Patent Publication Number: US-2023141237-A1

Title: Techniques for management data analytics (mda) process and service

Description:
CROSS REFERENCE TO RELATED APPLICATION 
     The present application claims priority to U.S. Provisional Patent Application No. 63/024,747, which was filed May 14, 2020. 
    
    
     FIELD 
     Various embodiments generally may relate to the field of wireless communications. 
     BACKGROUND 
     Some wireless cellular networks may include functional entities, such as a mobility robustness optimization (MRO) function and a mobility load balancing (MLB) function, that may change one or more parameters of a cell (e.g., a New Radio (NR) cell) of the network. Some of these functions may change the same parameters of the cell, potentially causing a conflict. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Embodiments will be readily understood by the following detailed description in conjunction with the accompanying drawings. To facilitate this description, like reference numerals designate like structural elements. Embodiments are illustrated by way of example and not by way of limitation in the figures of the accompanying drawings. 
         FIG.  1    illustrates a process for management data analytics (MDA), in accordance with various embodiments. 
         FIG.  2    schematically illustrates a wireless network in accordance with various embodiments. 
         FIG.  3    schematically illustrates components of a wireless network in accordance with various embodiments. 
         FIG.  4    is a block diagram illustrating components, 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. 
         FIG.  5    is a flowchart of an example process that may be performed by an MDA service producer, in accordance with various embodiments. 
         FIG.  6    is a flowchart of an example process that may be performed by an MDA service consumer, in accordance with various embodiments. 
     
    
    
     DETAILED 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). 
     Various embodiments herein provide techniques for management data analytics (MDA) for a wireless cellular network. An MDA service (MDAS) producer may receive raw data, classify the raw data, and analyze the raw data to generate an analytics output. The MDA process may be supported by machine learning (ML) techniques, and performed by the MDA service producer and assisted by a MDA service consumer. For example, the MDA service producer may receive training data from the MDA service consumer, and train a machine learning (ML) model based on the training data. The training data may include a training input and a corresponding desired output. 
     The MDA service producer may further receive raw data associated with one or more network functions (e.g., from the MDA service consumer). For example, the raw data associated with the one or more network functions may include one or more of historical changes made by one or more of the network functions; one or more most recent changes made by one or more of the network functions; one or more current network configurations; historical network performance data related to one or more of the network functions; current network performance data related to one or more of the network functions; or one or more policies or targets for one or more of the network functions. In some embodiments, the network performance data may include load information of one or more cells and/or handover performance information (e.g., measurements and/or other data associated with one or more handovers that are determined to be too early or too late). The MDA service producer may apply the trained ML model to the raw data to generate output data that indicates a conflict between the network functions and a recommended action to address the conflict. The conflict may be a potential (future) conflict or an existing conflict that already occurred. The recommended action may include, for example, one or more of: modify one or more policies or targets for one or more of the network functions; change a priority for one or more of the network functions; set or change a range of values of one or more parameters that one or more of the network functions are allowed to change; update a value of one or more parameters; or temporarily switch off one or more of the network functions. 
     In embodiments, the MDA consumer may validate the output data to generate validation data. The validation data may, for example, indicate whether the recommended action successfully addressed the conflict and/or effected another improvement. The MDA consumer may provide the validation data to the MDA producer. In embodiments, the MDA producer may use the validation data to further train the ML model. 
     The involvement of the MDA consumer in the MDA process may improve the ML model and/or the results of the MDA process. 
     Some network functions, such as a mobility robustness optimization (MRO) function and/or a mobility load balancing (MLB) function, may change one or more parameters of a cell (e.g., a New Radio (NR) cell) of the network. Some of these functions may change the same parameters of the cell, potentially causing a conflict. The MDA process described herein may prevent the conflict from happening and/or resolve the conflict when it occurs. 
     In some embodiments, the network functions may be self-organizing network (SON) functions. While aspects of various embodiments are described herein with respect to SON functions, the techniques may be used for any suitable network functions, such as one or more SON functions, management functions (MFs), network functions (NFs), application functions (AFs), and/or network and service optimization tools/functions, etc. In some embodiments, the MDA consumer may correspond to any suitable device, such as a device that implements one or more of the network functions, and/or another device. 
     Aspects of various embodiments are described further below. One or more of the described features may be added to a future version of 3GPP Technical Report (TR) 28.809: “Study on enhancement of Management Data Analytics (MDA).” 
     MDA process Various embodiments may provide techniques for MDA using a ML model. In embodiments, the consumer of the MDA process may be involved in the MDA process, e.g., to improve the accuracy of the MDA results. 
       FIG.  1    illustrates an MDA process  100  that utilizes ML in accordance with various embodiments. Some operations of the process  100  may be performed by a data classifier  102  and a ML model  104 . The data classifier  102  and/or ML model  104  may be implemented in a MDA service (MDAS) producer. The MDA process  100  may be performed in conjunction with a MDAS consumer. Aspects of the process  100  are described further below. 
     ML model training: The consumer may need to train the ML model  104  for MDA. For example, the consumer provides training data  106  (e.g., including training input and the desired output) to the MDAS producer (e.g., to the data classifier  102 ). The MDAS producer classifies the training data and uses the training input and the desired output to train the ML model  104  at  108 , e.g., to train the algorithm of the ML model  104  to be able to provide the desired output by analysis of the training input. The MDAS producer may provide an ML model training report as one kind of output data. 
     Data analysis: The MDAS producer (data classifier  102 ) classifies raw data  110  received and pass along to the trained ML model  104  for analysis. The MDAS producer (e.g., the trained ML model) may generate output data  112  based on the analysis. For example, the output data  112  may include an analytics report. 
     Validation: The consumer may validate the output data  112  provided by the MDAS producer (e.g., at  114  of the process  100 ). The output data to be validated may be the analytics report or the ML model training report as described above. The consumer may generate validation data  116  as part of the validation. The consumer may provide the validation data  116  as feedback to the MDAS producer (e.g., to the data classifier  102 ), and the MDAS producer may use the validation data  116  for further ML model training. 
     MDA Assisted SON Coordination 
     SON Conflict Prevention and Resolution 
     Use Case 
     Some SON functions, such as MRO function and MLB function, may change the same parameters of an NR cell and potentially cause conflict. For instance, the MRO function may need to change a handover (HO) with a neighbor cell to happen later (e.g., when the signal strength of the neighbor cell become stronger), however the MLB function may need to change HO to happen sooner to offload some traffic with the same neighbor cell. 
     The MDA process described herein may prevent potential SON conflicts from happening and/or resolve the conflicts soon after happening. 
     In various embodiments, the MDA process may analyze one or more of the following data (e.g., as training data and/or raw data) for identifying the potential SON conflict or detecting that the SON conflict occurred:
         historical and the most recent changes made by the SON functions;   the current network configurations;   historical and current network performance data related to the SON functions (for instance, load information of the NR cells, handover performance measurements (e.g., too early HOs, too late HOs, etc.)); and/or   policies and/or targets for the SON functions.       

     If the MDAS producer identifies a potential SON conflict and/or a SON conflict that already occurred, the MDAS producer may provide an analytics report (e.g., as output data). The analytics report may describe the conflict and the recommended actions to prevent or resolve the conflict. 
     The recommended actions for SON conflict prevention and/or resolution may include one or more of the following:
         modify the policies and targets for the SON function(s);   change the priority for the SON function(s);   set or change the range of the parameters value that the SON function(s) are allowed to change;   update the parameters value to correct the conflict (if already occurred); and/or   temporarily switch off one or more SON function(s).       

     Potential Capability Requirements 
     The MDAS producer may have one or more of the following capabilities to support the MDA process described herein (e.g., the process  100 ). 
     Management Requirements 
     REQ-MDA_MGMT-CON-1 The MDAS producer should have a capability allowing the consumer to train the MDA process. 
     REQ-MDA_MGMT-CON-2 The MDAS producer should have a capability to provide MDA process training report to the consumer. 
     REQ-MDA_MGMT-CON-3 The MDAS producer should have a capability to receive the validation data from the consumer and train the MDA process based on the received validation data. 
     Coordination Requirements 
     REQ-MDA_SONCO-CON-1 The MDAS producer should have a capability to provide the analytics report describing the identified potential SON conflict with recommended actions to prevent the conflict from happening. 
     REQ-MDA_SONCO-CON-2 The MDAS producer should have a capability to provide the analytics report describing the detected SON conflict with recommended actions to resolve the conflict. 
     Systems and Implementations 
       FIGS.  2 - 4    illustrate various systems, devices, and components that may implement aspects of disclosed embodiments. 
       FIG.  2    illustrates a network  200  in accordance with various embodiments. The network  200  may operate in a manner consistent with 3GPP 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  200  may include a UE  202 , which may include any mobile or non-mobile computing device designed to communicate with a RAN  204  via an over-the-air connection. The UE  202  may be, but is not limited to, a smartphone, tablet computer, wearable computer device, desktop computer, laptop computer, in-vehicle infotainment, in-car entertainment device, instrument cluster, head-up display 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, M2M or D2D device, IoT device, etc. 
     In some embodiments, the network  200  may include a plurality of UEs coupled directly with one another via a sidelink interface. The UEs may be M2M/D2D devices that communicate using physical sidelink channels such as, but not limited to, PSBCH, PSDCH, PSSCH, PSCCH, PSFCH, etc. 
     In some embodiments, the UE  202  may additionally communicate with an AP  206  via an over-the-air connection. The AP  206  may manage a WLAN connection, which may serve to offload some/all network traffic from the RAN  204 . The connection between the UE  202  and the AP  206  may be consistent with any IEEE 802.11 protocol, wherein the AP  206  could be a wireless fidelity (Wi-Fi®) router. In some embodiments, the UE  202 , RAN  204 , and AP  206  may utilize cellular-WLAN aggregation (for example, LWA/LWIP). Cellular-WLAN aggregation may involve the UE  202  being configured by the RAN  204  to utilize both cellular radio resources and WLAN resources. 
     The RAN  204  may include one or more access nodes, for example, AN  208 . AN  208  may terminate air-interface protocols for the UE  202  by providing access stratum protocols including RRC, PDCP, RLC, MAC, and L1 protocols. In this manner, the AN  208  may enable data/voice connectivity between CN  220  and the UE  202 . In some embodiments, the AN  208  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, which may be referred to as a CRAN or virtual baseband unit pool. The AN  208  be referred to as a BS, gNB, RAN node, eNB, ng-eNB, NodeB, RSU, TRxP, TRP, etc. The AN  208  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. 
     In embodiments in which the RAN  204  includes a plurality of ANs, they may be coupled with one another via an X2 interface (if the RAN  204  is an LTE RAN) or an Xn interface (if the RAN  204  is a 5G RAN). 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  204  may each manage one or more cells, cell groups, component carriers, etc. to provide the UE  202  with an air interface for network access. The UE  202  may be simultaneously connected with a plurality of cells provided by the same or different ANs of the RAN  204 . For example, the UE  202  and RAN  204  may use carrier aggregation to allow the UE  202  to connect with a plurality of component carriers, each corresponding to a Pcell or Scell. In dual connectivity scenarios, a first AN may be a master node that provides an MCG and a second AN may be secondary node that provides an SCG. The first/second ANs may be any combination of eNB, gNB, ng-eNB, etc. 
     The RAN  204  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  202  or AN  208  may be or act as a 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  204  may be an LTE RAN  210  with eNBs, for example, eNB  212 . The LTE RAN  210  may provide an LTE air interface 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  204  may be an NG-RAN  214  with gNBs, for example, gNB  216 , or ng-eNBs, for example, ng-eNB  218 . The gNB  216  may connect with 5G-enabled UEs using a 5G NR interface. The gNB  216  may connect with a 5G core through an NG interface, which may include an N2 interface or an N3 interface. The ng-eNB  218  may also connect with the 5G core through an NG interface, but may connect with a UE via an LTE air interface. The gNB  216  and the ng-eNB  218  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  214  and a UPF  248  (e.g., N3 interface), and an NG control plane (NG-C) interface, which is a signaling interface between the nodes of the NG-RAN214 and an AMF  244  (e.g., N2 interface). 
     The NG-RAN  214  may provide a 5G-NR air 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. 
     In some embodiments, 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  202  can be configured with multiple BWPs where each BWP configuration has a different SCS. When a BWP change is indicated to the UE  202 , 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  202  with different amount of frequency resources (for example, 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  202  and in some cases at the gNB  216 . A BWP containing a larger number of PRBs can be used for scenarios with higher traffic load. 
     The RAN  204  is communicatively coupled to CN  220  that includes network elements to provide various functions to support data and telecommunications services to customers/subscribers (for example, users of UE  202 ). The components of the CN  220  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  220  onto physical compute/storage resources in servers, switches, etc. A logical instantiation of the CN  220  may be referred to as a network slice, and a logical instantiation of a portion of the CN  220  may be referred to as a network sub-slice. 
     In some embodiments, the CN  220  may be an LTE CN  222 , which may also be referred to as an EPC. The LTE CN  222  may include MME  224 , SGW  226 , SGSN  228 , HSS  230 , PGW  232 , and PCRF  234  coupled with one another over interfaces (or “reference points”) as shown. Functions of the elements of the LTE CN  222  may be briefly introduced as follows. 
     The MME  224  may implement mobility management functions to track a current location of the UE  202  to facilitate paging, bearer activation/deactivation, handovers, gateway selection, authentication, etc. 
     The SGW  226  may terminate an S1 interface toward the RAN and route data packets between the RAN and the LTE CN  222 . The SGW  226  may be a local mobility anchor point for inter-RAN node handovers and also may provide an anchor for inter-3GPP mobility. Other responsibilities may include lawful intercept, charging, and some policy enforcement. 
     The SGSN  228  may track a location of the UE  202  and perform security functions and access control. In addition, the SGSN  228  may perform inter-EPC node signaling for mobility between different RAT networks; PDN and S-GW selection as specified by MME  224 ; MME selection for handovers; etc. The S3 reference point between the MME  224  and the SGSN  228  may enable user and bearer information exchange for inter-3GPP access network mobility in idle/active states. 
     The HSS  230  may include a database for network users, including subscription-related information to support the network entities&#39; handling of communication sessions. The HSS  230  can provide support for routing/roaming, authentication, authorization, naming/addressing resolution, location dependencies, etc. An S6a reference point between the HSS  230  and the MME  224  may enable transfer of subscription and authentication data for authenticating/authorizing user access to the LTE CN  220 . 
     The PGW  232  may terminate an SGi interface toward a data network (DN)  236  that may include an application/content server  238 . The PGW  232  may route data packets between the LTE CN  222  and the data network  236 . The PGW  232  may be coupled with the SGW  226  by an S5 reference point to facilitate user plane tunneling and tunnel management. The PGW  232  may further include a node for policy enforcement and charging data collection (for example, PCEF). Additionally, the SGi reference point between the PGW  232  and the data network  236  may be an operator external public, a private PDN, or an intra-operator packet data network, for example, for provision of IMS services. The PGW  232  may be coupled with a PCRF  234  via a Gx reference point. 
     The PCRF  234  is the policy and charging control element of the LTE CN  222 . The PCRF  234  may be communicatively coupled to the app/content server  238  to determine appropriate QoS and charging parameters for service flows. The PCRF  232  may provision associated rules into a PCEF (via Gx reference point) with appropriate TFT and QCI. 
     In some embodiments, the CN  220  may be a 5GC  240 . The 5GC  240  may include an AUSF  242 , AMF  244 , SMF  246 , UPF  248 , NSSF  250 , NEF  252 , NRF  254 , PCF  256 , UDM  258 , and AF  260  coupled with one another over interfaces (or “reference points”) as shown. Functions of the elements of the 5GC  240  may be briefly introduced as follows. 
     The AUSF  242  may store data for authentication of UE  202  and handle authentication-related functionality. The AUSF  242  may facilitate a common authentication framework for various access types. In addition to communicating with other elements of the 5GC  240  over reference points as shown, the AUSF  242  may exhibit an Nausf service-based interface. 
     The AMF  244  may allow other functions of the 5GC  240  to communicate with the UE  202  and the RAN  204  and to subscribe to notifications about mobility events with respect to the UE  202 . The AMF  244  may be responsible for registration management (for example, for registering UE  202 ), connection management, reachability management, mobility management, lawful interception of AMF-related events, and access authentication and authorization. The AMF  244  may provide transport for SM messages between the UE  202  and the SMF  246 , and act as a transparent proxy for routing SM messages. AMF  244  may also provide transport for SMS messages between UE  202  and an SMSF. AMF  244  may interact with the AUSF  242  and the UE  202  to perform various security anchor and context management functions. Furthermore, AMF  244  may be a termination point of a RAN CP interface, which may include or be an N2 reference point between the RAN  204  and the AMF  244 ; and the AMF  244  may be a termination point of NAS (N1) signaling, and perform NAS ciphering and integrity protection. AMF  244  may also support NAS signaling with the UE  202  over an N3 IWF interface. 
     The SMF  246  may be responsible for SM (for example, session establishment, tunnel management between UPF  248  and AN  208 ); UE IP address allocation and management (including optional authorization); selection and control of UP function; configuring traffic steering at UPF  248  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  244  over N2 to AN  208 ; and determining SSC mode of a session. SM may refer to management of a PDU session, and a PDU session or “session” may refer to a PDU connectivity service that provides or enables the exchange of PDUs between the UE  202  and the data network  236 . 
     The UPF  248  may act as an anchor point for intra-RAT and inter-RAT mobility, an external PDU session point of interconnect to data network  236 , and a branching point to support multi-homed PDU session. The UPF  248  may also perform packet routing and forwarding, perform packet inspection, enforce the user plane part of policy rules, lawfully intercept packets (UP collection), perform traffic usage reporting, perform QoS handling for a user plane (e.g., packet filtering, gating, UL/DL rate enforcement), perform uplink traffic verification (e.g., SDF-to-QoS flow mapping), transport level packet marking in the uplink and downlink, and perform downlink packet buffering and downlink data notification triggering. UPF  248  may include an uplink classifier to support routing traffic flows to a data network. 
     The NSSF  250  may select a set of network slice instances serving the UE  202 . The NSSF  250  may also determine allowed NSSAI and the mapping to the subscribed S-NSSAIs, if needed. The NS SF  250  may also determine the AMF set to be used to serve the UE  202 , or a list of candidate AMFs based on a suitable configuration and possibly by querying the NRF  254 . The selection of a set of network slice instances for the UE  202  may be triggered by the AMF  244  with which the UE  202  is registered by interacting with the NSSF  250 , which may lead to a change of AMF. The NSSF  250  may interact with the AMF  244  via an N22 reference point; and may communicate with another NSSF in a visited network via an N31 reference point (not shown). Additionally, the NSSF  250  may exhibit an Nnssf service-based interface. 
     The NEF  252  may securely expose services and capabilities provided by 3GPP network functions for third party, internal exposure/re-exposure, AFs (e.g., AF  260 ), edge computing or fog computing systems, etc. In such embodiments, the NEF  252  may authenticate, authorize, or throttle the AFs. NEF  252  may also translate information exchanged with the AF  260  and information exchanged with internal network functions. For example, the NEF  252  may translate between an AF-Service-Identifier and an internal 5GC information. NEF  252  may also receive information from other NFs based on exposed capabilities of other NFs. This information may be stored at the NEF  252  as structured data, or at a data storage NF using standardized interfaces. The stored information can then be re-exposed by the NEF  252  to other NFs and AFs, or used for other purposes such as analytics. Additionally, the NEF  252  may exhibit an Nnef service-based interface. 
     The NRF  254  may support service discovery functions, receive NF discovery requests from NF instances, and provide the information of the discovered NF instances to the NF instances. NRF  254  also maintains information of available NF instances and their supported services. As used herein, the terms “instantiate,” “instantiation,” and the like may refer to the creation of an instance, and an “instance” may refer to a concrete occurrence of an object, which may occur, for example, during execution of program code. Additionally, the NRF  254  may exhibit the Nnrf service-based interface. 
     The PCF  256  may provide policy rules to control plane functions to enforce them, and may also support unified policy framework to govern network behavior. The PCF  256  may also implement a front end to access subscription information relevant for policy decisions in a UDR of the UDM  258 . In addition to communicating with functions over reference points as shown, the PCF  256  exhibit an Npcf service-based interface. 
     The UDM  258  may handle subscription-related information to support the network entities&#39; handling of communication sessions, and may store subscription data of UE  202 . For example, subscription data may be communicated via an N8 reference point between the UDM  258  and the AMF  244 . The UDM  258  may include two parts, an application front end and a UDR. The UDR may store subscription data and policy data for the UDM  258  and the PCF  256 , and/or structured data for exposure and application data (including PFDs for application detection, application request information for multiple UEs  202 ) for the NEF  252 . The Nudr service-based interface may be exhibited by the UDR  221  to allow the UDM  258 , PCF  256 , and NEF  252  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  258  may exhibit the Nudm service-based interface. 
     The AF  260  may provide application influence on traffic routing, provide access to NEF, and interact with the policy framework for policy control. 
     In some embodiments, the 5GC  240  may enable edge computing by selecting operator/3rd party services to be geographically close to a point that the UE  202  is attached to the network. This may reduce latency and load on the network. To provide edge-computing implementations, the 5GC  240  may select a UPF  248  close to the UE  202  and execute traffic steering from the UPF  248  to data network  236  via the N6 interface. This may be based on the UE subscription data, UE location, and information provided by the AF  260 . In this way, the AF  260  may influence UPF (re)selection and traffic routing. Based on operator deployment, when AF  260  is considered to be a trusted entity, the network operator may permit AF  260  to interact directly with relevant NFs. Additionally, the AF  260  may exhibit an Naf service-based interface. 
     The data network  236  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/content server  238 . 
       FIG.  3    schematically illustrates a wireless network  300  in accordance with various embodiments. The wireless network  300  may include a UE  302  in wireless communication with an AN  304 . The UE  302  and AN  304  may be similar to, and substantially interchangeable with, like-named components described elsewhere herein. 
     The UE  302  may be communicatively coupled with the AN  304  via connection  306 . The connection  306  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-6 GHz frequencies. 
     The UE  302  may include a host platform  308  coupled with a modem platform  310 . The host platform  308  may include application processing circuitry  312 , which may be coupled with protocol processing circuitry  314  of the modem platform  310 . The application processing circuitry  312  may run various applications for the UE  302  that source/sink application data. The application processing circuitry  312  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  314  may implement one or more of layer operations to facilitate transmission or reception of data over the connection  306 . The layer operations implemented by the protocol processing circuitry  314  may include, for example, MAC, RLC, PDCP, RRC and NAS operations. 
     The modem platform  310  may further include digital baseband circuitry  316  that may implement one or more layer operations that are “below” layer operations performed by the protocol processing circuitry  314  in a network protocol stack. These operations may include, for example, PHY operations including one or more of HARQ-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  310  may further include transmit circuitry  318 , receive circuitry  320 , RF circuitry  322 , and RF front end (RFFE)  324 , which may include or connect to one or more antenna panels  326 . Briefly, the transmit circuitry  318  may include a digital-to-analog converter, mixer, intermediate frequency (IF) components, etc.; the receive circuitry  320  may include an analog-to-digital converter, mixer, IF components, etc.; the RF circuitry  322  may include a low-noise amplifier, a power amplifier, power tracking components, etc.; RFFE  324  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  318 , receive circuitry  320 , RF circuitry  322 , RFFE  324 , and antenna panels  326  (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  314  may include one or more instances of control circuitry (not shown) to provide control functions for the transmit/receive components. 
     A UE reception may be established by and via the antenna panels  326 , RFFE  324 , RF circuitry  322 , receive circuitry  320 , digital baseband circuitry  316 , and protocol processing circuitry  314 . In some embodiments, the antenna panels  326  may receive a transmission from the AN  304  by receive-beamforming signals received by a plurality of antennas/antenna elements of the one or more antenna panels  326 . 
     A UE transmission may be established by and via the protocol processing circuitry  314 , digital baseband circuitry  316 , transmit circuitry  318 , RF circuitry  322 , RFFE  324 , and antenna panels  326 . In some embodiments, the transmit components of the UE  304  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  326 . 
     Similar to the UE  302 , the AN  304  may include a host platform  328  coupled with a modem platform  330 . The host platform  328  may include application processing circuitry  332  coupled with protocol processing circuitry  334  of the modem platform  330 . The modem platform may further include digital baseband circuitry  336 , transmit circuitry  338 , receive circuitry  340 , RF circuitry  342 , RFFE circuitry  344 , and antenna panels  346 . The components of the AN  304  may be similar to and substantially interchangeable with like-named components of the UE  302 . In addition to performing data transmission/reception as described above, the components of the AN  308  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.  4    is a block diagram illustrating components, 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.  4    shows a diagrammatic representation of hardware resources  400  including one or more processors (or processor cores)  410 , one or more memory/storage devices  420 , and one or more communication resources  430 , each of which may be communicatively coupled via a bus  440  or other interface circuitry. For embodiments where node virtualization (e.g., NFV) is utilized, a hypervisor  402  may be executed to provide an execution environment for one or more network slices/sub-slices to utilize the hardware resources  400 . 
     The processors  410  may include, for example, a processor  412  and a processor  414 . The processors  410  may be, for example, a central processing unit (CPU), a reduced instruction set computing (RISC) processor, a complex instruction set computing (CISC) processor, a graphics processing unit (GPU), a DSP such as a baseband processor, an ASIC, an FPGA, a radio-frequency integrated circuit (RFIC), another processor (including those discussed herein), or any suitable combination thereof. 
     The memory/storage devices  420  may include main memory, disk storage, or any suitable combination thereof. The memory/storage devices  420  may include, but are not limited to, any type of volatile, non-volatile, or semi-volatile memory such as dynamic random access memory (DRAM), static random access memory (SRAM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), Flash memory, solid-state storage, etc. 
     The communication resources  430  may include interconnection or network interface controllers, components, or other suitable devices to communicate with one or more peripheral devices  404  or one or more databases  406  or other network elements via a network  408 . For example, the communication resources  430  may include wired communication components (e.g., for coupling via USB, Ethernet, etc.), cellular communication components, NFC components, Bluetooth® (or Bluetooth® Low Energy) components, Wi-Fi® components, and other communication components. 
     Instructions  450  may comprise software, a program, an application, an applet, an app, or other executable code for causing at least any of the processors  410  to perform any one or more of the methodologies discussed herein. The instructions  450  may reside, completely or partially, within at least one of the processors  410  (e.g., within the processor&#39;s cache memory), the memory/storage devices  420 , or any suitable combination thereof. Furthermore, any portion of the instructions  450  may be transferred to the hardware resources  400  from any combination of the peripheral devices  404  or the databases  406 . Accordingly, the memory of processors  410 , the memory/storage devices  420 , the peripheral devices  404 , and the databases  406  are examples of computer-readable and machine-readable media. 
     Example Procedures 
     In some embodiments, the electronic device(s), network(s), system(s), chip(s) or component(s), or portions or implementations thereof, of  FIGS.  2 - 4   , or some other figure herein, may be configured to perform one or more processes, techniques, or methods as described herein, or portions thereof. One such process  500  is depicted in  FIG.  5   . In some embodiments, the process  500  may be performed by a service producer for management data analytics (MDA) for a wireless communication network. 
     In various embodiments, the process may include, at  502 , receiving training data from a MDA consumer, the training data including a training input and a corresponding desired output. The training data may be associated with a managed network or service. For example, the training data may be associated with one or more network functions, such as one or more SON functions and/or other network functions. 
     At  504 , the process  500  may further include training a machine learning model based on the training data. At  506 , the process  500  may further include receiving raw data associated with network functions (e.g., one or more SON functions, such as a mobility robustness optimization function (MRO) and/or a mobility load balancing (MLB) function). In embodiments, the raw data may be received from the MDA consumer or from some other data source (e.g., from the network functions). The raw data may include, for example, one or more of: historical changes made by one or more network functions; one or more most recent changes made by one or more network functions; one or more current network configurations; historical and/or current network performance data related to one or more network functions; and/or one or more policies and/or targets for the network functions. In some embodiments, the network performance data may include load information of one or more cells and/or handover performance information (e.g., measurements and/or other information associated with one or more handovers that were determined to be too early or too late). The one or more network functions may include, for example, one or more SON functions and/or other network functions. 
     At  508 , the process  500  may further include applying the trained machine learning model to the raw data to generate output data that indicates a conflict between the network functions and a recommended action to address the conflict. The conflict may be a potential conflict or an existing conflict. The conflict may correspond to one or more parameters of the network (e.g., of one or more cells) that may be or have been adjusted in different ways by different network functions. In some embodiments, the recommended action may include one or more of: modify one or more policies and/or targets for the network function(s); change a priority for the network function(s); set or change a range of values of one or more parameters that the network function(s) are allowed to change; update a value of one or more parameters; and/or temporarily switch off one or more network function(s). 
       FIG.  6    illustrates another process  600  in accordance with various embodiments. In some embodiments, the process  600  may be performed by a MDA consumer of a wireless communication network. 
     For example, the process  600  may include, at  602 , providing training data associated with one or more network functions to a MDA producer to train a machine learning model. The training data may be associated with a managed network or service. For example, the training data may be associated with one or more network functions, such as one or more SON functions and/or other network functions. 
     At  604 , the process  600  may further include providing raw data associated with the network functions to the MDA producer for analysis by the trained machine learning model. In some embodiments, some or all of the raw data may be provided by other sources than the MDA consumer. The raw data may include, for example, one or more of: historical changes made by one or more network functions; one or more most recent changes made by one or more network functions; one or more current network configurations; historical and/or current network performance data related to one or more network functions; and/or one or more policies and/or targets for the network functions. The network functions may include, for example, one or more SON functions, such as a MRO function and/or a MLB function. 
     At  606 , the process  600  may further include receiving output data from the MDA producer that indicates a conflict between the network functions and a recommended action to address the conflict. The conflict may be a potential conflict or an existing conflict. The conflict may correspond to one or more parameters of the network (e.g., of one or more cells) that may be or have been adjusted in different ways by different network functions. In some embodiments, the recommended action may include one or more of: modify one or more policies and/or targets for the network function(s); change a priority for the network function(s); set or change a range of values of one or more parameters that the network function(s) are allowed to change; update a value of one or more parameters; and/or temporarily switch off one or more network function(s). 
     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. 
     EXAMPLES 
     Example 1 may include one or more non-transitory, computer-readable media (NTCRM) having instructions, stored thereon, that when executed by one or more processors cause a management data analytics (MDA) service producer to: receive training data from a MDA consumer, the training data including a training input and a corresponding desired output; train a machine learning model based on the training data; receive raw data associated with one or more managed networks or services; apply the trained machine learning model to the raw data to generate analytics output data; and send the analytics output data to a MDA consumer. 
     Example 2 may include the one or more NTCRM of Example 1, wherein the raw data is associated with network functions, and the analytics output data indicates a conflict between the network functions and one or more recommended actions to address the conflict. 
     Example 3 may include the one or more NTCRM of Example 2, wherein the received raw data includes one or more of: historical changes made by one or more of the network functions; one or more most recent changes made by one or more of the network functions; one or more current network configurations; historical network performance data related to one or more of the network functions; current network performance data related to one or more of the network functions; or one or more policies or targets for one or more of the network functions. 
     Example 4 may include the one or more NTCRM of Example 3, wherein the raw data includes the historical or current network performance data, and wherein the historical or current network performance data includes one or more of: load information of one or more cells or handover performance measurements. 
     Example 5 may include the one or more NTCRM of Example 4, wherein the historical or current network performance data includes the handover performance measurements, and wherein the handover performance measurements include information associated with handovers that were determined to be too early or too late. 
     Example 6 may include the one or more NTCRM of Example 2, wherein the recommended action includes at least one of: modify one or more policies or targets for one or more of the network functions; change a priority for one or more of the network functions; set or change a range of values of one or more parameters that one or more of the network functions are allowed to change; update a value of one or more parameters; or temporarily switch off one or more of the network functions. 
     Example 7 may include the one or more NTCRM of Example 2, wherein the conflict is a potential conflict or an existing conflict. 
     Example 8 may include the one or more NTCRM of Example 2, wherein the network functions include a mobility robustness optimization (MRO) function and a mobility load balancing (MLB) function of a self-organizing network (SON). 
     Example 9 may include the one or more NTCRM of Example 1, wherein the instructions, when executed, are further to cause the MDA service producer to: 
     receive validation data from the MDA consumer based on the analytics output data; and 
     further train the machine learning model based on the validation data. 
     Example 10 may include the one or more NTCRM of Example 1, wherein the instructions, when executed, are further to cause the MDA service producer to classify the training data and the raw data prior to providing the respective training data and raw data to the machine learning model. 
     Example 11 may include one or more non-transitory, computer-readable media (NTCRM) having instructions, stored thereon, that when executed by one or more processors cause a management data analytics (MDA) consumer to: provide training data related to network functions associated with a network or service to a MDA producer to train a machine learning model; receive analytics output data from the trained machine learning model that indicates a recommended action to address a conflict between the network functions; and perform the recommended action. 
     Example 12 may include the one or more NTCRM of Example 11, wherein the analytics output data is based on raw data that includes one or more of: historical changes made by one or more of the network functions; one or more most recent changes made by one or more of the network functions; one or more current network configurations; historical network performance data related to one or more of the network functions; current network performance data related to one or more of the network functions; or one or more policies or targets for one or more of the network functions. 
     Example 13 may include the one or more NTCRM of Example 12, wherein the raw data includes the historical or current network performance data, and wherein the historical or current network performance data includes one or more of: load information of one or more cells or handover performance measurements associated with one or more handovers that were determined to be too early or too late. 
     Example 14 may include the one or more NTCRM of Example 12, wherein the instructions, when executed, are further to cause the MDA consumer to provide at least some of the raw data to the MDA producer. 
     Example 15 may include the one or more NTCRM of Example 11, wherein the recommended action includes at least one of: modify one or more policies or targets for one or more of the network functions; change a priority for one or more of the network functions; set or change a range of values of one or more parameters that one or more of the network functions are allowed to change; update a value of one or more parameters; or temporarily switch off one or more of the network functions. 
     Example 16 may include the one or more NTCRM of Example 11, wherein the instructions, when executed, are further to cause the MDA consumer to: validate the analytics output data to generate validation data; and provide the validation data to the MDA producer to further train the machine learning model. 
     Example 17 may include the one or more NTCRM of Example 11, wherein the conflict is a potential conflict or an existing conflict. 
     Example 18 may include the one or more NTCRM of Example 11, wherein the network functions include a mobility robustness optimization (MRO) function and a mobility load balancing (MLB) function of a self-organizing network (SON). 
     Example 19 may include an apparatus to implement a management data analytics (MDA) service producer, the apparatus comprising processing circuitry to: receive training data from a MDA consumer associated with a managed network or service, wherein the training data includes a training input and a corresponding desired output; provide the training data to a machine learning model to train the machine learning model; receive raw data associated with network functions; and provide the raw data to the trained machine learning model to generate output data that indicates a conflict associated with the network functions and a recommended action to address the conflict. The apparatus of Example 19 may further include a memory to store the raw data and the analytics output data. 
     Example 20 may include the apparatus of Example 19, wherein the received raw data includes one or more of: historical changes made by one or more of the network functions; one or more most recent changes made by one or more of the network functions; one or more current network configurations; historical or current network performance data related to one or more of the network functions, wherein the historical or current performance data includes at least one of load information of one or more cells or handover performance information associated with handovers that were determined to be too early or too late; or one or more policies or targets for one or more of the network functions. 
     Example 21 may include the apparatus of Example 19, wherein the recommended action includes at least one of: modify one or more policies or targets for one or more of the network functions; change a priority for one or more of the network functions; set or change a range of values of one or more parameters that one or more of the network functions are allowed to change; update a value of one or more parameters of one or more cells; or temporarily switch off one or more of the network functions. 
     Example 22 may include the apparatus of Example 19, wherein the processing circuitry is further to: receive validation data from the MDA consumer based on the analytics output data; and provide the validation data to the machine learning model to further train the machine learning model. 
     Example 23 may include the apparatus of Example 19, wherein the processing circuitry is further to classify the training data and the raw data prior to providing the respective training data and raw data to the machine learning model. 
     Example 24 may include the apparatus of Example 19, wherein the conflict is a potential conflict or an existing conflict. 
     Example 25 may include the apparatus of Example 19, wherein the network functions are self-organizing network (SON) functions. 
     Example 26 may include an apparatus comprising means to perform one or more elements of a method described in or related to any of examples 1-25, or any other method or process described herein. 
     Example 27 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 1-25, or any other method or process described herein. 
     Example 28 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 1-25, or any other method or process described herein. 
     Example 29 may include a method, technique, or process as described in or related to any of examples 1-25, or portions or parts thereof. 
     Example 30 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 1-25, or portions thereof. 
     Example 31 may include a signal as described in or related to any of examples 1-25, or portions or parts thereof. 
     Example 32 may include a datagram, packet, frame, segment, protocol data unit (PDU), or message as described in or related to any of examples 1-25, or portions or parts thereof, or otherwise described in the present disclosure. 
     Example 33 may include a signal encoded with data as described in or related to any of examples 1-25, or portions or parts thereof, or otherwise described in the present disclosure. 
     Example 34 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 1-25, or portions or parts thereof, or otherwise described in the present disclosure. 
     Example 35 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 1-25, or portions thereof. 
     Example 36 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 1-25, or portions thereof. 
     Example 37 may include a signal in a wireless network as shown and described herein. 
     Example 38 may include a method of communicating in a wireless network as shown and described herein. 
     Example 39 may include a system for providing wireless communication as shown and described herein. 
     Example 40 may include a device for providing wireless communication as shown and 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. 
     Terminology 
     Unless used differently herein, terms, definitions, and abbreviations may be consistent with terms, definitions, and abbreviations defined in 3GPP TR 21.905 v16.0.0 (2019-06). For the purposes of the present document, the following terms and definitions are applicable to the examples and embodiments discussed herein. 
     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 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 “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 “resource” as used herein refers to a physical or virtual device, a physical or virtual component within a computing environment, and/or a physical or virtual component within a particular device, such as computer devices, mechanical devices, memory space, processor/CPU time, processor/CPU usage, processor and accelerator loads, hardware time or usage, electrical power, input/output operations, ports or network sockets, channel/link allocation, throughput, memory usage, storage, network, database and applications, workload units, 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. 
     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. 
     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 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 link, and/or the like. 
     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. 
     The term “SMTC” refers to an SSB-based measurement timing configuration configured by SSB-MeasurementTimingConfiguration. 
     The term “SSB” refers to an SS/PBCH block. 
     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.