything (V2X), or vehicle-to-infrastructure (V2I) scenarios, and optionally operating according to ETSI MEC specifications, operable to invoke or perform the use cases discussed herein, with use of the examples above, or other subject matter described herein. Another example implementation is an edge computing system adapted for mobile wireless communications, including configurations according to an 3 GPP 4G/LTE or 5G network capabilities, operable to invoke or perform the use cases discussed herein, with use of the examples above, or other subject matter described herein. Another example implementation is a computing system adapted for network communications, including configurations according to an O-RAN capabilities, operable to invoke or perform the use cases discussed herein, with use of the examples above, or other subject matter described herein. Any of the above-described examples may be combined with any other example (or combination of examples), unless explicitly stated otherwise. The foregoing description of one or more implementations provides illustration and description, but is not intended to be exhaustive or to limit the scope of embodiments to the precise form disclosed. Modifications and variations are possible in light of the above teachings or may be acquired from practice of various embodiments.

Einless used differently herein, terms, definitions, and abbreviations may be consistent with terms, definitions, and abbreviations defined in 3GPP TR 21.905 vl6.0.0 (2019-06). For the purposes of the present document, the following abbreviations may apply to the examples and embodiments discussed herein.

Table 4 - Abbreviations:

The foregoing description provides illustration and description of various example embodiments, but is not intended to be exhaustive or to limit the scope of embodiments to the precise forms disclosed. Modifications and variations are possible in light of the above teachings or may be acquired from practice of various embodiments. Where specific details are set forth in order to describe example embodiments of the disclosure, it should be apparent to one skilled in the art that the disclosure can be practiced without, or with variation of, these specific details. It should be understood, however, that there is no intent to limit the concepts of the present disclosure to the particular forms disclosed, but on the contrary, the intention is to cover all modifications, equivalents, and alternatives consistent with the present disclosure and the appended claims.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used herein, the singular forms “a,” “an” and “the” are intended to include plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises” and/or “comprising,” when used in this specification, specific the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operation, elements, components, and/or groups thereof.

For the purposes of the present disclosure, the phrase “A and/or B” means (A), (B), or (A and B). For the purposes of the present disclosure, the phrase “A, B, and/or C” means (A), (B), (C), (A and B), (A and C), (B and C), or (A, B and C). The description may use the phrases “in an embodiment,” or “In some embodiments,” which may each refer to one or more of the same or different embodiments. Furthermore, the terms “comprising,” “including,” “having,” and the like, as used with respect to embodiments of the present disclosure, are synonymous.

The terms “coupled,” “communicatively coupled,” along with derivatives thereof are used herein. The term “coupled” may mean two or more elements are in direct physical or electrical contact with one another, may mean that two or more elements indirectly contact each other but still cooperate or interact with each other, and/or may mean that one or more other elements are coupled or connected between the elements that are said to be coupled with each other. The term “directly coupled” may mean that two or more elements are in direct contact with one another. The term “communicatively coupled” may mean that two or more elements may be in contact with one another by a means of communication including through a wire or other interconnect connection, through a wireless communication channel or ink, and/or the like. The term “circuitry” as used herein refers to, is part of, or includes hardware components such as an electronic circuit, a logic circuit, a processor (shared, dedicated, or group) and/or memory (shared, dedicated, or group), an Application Specific Integrated Circuit (ASIC), a field-programmable device (FPD) (e.g., a field-programmable gate array (FPGA), a programmable logic device (PLD), a complex PLD (CPLD), a high-capacity PLD (HCPLD), a structured ASIC, or a programmable SoC), digital signal processors (DSPs), etc., that are configured to provide the described functionality. In some embodiments, the circuitry may execute one or more software or firmware programs to provide at least some of the described functionality. The term “circuitry” may also refer to a combination of one or more hardware elements (or a combination of circuits used in an electrical or electronic system) with the program code used to carry out the functionality of that program code. In these embodiments, the combination of hardware elements and program code may be referred to as a particular type of circuitry.

The term “processor circuitry” as used herein refers to, is part of, or includes circuitry capable of sequentially and automatically carrying out a sequence of arithmetic or logical operations, or recording, storing, and/or transferring digital data. Processing circuitry may include one or more processing cores to execute instructions and one or more memory structures to store program and data information. The term “processor circuitry” may refer to one or more application processors, one or more baseband processors, a physical central processing unit (CPU), a single-core processor, a dual-core processor, a triple-core processor, a quad-core processor, and/or any other device capable of executing or otherwise operating computer-executable instructions, such as program code, software modules, and/or functional processes. Processing circuitry may include more hardware accelerators, which may be microprocessors, programmable processing devices, or the like. The one or more hardware accelerators may include, for example, computer vision (CV) and/or deep learning (DL) accelerators. The terms “application circuitry” and/or “baseband circuitry” may be considered synonymous to, and may be referred to as, “processor circuitry.”

The term “memory” and/or “memory circuitry” as used herein refers to one or more hardware devices for storing data, including RAM, MRAM, PRAM, DRAM, and/or SDRAM, core memory, ROM, magnetic disk storage mediums, optical storage mediums, flash memory devices or other machine readable mediums for storing data. The term “computer-readable medium” may include, but is not limited to, memory, portable or fixed storage devices, optical storage devices, and various other mediums capable of storing, containing or carrying instructions or data. The term “interface circuitry” as used herein refers to, is part of, or includes circuitry that enables the exchange of information between two or more components or devices. The term “interface circuitry” may refer to one or more hardware interfaces, for example, buses, I/O interfaces, peripheral component interfaces, network interface cards, and/or the like.

The term “user equipment” or “UE” as used herein refers to a device with radio communication capabilities and may describe a remote user of network resources in a communications network. The term “user equipment” or “UE” may be considered synonymous to, and may be referred to as, client, mobile, mobile device, mobile terminal, user terminal, mobile unit, mobile station, mobile user, subscriber, user, remote station, access agent, user agent, receiver, radio equipment, reconfigurable radio equipment, reconfigurable mobile device, etc. Furthermore, the term “user equipment” or “UE” may include any type of wireless/wired device or any computing device including a wireless communications interface.

The term “network element” as used herein refers to physical or virtualized equipment and/or infrastructure used to provide wired or wireless communication network services. The term “network element” may be considered synonymous to and/or referred to as a networked computer, networking hardware, network equipment, network node, router, switch, hub, bridge, radio network controller, RAN device, RAN node, gateway, server, virtualized VNF, NFVI, and/or the like.

The term “computer system” as used herein refers to any type interconnected electronic devices, computer devices, or components thereof. Additionally, the term “computer system” and/or “system” may refer to various components of a computer that are communicatively coupled with one another. Furthermore, the term “computer system” and/or “system” may refer to multiple computer devices and/or multiple computing systems that are communicatively coupled with one another and configured to share computing and/or networking resources.

The term “appliance,” “computer appliance,” or the like, as used herein refers to a computer device or computer system with program code (e.g., software or firmware) that is specifically designed to provide a specific computing resource. A “virtual appliance” is a virtual machine image to be implemented by a hypervisor-equipped device that virtualizes or emulates a computer appliance or otherwise is dedicated to provide a specific computing resource. The term “element” refers to a unit that is indivisible at a given level of abstraction and has a clearly defined boundary, wherein an element may be any type of entity including, for example, one or more devices, systems, controllers, network elements, modules, etc., or combinations thereof. The term “device” refers to a physical entity embedded inside, or attached to, another physical entity in its vicinity, with capabilities to convey digital information from or to that physical entity. The term “entity” refers to a distinct component of an architecture or device, or information transferred as a payload. The term “controller” refers to an element or entity that has the capability to affect a physical entity, such as by changing its state or causing the physical entity to move.

The term “cloud computing” or “cloud” refers to a paradigm for enabling network access to a scalable and elastic pool of shareable computing resources with self-service provisioning and administration on-demand and without active management by users. Cloud computing provides cloud computing services (or cloud services), which are one or more capabilities offered via cloud computing that are invoked using a defined interface (e.g., an API or the like). The term “computing resource” or simply “resource” refers to any physical or virtual component, or usage of such components, of limited availability within a computer system or network. Examples of computing resources include usage/access to, for a period of time, servers, processor(s), storage equipment, memory devices, memory areas, networks, electrical power, input/output (peripheral) devices, mechanical devices, network connections (e.g., channel s/links, ports, network sockets, etc.), operating systems, virtual machines (VMs), software/applications, computer files, and/or the like. A “hardware resource” may refer to compute, storage, and/or network resources provided by physical hardware element(s). A “virtualized resource” may refer to compute, storage, and/or network resources provided by virtualization infrastructure to an application, device, system, etc. The term “network resource” or “communication resource” may refer to resources that are accessible by computer devices/systems via a communications network. The term “system resources” may refer to any kind of shared entities to provide services, and may include computing and/or network resources. System resources may be considered as a set of coherent functions, network data objects or services, accessible through a server where such system resources reside on a single host or multiple hosts and are clearly identifiable. As used herein, the term “cloud service provider” (or CSP) indicates an organization which operates typically large-scale “cloud” resources comprised of centralized, regional, and edge data centers (e.g., as used in the context of the public cloud). In other examples, a CSP may also be referred to as a Cloud Service Operator (CSO). References to “cloud computing” generally refer to computing resources and services offered by a CSP or a CSO, at remote locations with at least some increased latency, distance, or constraints relative to edge computing.

As used herein, the term “data center” refers to a purpose-designed structure that is intended to house multiple high-performance compute and data storage nodes such that a large amount of compute, data storage and network resources are present at a single location. This often entails specialized rack and enclosure systems, suitable heating, cooling, ventilation, security, fire suppression, and power delivery systems. The term may also refer to a compute and data storage node in some contexts. A data center may vary in scale between a centralized or cloud data center (e.g., largest), regional data center, and edge data center (e.g., smallest).

As used herein, the term “edge computing” refers to the implementation, coordination, and use of computing and resources at locations closer to the “edge” or collection of “edges” of a network. Deploying computing resources at the network’s edge may reduce application and network latency, reduce network backhaul traffic and associated energy consumption, improve service capabilities, improve compliance with security or data privacy requirements (especially as compared to conventional cloud computing), and improve total cost of ownership). As used herein, the term “edge compute node” refers to a real-world, logical, or virtualized implementation of a compute-capable element in the form of a device, gateway, bridge, system or subsystem, component, whether operating in a server, client, endpoint, or peer mode, and whether located at an “edge” of an network or at a connected location further within the network. References to a “node” used herein are generally interchangeable with a “device”, “component”, and “sub-system”; however, references to an “edge computing system” or “edge computing network” generally refer to a distributed architecture, organization, or collection of multiple nodes and devices, and which is organized to accomplish or offer some aspect of services or resources in an edge computing setting.

Additionally or alternatively, the term “Edge Computing” refers to a concept, as described in [6], that enables operator and 3rd party services to be hosted close to the UE's access point of attachment, to achieve an efficient service delivery through the reduced end-to- end latency and load on the transport network. As used herein, the term “Edge Computing Service Provider” refers to a mobile network operator or a 3rd party service provider offering Edge Computing service. As used herein, the term “Edge Data Network” refers to a local Data Network (DN) that supports the architecture for enabling edge applications. As used herein, the term “Edge Hosting Environment” refers to an environment providing support required for Edge Application Server's execution. As used herein, the term “Application Server” refers to application software resident in the cloud performing the server function.

The term “Internet of Things” or “IoT” refers to a system of interrelated computing devices, mechanical and digital machines capable of transferring data with little or no human interaction, and may involve technologies such as real-time analytics, machine learning and/or AI, embedded systems, wireless sensor networks, control systems, automation (e.g., smarthome, smart building and/or smart city technologies), and the like. IoT devices are usually low-power devices without heavy compute or storage capabilities. “Edge IoT devices” may be any kind of IoT devices deployed at a network’s edge.

As used herein, the term “cluster” refers to a set or grouping of entities as part of an edge computing system (or systems), in the form of physical entities (e.g., different computing systems, networks or network groups), logical entities (e.g., applications, functions, security constructs, containers), and the like. In some locations, a “cluster” is also referred to as a “group” or a “domain”. The membership of cluster may be modified or affected based on conditions or functions, including from dynamic or property-based membership, from network or system management scenarios, or from various example techniques discussed below which may add, modify, or remove an entity in a cluster. Clusters may also include or be associated with multiple layers, levels, or properties, including variations in security features and results based on such layers, levels, or properties.

The term “application” may refer to a complete and deployable package, environment to achieve a certain function in an operational environment. The term “AI/ML application” or the like may be an application that contains some AI/ML models and application-level descriptions. The term “machine learning” or “ML” refers to the use of computer systems implementing algorithms and/or statistical models to perform specific task(s) without using explicit instructions, but instead relying on patterns and inferences. ML algorithms build or estimate mathematical model(s) (referred to as “ML models” or the like) based on sample data (referred to as “training data,” “model training information,” or the like) in order to make predictions or decisions without being explicitly programmed to perform such tasks. Generally, an ML algorithm is a computer program that learns from experience with respect to some task and some performance measure, and an ML model may be any object or data structure created after an ML algorithm is trained with one or more training datasets. After training, an ML model may be used to make predictions on new datasets. Although the term “ML algorithm” refers to different concepts than the term “ML model,” these terms as discussed herein may be used interchangeably for the purposes of the present disclosure.

The term “machine learning model,” “ML model,” or the like may also refer to ML methods and concepts used by an ML-assisted solution. An “ML-assisted solution” is a solution that addresses a specific use case using ML algorithms during operation. ML models include supervised learning (e.g., linear regression, k-nearest neighbor (KNN), decision tree algorithms, support machine vectors, Bayesian algorithm, ensemble algorithms, etc.) unsupervised learning (e.g., K-means clustering, principle component analysis (PCA), etc.), reinforcement learning (e.g., Q-learning, multi-armed bandit learning, deep RL, etc.), neural networks, and the like. Depending on the implementation a specific ML model could have many sub-models as components and the ML model may train all sub-models together. Separately trained ML models can also be chained together in an ML pipeline during inference. An “ML pipeline” is a set of functionalities, functions, or functional entities specific for an ML-assisted solution; an ML pipeline may include one or several data sources in a data pipeline, a model training pipeline, a model evaluation pipeline, and an actor. The “actor” is an entity that hosts an ML assisted solution using the output of the ML model inference). The term “ML training host” refers to an entity, such as a network function, that hosts the training of the model. The term “ML inference host” refers to an entity, such as a network function, that hosts model during inference mode (which includes both the model execution as well as any online learning if applicable). The ML-host informs the actor about the output of the ML algorithm, and the actor takes a decision for an action (an “action” is performed by an actor as a result of the output of an ML assisted solution). The term “model inference information” refers to information used as an input to the ML model for determining inference(s); the data used to train an ML model and the data used to determine inferences may overlap, however, “training data” and “inference data” refer to different concepts.

The terms “instantiate,” “instantiation,” and the like as used herein refers to the creation of an instance. An “instance” also refers to a concrete occurrence of an object, which may occur, for example, during execution of program code. The term “information element” refers to a structural element containing one or more fields. The term “field” refers to individual contents of an information element, or a data element that contains content. As used herein, a “database object”, “data structure”, or the like may refer to any representation of information that is in the form of an object, attribute-value pair (A VP), key- value pair (KVP), tuple, etc., and may include variables, data structures, functions, methods, classes, database records, database fields, database entities, associations between data and/or database entities (also referred to as a “relation”), blocks and links between blocks in block chain implementations, and/or the like.

An “information object,” as used herein, refers to a collection of structured data and/or any representation of information, and may include, for example electronic documents (or “documents”), database objects, data structures, files, audio data, video data, raw data, archive files, application packages, and/or any other like representation of information. The terms “electronic document” or “document,” may refer to a data structure, computer file, or resource used to record data, and includes various file types and/or data formats such as word processing documents, spreadsheets, slide presentations, multimedia items, webpage and/or source code documents, and/or the like. As examples, the information objects may include markup and/or source code documents such as HTML, XML, JSON, Apex®, CSS, JSP, MessagePack™, Apache® Thrift™, ASN.l, Google® Protocol Buffers (protobuf), or some other document(s)/format(s) such as those discussed herein. An information object may have both a logical and a physical structure. Physically, an information object comprises one or more units called entities. An entity is a unit of storage that contains content and is identified by a name. An entity may refer to other entities to cause their inclusion in the information object. An information object begins in a document entity, which is also referred to as a root element (or “root”). Logically, an information object comprises one or more declarations, elements, comments, character references, and processing instructions, all of which are indicated in the information object (e.g., using markup).

The term “data item” as used herein refers to an atomic state of a particular object with at least one specific property at a certain point in time. Such an object is usually identified by an object name or object identifier, and properties of such an object are usually defined as database objects (e.g., fields, records, etc.), object instances, or data elements (e.g., mark-up language elements/tags, etc.). Additionally or alternatively, the term “data item” as used herein may refer to data elements and/or content items, although these terms may refer to difference concepts. The term “data element” or “element” as used herein refers to a unit that is indivisible at a given level of abstraction and has a clearly defined boundary. A data element is a logical component of an information object (e.g., electronic document) that may begin with a start tag (e.g., “<element>”) and end with a matching end tag (e.g., “</element>”), or only has an empty element tag (e.g., “<element />”). Any characters between the start tag and end tag, if any, are the element’s content (referred to herein as “content items” or the like).

The content of an entity may include one or more content items, each of which has an associated datatype representation. A content item may include, for example, attribute values, character values, URIs, qualified names (qnames), parameters, and the like. A qname is a fully qualified name of an element, attribute, or identifier in an information object. A qname associates a URI of a namespace with a local name of an element, attribute, or identifier in that namespace. To make this association, the qname assigns a prefix to the local name that corresponds to its namespace. The qname comprises a URI of the namespace, the prefix, and the local name. Namespaces are used to provide uniquely named elements and attributes in information objects. Content items may include text content (e.g., “<element>content item</element>”), attributes (e.g., “<element attribute="attributeValue">”), and other elements referred to as “child elements” (e.g., “<elementl><element2>content item</element2></elementl>”). An “attribute” may refer to a markup construct including a name-value pair that exists within a start tag or empty element tag. Attributes contain data related to its element and/or control the element’s behavior.

The term “channel” as used herein refers to any transmission medium, either tangible or intangible, which is used to communicate data or a data stream. The term “channel” may be synonymous with and/or equivalent to “communications channel,” “data communications channel,” “transmission channel,” “data transmission channel,” “access channel,” “data access channel,” “link,” “data link,” “carrier,” “radiofrequency carrier,” and/or any other like term denoting a pathway or medium through which data is communicated. Additionally, the term “link” as used herein refers to a connection between two devices through a RAT for the purpose of transmitting and receiving information. As used herein, the term “radio technology” refers to technology for wireless transmission and/or reception of electromagnetic radiation for information transfer. The term “radio access technology” or “RAT” refers to the technology used for the underlying physical connection to a radio based communication network. As used herein, the term “communication protocol” (either wired or wireless) refers to a set of standardized rules or instructions implemented by a communication device and/or system to communicate with other devices and/or systems, including instructions for packetizing/depacketizing data, modulating/demodulating signals, implementation of protocols stacks, and/or the like.

As used herein, the term “radio technology” refers to technology for wireless transmission and/or reception of electromagnetic radiation for information transfer. The term “radio access technology” or “RAT” refers to the technology used for the underlying physical connection to a radio based communication network. As used herein, the term “communication protocol” (either wired or wireless) refers to a set of standardized rules or instructions implemented by a communication device and/or system to communicate with other devices and/or systems, including instructions for packetizing/depacketizing data, modulating/demodulating signals, implementation of protocols stacks, and/or the like. Examples of wireless communications protocols may be used in various embodiments include a Global System for Mobile Communications (GSM) radio communication technology, a General Packet Radio Service (GPRS) radio communication technology, an Enhanced Data Rates for GSM Evolution (EDGE) radio communication technology, and/or a Third Generation Partnership Project (3 GPP) radio communication technology including, for example, 3 GPP Fifth Generation (5G) or New Radio (NR), Universal Mobile Telecommunications System (UMTS), Freedom of Multimedia Access (FOMA), Long Term Evolution (LTE), LTE- Advanced (LTE Advanced), LTE Extra, LTE-A Pro, cdmaOne (2G), Code Division Multiple Access 2000 (CDMA 2000), Cellular Digital Packet Data (CDPD), Mobitex, Circuit Switched Data (CSD), High-Speed CSD (HSCSD), Universal Mobile Telecommunications System (UMTS), Wideband Code Division Multiple Access (W-CDM), High Speed Packet Access (HSPA), HSPA Plus (HSPA+), Time Division-Code Division Multiple Access (TD-CDMA), Time Division-Synchronous Code Division Multiple Access (TD-SCDMA), LTE LAA, MuLTEfire, UMTS Terrestrial Radio Access (UTRA), Evolved UTRA (E-UTRA), Evolution- Data Optimized or Evolution-Data Only (EV-DO), Advanced Mobile Phone System (AMPS), Digital AMPS (D-AMPS), Total Access Communication System/Extended Total Access Communication System (TACS/ETACS), Push-to-talk (PTT), Mobile Telephone System (MTS), Improved Mobile Telephone System (IMTS), Advanced Mobile Telephone System (AMTS), Cellular Digital Packet Data (CDPD), DataTAC, Integrated Digital Enhanced Network (iDEN), Personal Digital Cellular (PDC), Personal Handy-phone System (PHS), Wideband Integrated Digital Enhanced Network (WiDEN), iBurst, Unlicensed Mobile Access (UMA), also referred to as also referred to as 3 GPP Generic Access Network, or GAN standard), Bluetooth®, Bluetooth Low Energy (BLE), IEEE 802.15.4 based protocols (e.g., IPv6 over Low power Wireless Personal Area Networks (6L0WPAN), WirelessHART, MiWi, Thread, 802.11a, etc.) WiFi-direct, ANT/ANT+, ZigBee, Z-Wave, 3GPP device-to-device (D2D) or Proximity Services (ProSe), Universal Plug and Play (UPnP), Low-Power Wide- Area-Network (LPWAN), Long Range Wide Area Network (LoRA) or LoRaWAN™ developed by Semtech and the LoRa Alliance, Sigfox, Wireless Gigabit Alliance (WiGig) standard, Worldwide Interoperability for Microwave Access (WiMAX), mmWave standards in general (e.g., wireless systems operating at 10-300 GHz and above such as WiGig, IEEE 802. llad, IEEE 802.1 lay, etc.), V2X communication technologies (including 3GPP C-V2X), Dedicated Short Range Communications (DSRC) communication systems such as Intelligent- Transport- Systems (ITS) including the European ITS-G5, ITS-G5B, ITS-G5C, etc. In addition to the standards listed above, any number of satellite uplink technologies may be used for purposes of the present disclosure including, for example, radios compliant with standards issued by the International Telecommunication Union (ITU), or the European Telecommunications Standards Institute (ETSI), among others. The examples provided herein are thus understood as being applicable to various other communication technologies, both existing and not yet formulated. The term “access network” refers to any network, using any combination of radio technologies, RATs, and/or communication protocols, used to connect user devices and service providers. In the context of WLANs, an “access network” is an IEEE 802 local area network (LAN) or metropolitan area network (MAN) between terminals and access routers connecting to provider services. The term “access router” refers to router that terminates a medium access control (MAC) service from terminals and forwards user traffic to information servers according to Internet Protocol (IP) addresses.

The term “SMTC” refers to an SSB-based measurement timing configuration configured by SSB-MeasurementTimingConfiguration. The term “SSB” refers to a synchronization signal/Physical Broadcast Channel (SS/PBCH) block, which includes a Primary Syncrhonization Signal (PSS), a Secondary Syncrhonization Signal (SSS), and a PBCH. The term “a “Primary Cell” refers to the MCG cell, operating on the primary frequency, in which the UE either performs the initial connection establishment procedure or initiates the connection re-establishment procedure. The term “Primary SCG Cell” refers to the SCG cell in which the UE performs random access when performing the Reconfiguration with Sync procedure for DC operation. The term “Secondary Cell” refers to a cell providing additional radio resources on top of a Special Cell for a UE configured with CA. The term “Secondary Cell Group” refers to the subset of serving cells comprising the PSCell and zero or more secondary cells for a UE configured with DC. The term “Serving Cell” refers to the primary cell for a UE in RRC CONNECTED not configured with CA/DC there is only one serving cell comprising of the primary cell. The term “serving cell” or “serving cells” refers to the set of cells comprising the Special Cell(s) and all secondary cells for a UE in RRC CONNECTED configured with CA. The term “Special Cell” refers to the PCell of the MCG or the PSCell of the SCG for DC operation; otherwise, the term “Special Cell” refers to the Pcell.

The term “A1 policy” refers to a type of declarative policies expressed using formal statements that enable the non-RT RIC function in the SMO to guide the near-RT RIC function, and hence the RAN, towards better fulfilment of the RAN intent.

The term “A1 Enrichment information” refers to information utilized by near-RT RIC that is collected or derived at SMO/non-RT RIC either from non-network data sources or from network functions themselves.

The term “A1 -Policy Based Traffic Steering Process Mode” refers to an operational mode in which the Near-RT RIC is configured through A1 Policy to use Traffic Steering Actions to ensure a more specific notion of network performance (for example, applying to smaller groups of E2 Nodes and UEs in the RAN) than that which it ensures in the Background Traffic Steering.

The term “Background Traffic Steering Processing Mode” refers to an operational mode in which the Near-RT RIC is configured through 01 to use Traffic Steering Actions to ensure a general background network performance which applies broadly across E2 Nodes and EEs in the RAN.

The term “Baseline RAN Behavior” refers to the default RAN behavior as configured at the E2 Nodes by SMO

The term “E2” refers to an interface connecting the Near-RT RIC and one or more O- CU-CPs, one or more O-CU-UPs, one or more O-DUs, and one or more O-eNBs.

The term “E2 Node” refers to a logical node terminating E2 interface. In this version of the specification, ORAN nodes terminating E2 interface are: for NR access: O-CU-CP, O- CU-UP, O-DU or any combination; and for E-UTRA access: O-eNB.

The term “Intents”, in the context of O-RAN systems/implementations, refers to declarative policy to steer or guide the behavior of RAN functions, allowing the RAN function to calculate the optimal result to achieve stated objective.

The term “O-RAN non-real-time RAN Intelligent Controller” or “non-RT RIC” refers to a logical function that enables non-real-time control and optimization of RAN elements and resources, AI/ML workflow including model training and updates, and policy-based guidance of applications/features in Near-RT RIC.

The term “Near-RT RIC” or “O-RAN near-real-time RAN Intelligent Controller” refers to a logical function that enables near-real-time control and optimization of RAN elements and resources via fine-grained (e.g., UE basis, Cell basis) data collection and actions over E2 interface.

The term “O-RAN Central Unit” or “O-CU” refers to a logical node hosting RRC, SDAP and PDCP protocols.

The term “O-RAN Central Unit - Control Plane” or “O-CU-CP” refers to a logical node hosting the RRC and the control plane part of the PDCP protocol.

The term “O-RAN Central Unit - User Plane” or “O-CU-UP” refers to a logical node hosting the user plane part of the PDCP protocol and the SDAP protocol

The term “O-RAN Distributed Unit” or “O-DU” refers to a logical node hosting RLC/MAC/High-PHY layers based on a lower layer functional split.

The term “O-RAN eNB” or “O-eNB” refers to an eNB or ng-eNB that supports E2 interface. The term “O-RAN Radio Unit” or “O-RU” refers to a logical node hosting Low-PHY layer and RF processing based on a lower layer functional split. This is similar to 3GPP’s “TRP” or “RRH” but more specific in including the Low-PHY layer (FFT/iFFT, PRACH extraction).

The term “01” refers to an interface between orchestration & management entities (Orchestration/NMS) and O-RAN managed elements, for operation and management, by which FCAPS management, Software management, File management and other similar functions shall be achieved.

The term “RAN UE Group” refers to an aggregations of UEs whose grouping is set in the E2 nodes through E2 procedures also based on the scope of A1 policies. These groups can then be the target of E2 CONTROL or POLICY messages.

The term “Traffic Steering Action” refers to the use of a mechanism to alter RAN behavior. Such actions include E2 procedures such as CONTROL and POLICY.

The term “Traffic Steering Inner Loop” refers to the part of the Traffic Steering processing, triggered by the arrival of periodic TS related KPM (Key Performance Measurement) from E2 Node, which includes UE grouping, setting additional data collection from the RAN, as well as selection and execution of one or more optimization actions to enforce Traffic Steering policies.

The term “Traffic Steering Outer Loop” refers to the part of the Traffic Steering processing, triggered by the near-RT RIC setting up or updating Traffic Steering aware resource optimization procedure based on information from A1 Policy setup or update, A1 Enrichment Information (El) and/or outcome of Near-RT RIC evaluation, which includes the initial configuration (preconditions) and injection of related A1 policies, Triggering conditions for TS changes.

The term “Traffic Steering Processing Mode” refers to an operational mode in which either the RAN or the Near-RT RIC is configured to ensure a particular network performance. This performance includes such aspects as cell load and throughput, and can apply differently to different E2 nodes and UEs. Throughout this process, Traffic Steering Actions are used to fulfill the requirements of this configuration.

The term “Traffic Steering Target” refers to the intended performance result that is desired from the network, which is configured to Near-RT RIC over 01.

Furthermore, any of the disclosed embodiments and example implementations can be embodied in the form of various types of hardware, software, firmware, middleware, or combinations thereof, including in the form of control logic, and using such hardware or software in a modular or integrated manner. Additionally, any of the software components or functions described herein can be implemented as software, program code, script, instructions, etc., operable to be executed by processor circuitry. These components, functions, programs, etc., can be developed using any suitable computer language such as, for example, Python, PyTorch, NumPy, Ruby, Ruby on Rails, Scala, Smalltalk, Java™, C++, C#, “C”, Kotlin, Swift, Rust, Go (or “Golang”), EMCAScript, JavaScript, TypeScript, Jscript, ActionScript, Server- Side JavaScript (SSJS), PHP, Pearl, Lua, Torch/Lua with Just-In Time compiler (LuaJIT), Accelerated Mobile Pages Script (AMPscript), VBScript, JavaServer Pages (JSP), Active Server Pages (ASP), Node.js, ASP.NET, JAMscript, Hypertext Markup Language (HTML), extensible HTML (XHTML), Extensible Markup Language (XML), XML User Interface Language (XUL), Scalable Vector Graphics (SVG), RESTful API Modeling Language (RAML), wiki markup or Wikitext, Wireless Markup Language (WML), Java Script Object Notion (JSON), Apache® MessagePack™, Cascading Stylesheets (CSS), extensible stylesheet language (XSL), Mustache template language, Handlebars template language, Guide Template Language (GTL), Apache® Thrift, Abstract Syntax Notation One (ASN.1), Google® Protocol Buffers (protobuf), Bitcoin Script, EVM® bytecode, Solidity™, Vyper (Python derived), Bamboo, Lisp Like Language (LLL), Simplicity provided by Blockstream™, Rholang, Michelson, Counterfactual, Plasma, Plutus, Sophia, Salesforce® Apex®, and/or any other programming language or development tools including proprietary programming languages and/or development tools. The software code can be stored as a computer- or processor- executable instructions or commands on a physical non-transitory computer-readable medium. Examples of suitable media include RAM, ROM, magnetic media such as a hard-drive or a floppy disk, or an optical medium such as a compact disk (CD) or DVD (digital versatile disk), flash memory, and the like, or any combination of such storage or transmission devices.

Claims

CLAIMS What is claimed is:

1. An apparatus of a management service (MnS) producer, the apparatus comprising processing circuitry coupled to storage, the processing circuitry configured to: obtain input data related to network and service within a 5G system (5GS) to provide MDA capability to an MnS consumer within the 5GS; generate one or more MDA reports, wherein the one or more MDA reports comprise one or more common information elements, at least one MDA type associated with the MDA capability, and one or more MDA type specific information elements; and cause to send the one or more MDA reports to the MnS consumer.

2. The apparatus of claim 1, wherein the MnS producer is capable of acting as an machine learning (ML) capabilities producer to provide ML capabilities to an ML capability consumer.

3. The apparatus of claim 1, wherein the MnS producer is capable of acting as an machine learning (ML) capability consumer to receive ML capabilities from an ML capability producer.

4. The apparatus of claim 3, wherein an ML capability producer is configured to support ML for one or more MnSs in the 5GS by being configured to: receive training data from the ML capability consumer; train an ML model; and establish an ML capability in the ML capability producer based on the training data.

5. The apparatus of claim 4, wherein the ML capability producer is further configured to: send a training report to the ML capability consumer; and identify a validation feedback received from the ML capability consumer.

6. The apparatus of claim 5, wherein the ML capability producer is further configured to re-train the ML model based on the validation feedback.

7. The apparatus of claim 1, wherein the MnS is management data analytics service (MDAS).

8. The apparatus of claim 1, wherein the one or more common information elements comprise information that is common to a plurality of management data analytics (MDA) reports.

9. The apparatus of claim 5, wherein the validation feedback is associated with validating the training report related to an ML capability.

10. The apparatus of claim 8, wherein the one or more common information elements are for at least one or more of an identifier that uniquely identifies the MDA report between an MDAS producer and MDAS consumer, a time when the MDA report was generated, indication type of MDA capability for analysis of a corresponding issue, an identifier of an issue described in an MDA report, Cause of the issue described in the MDA report, severity level of the issue described in the MDA report, a time when the issue described in the MDA report started, a time when the issue described in the MDA report was lately updated, a time when the issue described in the MDA report stopped, managed object instances (MOIs) that are affected by the issue described in the MDA report, or recommended actions to solve the issue described in the MDA report.

11. A computer-readable storage medium comprising instructions to cause processing circuitry, upon execution of the instructions by the processing circuitry, to: obtain, by a management service (MnS) producer, input data related to network and service within a 5G system (5GS) to provide MDA capability to an MnS consumer within the 5GS; generate one or more MDA reports, wherein the one or more MDA reports comprise one or more common information elements, at least one MDA type associated with the MDA capability, and one or more MDA type specific information elements; and cause to send the one or more MDA reports to the MnS consumer.

12. The computer-readable storage medium of claim 11, wherein the MnS producer is capable of acting as an machine learning (ML) capabilities producer to provide ML capabilities to an ML capability consumer.

13. The computer-readable storage medium of claim 11, wherein the MnS producer is capable of acting as an machine learning (ML) capability consumer to receive ML capabilities from an ML capability producer.

14. The computer-readable storage medium of claim 13, wherein an ML capability producer is configured to support ML for one or more MnSs in the 5GS by being configured to: receive training data from the ML capability consumer; train an ML model; and establish an ML capability in the ML capability producer based on the training data.

15. The computer-readable storage medium of claim 14, wherein the ML capability producer is further configured to: send a training report to the ML capability consumer; and identify a validation feedback received from the ML capability consumer.

16. The computer-readable storage medium of claim 15, wherein the ML capability producer is further configured to re-train the ML model based on the validation feedback.

17. The computer-readable storage medium of claim 11, wherein the MnS is management data analytics service (MDAS).

18. The computer-readable storage medium of claim 11, wherein the one or more common information elements comprise information that is common to a plurality of management data analytics (MDA) reports.

19. The computer-readable storage medium of claim 15, wherein the validation feedback is associated with validating the training report related to an ML capability.

20. The computer-readable storage medium of claim 18, wherein the one or more common information elements are for at least one or more of an identifier that uniquely identifies the MDA report between an MDAS producer and MDAS consumer, a time when the MDA report was generated, indication type of MDA capability for analysis of a corresponding issue, an identifier of an issue described in an MDA report, Cause of the issue described in the MDA report, severity level of the issue described in the MD A report, a time when the issue described in the MDA report started, a time when the issue described in the MDA report was lately updated, a time when the issue described in the MDA report stopped, managed object instances (MOIs) that are affected by the issue described in the MDA report, or recommended actions to solve the issue described in the MDA report.

21. The computer-readable storage medium of any one of claims 20, wherein the recommended actions could be creating, modifying, and/or deleting of 3GPP MOI(s), and/or invoking one or more non-3GPP operations.

22. A method comprising: obtaining, by one or more processors of a management service (MnS) producer, input data related to network and service within a 5G system (5GS) to provide MDA capability to an MnS consumer within the 5GS; generating one or more MDA reports, wherein the one or more MDA reports comprise one or more common information elements, at least one MDA type associated with the MDA capability, and one or more MDA type specific information elements; and causing to send the one or more MDA reports to the MnS consumer.

23. The method of claim 22, wherein the MnS producer is capable of acting as an machine learning (ML) capabilities producer to provide ML capabilities to an ML capability consumer.

24. An apparatus comprising means for performing any of the methods of claims 22-23.

25. A network node comprising a communication interface and processing circuitry connected thereto and configured to perform the methods of claims 22-23.