Managing data collection for machine learning (ML) models used in a communication network

Abstract

Embodiments include methods performed by a network node or function (NNF) configured to facilitate data collection in a communication network Such methods include receiving, from a data collection server, a request to initiate data collection by a user equipment (UE) in the communication network. Such methods include determining a configuration for the data collection by the UE. The configuration indicates user plane transport of the collected data to the NNF. Such methods include sending the determined configuration to the UE. Other embodiments include complementary methods for a data collection server, as well as network equipment arranged to implement NNFs and data collection servers configured to perform such methods.

Description

[0001] MANAGING DATA COLLECTION FOR MACHINE LEARNING (ML) MODELS USED IN A COMMUNICATION NETWORK

[0002] TECHNICAL FIELD

[0003] The present disclosure relates generally to communication networks, and more specifically to managing the collection of data ingested by machine learning (ML) models used in conjunction with operation of a communication network.

[0004] BACKGROUND

[0005] Currently the fifth generation (5G) of cellular systems, also referred to as New Radio (NR), is being standardized within the Third-Generation Partnership Project (3GPP). 5G / NR is developed for maximum flexibility to support multiple and substantially different use cases. These include enhanced mobile broadband (eMBB), machine type communications (MTC), ultra-reliable low latency communications (URLLC), side-link device -to-device (D2D), and several other use cases.

[0006] Figure 1 shows a high-level view of an exemplary 5G network architecture, including an NG-RAN (199) and a 5GC (198). The NG-RAN can include gNBs (e.g., 110a, b) and ng-eNBs (e.g. , 120a, b) that are interconnected via respective Xn interfaces. The gNBs and ng-eNBs are also connected via NG interfaces to the 5GC, more specifically to the Access and Mobility Management Functions (AMFs, e.g., 130a, b) via respective NG-C interfaces and to the User Plane Functions (UPFs, e.g, 140a, b) via respective NG-U interfaces. Moreover, the AMFs can communicate with one or more policy control functions (PCFs, e.g., 150a, b) and network exposure functions (NEFs, e.g., 160a, b).

[0007] Each of the gNBs can support the NR radio interface including frequency division duplexing (FDD), time division duplexing (TDD), or a combination thereof. Each of ng-eNBs can support the LTE radio interface. Unlike conventional LTE eNBs, however, ng-eNBs connect to the 5GC via the NG interface. Each of the gNBs and ng-eNBs can serve a geographic coverage area including one more cells (e.g., 21 la-b, 221a-b). Depending on the cell in which it is located, a UE (205) can communicate with the gNB or ng-eNB serving that cell via the NR or LTE radio interface, respectively. Although Figure 2 shows gNBs and ng-eNBs separately, it is also possible that a single NG-RAN node provides both LTE and NR radio interfaces.

[0008] NG RAN logical nodes (e.g., gNBs) include a central unit (CU) and one or more distributed units (DUs). CUs are logical nodes that host higher-layer protocols and perform various gNB functions such controlling the operation of DUs. DUs are decentralized logical nodes that host lower layer protocols and can include, depending on the functional split option, various subsets of the gNB functions. Each CU and DU can include various circuitry needed to perform their respective functions, including processing circuitry, communication interface circuitry (e.g., transceivers), and power supply circuitry. A CU connects to one or more DUs over respective Fl logical interfaces. However, a DU can be connected to only a single CU. A CU and its connected DU(s) are only visible to other gNBs and the 5GC as a gNB. In other words, the Fl interface is not visible beyond gNB-CU.

[0009] Another change in 5G networks (e.g., in 5GC) is that traditional peer-to-peer interfaces and protocols found in earlier-generation networks are modified and / or replaced by a Service Based Architecture (SBA) in which Network Functions (NFs) provide one or more services to one or more service consumers. The services are composed of various “service operations”, which are more granular divisions of the overall service functionality.

[0010] A 5GC NF of interest in the present disclosure is the Network Data Analytics Function (NWDAF). This NF provides network analytics information (e.g., statistical information of past events and / or predictive information) to other NFs on a network slice instance level. The NWDAF can collect data from any 5GC NF. Note that a “network slice” is a logical partition of a 5G network that provides specific network capabilities and characteristics, e.g., in support of a particular service. A network slice instance is a set of NF instances and the required network resources (e.g., compute, storage, communication) that provide the capabilities and characteristics of the network slice.

[0011] Machine learning (MU) is a type of artificial intelligence (Al) that focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving accuracy as more data becomes available. MU algorithms build models based on sample (or “training”) data, with the models being used subsequently to make predictions or decisions. AI / MU algorithms (or models) can be used in a wide variety of applications (e.g., medicine, email filtering, speech recognition, etc.) in which it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks. A subset of MU is closely related to computational statistics.

[0012] 3GPP TS 23.288 (vl8.5.0) specifies that NWDAF is the main NF for computing analytics based on MU models, and classifies NWDAF into two sub-functions (or logical functions): Analytics Uogical Function (AnUF), which performs analytics procedures; and Model Training Uogical Function (MTUF), which performs training and retraining of MU models used by the AnUF.

[0013] 3GPP standards provide various ways for positioning (e.g., determining the position of, locating, and / or determining the location of) UEs operating in NR networks. In general, a positioning node configures a target device (e.g., UE) and / or radio network nodes (RNN, e.g., gNB, ng-eNB, or RNN dedicated for positioning measurements) to perform one or more positioning measurements according to one or more positioning methods. For example, the positioning measurements can include timing (and / or timing difference) measurements on UE, network, and / or satellite transmissions. The positioning measurements are used by the target device, the measuring node, and / or the positioning node to determine the target device’s location.

[0014] SUMMARY

[0015] AI / ML models may also be used for UE positioning. For example, a UE or RAN node (e.g., gNB) may use an AI / ML model to estimate measurements needed to determine the UE’s location and / or to directly determine the UE’s location based on measurements performed by the UE or gNB on reference signals transmitted by the other. The AI / ML model may be previously trained or may be trained on-the-fly.

[0016] AI / ML models used for UE positioning may reside in the UE, in a RAN node (e.g., gNB), in 5GC NFs (e.g., NWDAF), and in network operations / administration / maintenance (OAM) function. However, each of these locations have different data collection requirements and solutions. This is overly complex. Furthermore, when the positioning AI / ML model resides in the UE, it is unclear what other network entities should have visibility into the UE AI / ML model parameters and collected data.

[0017] An object of embodiments of the present disclosure is to improve data collection for AI / ML models used for operation of communication networks, such as by providing, enabling, and / or facilitating solutions to overcome exemplary problems summarized above and described in more detail below.

[0018] Some embodiments include methods (e.g., procedures) for a network node or function (NNF) configured to facilitate data collection in a communication network.

[0019] These exemplary methods include receiving, from a data collection server, a request to initiate data collection by a UE in the communication network. These exemplary methods also include determining a configuration for the data collection by the UE. In particular, the configuration indicates user plane transport of the collected data to the NNF. These exemplary methods also include sending the determined configuration to the UE.

[0020] In some embodiments, the configuration also includes or indicates one or more of the following:

[0021] • duration of the data collection;

[0022] • minimization of drive testing (MDT) configuration to be used for the data collection;

[0023] • positioning methods to be used for the data collection;

[0024] • measurements to be collected;

[0025] • assistance data for the data collection; and

[0026] • one or more data collection criteria. In some of these embodiments, the one or more data collection criteria include one or more of the following: signal strength threshold, geometric dilution of precision (GDOP) threshold, user equipment (UE) speed threshold, UE environment type, and UE line-of-sight (LOS) or non-LOS signal conditions..

[0027] In some embodiments, the configuration includes or indicates one or more of the following associated with the user plane transport:

[0028] • whether encryption and / or integrity protection should be used;

[0029] • whether the collected data in a report should be comprehensible by intermediate network entities through which the report is sent;

[0030] • quality-of-service requirements for any flow or radio bearer used to report the collected data;

[0031] • a particular flow or radio bearer on which the collected data should be reported; and

[0032] • an Internet Protocol (IP) address of the data collection server.

[0033] In some embodiments, these exemplary methods also include receiving from a UE a first indication of the UE’s capability for data collection. In such case, the configuration is determined based on the first indication. In some embodiments, these exemplary methods also include obtaining, from a unified data management function (UDMO of the communication network, a second indication of data collection consent by a user associated with the UE. In such case, the configuration is determined based on the second indication.

[0034] In some embodiments, these exemplary methods also include receiving from the UE a report that includes data collected according to the configuration sent to the UE, and sending at least a portion of the collected data in the report to the data collection server.

[0035] In some variants of these embodiments, these exemplary methods also include determining a first portion of the collected data in the report to retain and a second portion of the collected data in the report to send to the recipient. In some further variants, determining the first and second portions is further based on whether the collected data in the report is comprehensible by (or transparent / non-transparent to) the NNF.

[0036] In some variants of these embodiments, these exemplary methods also include receiving from the data collection server a further request to provide data collected in accordance with the request. The at least a portion of the collected data from the report is sent in response to the further request.

[0037] In some embodiments, these exemplary methods also include receiving from the data collection server a second request to stop or pause the data collection by the UE, and sending a stop or pause commands to the UE in accordance with the second request. Other embodiments include methods (e.g., procedures) for a data collection server for a communication network.

[0038] These exemplary methods include sending, to an NNF of the communication network, a request to initiate data collection by a UE in the communication network. These exemplary methods also include receiving from the NNF a report that includes data collected by the UE in accordance with the request.

[0039] In some embodiments, these exemplary methods also include sending to the NNF a further request to provide data collected in accordance with the request. The report is received in response to the further request.

[0040] In some embodiments, these exemplary methods also include sending to the NNF a second request to stop or pause the data collection by the UE.

[0041] The following summary applies to all of the embodiments summarized above.

[0042] In some embodiments, the data collection is associated with machine learning (ML) models hosted by one or more of the following: the NNF, the UE, and the data collection server. In some embodiments, the request includes or identifies one or more of the following:

[0043] • the UE;

[0044] • geographic area where the data needs to be collected;

[0045] • duration of the data collection;

[0046] • positioning methods to be used for the data collection;

[0047] • measurements to be collected;

[0048] • security and / or privacy requirements for the data collection; and

[0049] • a recipient for the collected data.

[0050] In some embodiments, the request includes an identifier (ID) associated with the data collection, the ID is included in or with the configuration sent to the UE, and the ID is included in or with the report received from the UE and / or in or with the report sent by the NNF to the data collection server.

[0051] In some embodiments, the data collection server is one of the following: a further NNF of the communication network, or an application function (AF) or server external to the communication network. In some embodiments, the NNF is one of the following: a location management function (LMF), a network data analytics function (NWDAF), or an operations / administration / maintenance (OAM) function.

[0052] Other embodiments and variants of the exemplary methods summarized above are described herein. Other embodiments include NNFs (e.g., NWDAFs, LMFs, OAM functions) and data collection servers or network equipment that implement these, which are configured to perform operations corresponding to any of the exemplary methods described herein. Other embodiments include non-transitory, computer-readable media storing program instructions that, when executed by processing circuitry, configure such NNFs or data collection servers to perform operations corresponding to any of the exemplary methods described herein.

[0053] These and other embodiments described herein may provide various benefits and / or advantages. For example, embodiments may provide a unified framework for data collection for both UE-side and network -side AI / ML models. As another example, embodiments may facilitate compliance with MNO requirements for data collection, such as for data security. As another example, embodiments may facilitate distinguishing between multiple ongoing data collection sessions, thereby improving throughput of data collection in mobile networks.

[0054] These and other objects, features, and advantages of embodiments of the present disclosure will become apparent upon reading the following Detailed Description in view of the Drawings briefly described below.

[0055] BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figures 1-2 illustrate various aspects of an exemplary 5G network architecture.

[0057] Figure 3 shows an exemplary configuration of NR user plane (UP) and control plane (CP) protocol stacks.

[0058] Figure 4 shows a high-level architecture for UE positioning in NR networks.

[0059] Figure 5 shows an example AI / ML model training pipeline.

[0060] Figures 6-7 show signaling diagrams of example procedures that illustrate various embodiments of the present disclosure.

[0061] Figure 8 shows a flow diagram of an exemplary method (e.g., procedure) for a network node or function (NNF), according to various exemplary embodiments of the present disclosure.

[0062] Figure 9 shows a flow diagram of an exemplary method (e.g., procedure) for a data collection server, according to various embodiments of the present disclosure.

[0063] Figure 10 shows a flow diagram of another exemplary method (e.g., procedure) for an NNF, according to various exemplary embodiments of the present disclosure.

[0064] Figure 11 shows a flow diagram of another exemplary method (e.g., procedure) for a data collection server, according to various embodiments of the present disclosure.

[0065] Figure 12 shows a communication system according to various embodiments of the present disclosure.

[0066] Figure 13 shows a host system according to various embodiments of the present disclosure.

[0067] Figure 14 shows a network node according to various embodiments of the present disclosure. Figure 15 is a block diagram of a virtualization environment in which functions implemented by some embodiments of the present disclosure may be virtualized.

[0068] DETAILED DESCRIPTION

[0069] Some of the embodiments contemplated herein will now be described more fully with reference to the accompanying drawings. Other embodiments, however, are contained within the scope of the subject matter disclosed herein, the disclosed subject matter should not be construed as limited to only the embodiments set forth herein; rather, these embodiments are provided as examples to convey the scope of the subject matter to those skilled in the art.

[0070] In general, all terms used herein are to be interpreted according to their ordinary meaning to a person of ordinary skill in the relevant technical field, unless a different meaning is expressly defined and / or implied from the context of use. All references to a / an / the element, apparatus, component, means, step, etc. are to be interpreted openly as referring to at least one instance of the element, apparatus, component, means, step, etc. , unless explicitly stated otherwise or clearly implied from the context of use. The operations of any methods and / or procedures disclosed herein do not have to be performed in the exact order disclosed, unless an operation is explicitly described as following or preceding another operation and / or where it is implicit that an operation must follow or precede another operation. Any feature of any embodiment disclosed herein can apply to any other disclosed embodiment, as appropriate. Likewise, any advantage of any embodiment described herein can apply to any other disclosed embodiment, as appropriate.

[0071] Furthermore, the following terms are used throughout the description given below:

[0072] • Radio Access Node: As used herein, a “radio access node” (or equivalently “radio network node,” “radio access network node,” or “RAN node”) can be any node in a radio access network (RAN) that operates to wirelessly transmit and / or receive signals. Some examples of a radio access node include, but are not limited to, a base station (e.g, gNB in a 3GPP 5G / NR network or an enhanced or eNB in a 3GPP LTE network), base station distributed components (e.g, CU and DU), a high-power or macro base station, a low-power base station (e.g., micro, pico, femto, or home base station, or the like), an integrated access backhaul (IAB) node, a transmission point (TP), a transmission reception point (TRP), a remote radio unit (RRU or RRH), and a relay node.

[0073] • Core Network Node: As used herein, a “core network node” is any type of node in a core network. Some examples of a core network node include, e.g., a Mobility Management Entity (MME), a serving gateway (SGW), a PDN Gateway (P-GW), a Policy and Charging Rules Function (PCRF), an access and mobility management function (AMF), a session management function (SMF), a user plane function (UPF), a Charging Function (CHF), a Policy Control Function (PCF), an Authentication Server Function (AUSF), a location management function (LMF), or the like.

[0074] • Wireless Device: As used herein, a “wireless device” (or “WD” for short) is any type of device that is capable, configured, arranged and / or operable to communicate wirelessly with network nodes and / or other wireless devices. Communicating wirelessly can involve transmitting and / or receiving wireless signals using electromagnetic waves, radio waves, infrared waves, and / or other types of signals suitable for conveying information through air. Unless otherwise noted, the term “wireless device” is used interchangeably herein with the term “user equipment” (or “UE” for short), with both of these terms having a different meaning than the term “network node”.

[0075] • Radio Node: As used herein, a “radio node” can be either a “radio access node” (or equivalent term) or a “wireless device.”

[0076] • Network Node: As used herein, a “network node” is any node that is either part of the radio access network (e.g., a radio access node or equivalent term) or of the core network (e.g, a core network node discussed above) of a cellular communications network. Functionally, a network node is equipment capable, configured, arranged, and / or operable to communicate directly or indirectly with a wireless device and / or with other network nodes or equipment in the cellular communications network, to enable and / or provide wireless access to the wireless device, and / or to perform other functions (e.g., administration) in the cellular communications network.

[0077] • Node: As used herein, the term “node” (without prefix) can be any type of node that can in or with a wireless network (including RAN and / or core network), including a radio access node (or equivalent term), core network node, or wireless device. However, the term “node” may be limited to a particular type (e.g., radio access node, IAB node) based on its specific characteristics in any given context.

[0078] The above definitions are not meant to be exclusive. In other words, various ones of the above terms may be explained and / or described elsewhere in the present disclosure using the same or similar terminology. Nevertheless, to the extent that such other explanations and / or descriptions conflict with the above definitions, the above definitions should control.

[0079] Note that the description given herein focuses on a 3GPP cellular communications system and, as such, 3GPP terminology or terminology similar to 3GPP terminology is oftentimes used. However, the concepts disclosed herein are not limited to a 3GPP system and can be applied to any communication system that may benefit from them.

[0080] Figure 2 shows an exemplary architecture for a 5G network (200). This exemplary architecture include the following 3GPP -defined NFs and service-based interfaces: • Application Function (AF, 250, with Naf interface) interacts with the 5GC to provision information to the network operator and to subscribe to certain events happening in operator's network. An AF offers applications for which service is delivered in a different layer (i.e., transport layer) than the one in which the service has been requested (i.e., signaling layer), the control of flow resources according to what has been negotiated with the network. An AF communicates dynamic session information to PCF (via N5 interface), including description of media to be delivered by transport layer.

[0081] • Policy Control Function (PCF, with Npcf interface) - supports unified policy framework to govern the network behavior, via providing PCC rules (e.g., on the treatment of each service data flow that is under PCC control) to the SMF via the N7 reference point. PCF provides policy control decisions and flow based charging control, including service data flow detection, gating, QoS, and flow-based charging (except credit management) towards the SMF. The PCF receives session and media related information from the AF and informs the AF of traffic (or user) plane events.

[0082] User Plane Function (UPF) - handles user plane traffic based on the rules received from SMF, including packet inspection and different enforcement actions (e.g., event detection and reporting). UPFs communicate with the RAN (230, e.g., NG-RAN) via the N3 reference point, with SMFs (discussed below) via the N4 reference point, and with an external packet data network (PDN) via the N6 reference point. The N9 reference point is for communication between two UPFs.

[0083] • Session Management Function (SMF, with Nsmf interface) - interacts with decoupled user plane, including creating, updating, and removing Protocol Data Unit (PDU) sessions and managing session context with UPF, e.g., for event reporting. For example, SMF performs data flow detection (based on filter definitions included in PCC rules), online and offline charging interactions, and policy enforcement.

[0084] • Charging Function (CHF, with Nchf interface) - responsible for converged online charging and offline charging functionalities. It provides quota management (for online charging), re-authorization triggers, rating conditions, etc. and is notified about usage reports from the SMF. Quota management involves granting a specific number of units (e.g., bytes, seconds) for a service. CHF also interacts with billing systems.

[0085] Access and Mobility Management Function (AMF, with Namf interface) - terminates the RAN CP interface and handles all mobility and connection management of UEs (240), similar to MME in 4G Evolved Packet Core (EPC) network. AMFs communicate with UEs via the N 1 reference point and with the RAN via the N2 reference point. • Network Exposure Function (NEF, with Nnef interface) acts as the entry point into operator's network, by securely exposing to AFs the network capabilities and events provided by 3GPP NFs and by providing ways for the AF to securely provide information to 3GPP network. For example, NEF provides a service that allows an AF to provision specific subscription data (e.g., expected UE behavior) for various UEs.

[0086] • Network Repository Function (NRF, with Nnrf interface) provides service registration and discovery, enabling NFs to identify appropriate services available from other NFs.

[0087] • Network Slice Selection Function (NSSF, with Nnssf interface) - manages logical partition of the 5G network into “network slices” that provides specific network capabilities and characteristics, e.g., in support of a particular service. A network slice instance is a set of NF instances and the required network resources (e.g., compute, storage, communication) that provide the capabilities and characteristics of the network slice. The NSSF enables other NFs (e.g., AMF) to identify a network slice instance that is appropriate for a UE’s desired service.

[0088] • Authentication Server Function (AUSF, with Nausf interface) - performs user authentication and computes security key materials for various purposes, and is based in a user’s home network (HPLMN).

[0089] • Network Data Analytics Function (NWDAF, 210, with Nnwdaf interface) - provides network analytics information (e.g., statistical information of past events and / or predictive information) to other NFs on a network slice instance level.

[0090] • Location Management Function (LMF, 220, with Nlmf interface) - supports various functions related to determination of UE locations, including location determination for a UE and obtaining any of the following: DL location measurements or a location estimate from the UE; UL location measurements from the NG RAN; and non-UE associated assistance data from the NG RAN.

[0091] • Unified Data Management function (UDM, with Nudm interface) - supports generation of 3GPP authentication credentials, user identification handling, access authorization based on subscription data, and other subscriber-related functions. The UDM provides this functionality based on subscription data (including authentication data) stored in the 5GC unified data repository (UDR), which also supports storage / retrieval of policy data by the PCF and storage / retrieval of application data by NEF.

[0092] The services provided by the various NFs are composed of “service operations”, which are more granular divisions of the overall service functionality. The interactions between service consumers and producers can be of the type “request / response” or “subscribe / notify”. In the latter type, a service consumer NF (or equivalently, “consumer NF”) requests a service producer NF (or equivalently, “producer NF”) to establish a subscription for the service consumer NF to receive notifications from the service producer NF under conditions specified in this subscription.

[0093] Figure 3 shows an exemplary configuration of NR UP and CP protocol layers between a UE (310), a gNB (320), and an AMF (330), such as those shown in Figures 1-2. Physical (PHY), Medium Access Control (MAC), Radio Link Control (RLC), and Packet Data Convergence Protocol (PDCP) layers between UE and gNB are common to UP and CP. PDCP provides ciphering / deciphering, integrity protection, sequence numbering, reordering, and duplicate detection for both CP and UP, as well as header compression and retransmission for UP data.

[0094] On the UP side, Internet protocol (IP) packets arrive to PDCP as service data units (SDUs), and PDCP creates protocol data units (PDUs) to deliver to RLC. The Service Data Adaptation Protocol (SDAP) layer handles quality-of-service (QoS) including mapping between QoS flows and Data Radio Bearers (DRBs) and marking QoS flow identifiers (QFI) in UL and DL packets. RLC transfers PDCP PDUs to MAC through logical channels (LCH). RLC provides error detection / correction, concatenation, segmentation / reassembly, sequence numbering, reordering of data transferred to / from the upper layers. MAC provides mapping between LCHs and PHY transport channels, LCH prioritization, multiplexing into or demultiplexing from transport blocks (TBs), hybrid ARQ (HARQ) error correction, and dynamic scheduling (in gNB). PHY provides transport channel services to MAC and handles transfer over the NR radio interface, e.g., via modulation, coding, antenna mapping, and beam forming.

[0095] On the CP side, the non-access stratum (NAS) layer between UE and AMF handles UE / gNB authentication, mobility management, and security control. RRC sits below NAS in the UE but terminates in the gNB rather than the AMF. RRC controls communications between UE and gNB at the radio interface as well as the mobility of a UE between cells in the NG-RAN. RRC also broadcasts system information (SI) and performs establishment, configuration, maintenance, and release of DRBs and Signaling Radio Bearers (SRBs) and used by UEs. Additionally, RRC controls addition, modification, and release of carrier aggregation (CA) and dual-connectivity (DC) configurations for UEs, and performs various security functions such as key management.

[0096] After a UE is powered ON it will be in the RRC_IDLE state until an RRC connection is established with the network, at which time the UE will transition to RRC_CONNECTED state (e.g., where data transfer can occur). The UE returns to RRC IDLE after the connection with the network is released. In RRC IDLE state, the UE's radio is active on a discontinuous reception (DRX) schedule configured by upper layers. During DRX active periods (also referred to as “DRX On durations”), an RRC IDLE UE receives SI broadcast in the cell where the UE is camping, performs measurements of neighbor cells to support ceil reselection, and monitors a paging channel on PDCCH for pages from 5GC via gNB, An NR UE in RRC_IDLE state is not known to the gNB serving the cell where the UE is camping. However, NR RRC includes an RRC_INACTIVE state in which a UE is known (e.g., via UE context) by the serving gNB. RRC INACTIVE has some properties like a “suspended” condition used in UTE.

[0097] UEs and RAN nodes (e.g., gNBs) can be configured to perform and report measurements to support minimization of drive testing (MDT), which is intended to reduce and / or minimize the requirements for manual testing of network performance by driving around the geographic coverage of the network. The MDT feature was first studied in Eong-Term Evolution (LTE) Rel- 9 (e.g., 3GPP TR 36.805 v9.0.0), first standardized for LTE in Rel-10, and is also supported for 5G / NR. MDT can address various network performance improvements such as coverage optimization, capacity optimization, mobility optimization, quality-of-service (QoS) verification, and parameterization for common channels (e.g., PDSCH). In general, MDT measurements can be management -based or signaling-based. MDT measurements performed by the UE while in RRC CONNECTED state are reported directly (in “immediate MDT reports”) while MDT measurements performed by the UE while in RRC IDLE / RRC INACTIVE are logged and reported when later connected to the RAN (in “logged MDT reports”).

[0098] The MDT framework provides two ways of activating MDT and collecting data from the UEs. In management-based MDT, data is collected from UEs in a specified area defined as a list of cells or as a list of tracking / routing / location areas. Management-based MDT is an enhancement of management-based trace functionality and can be used with either logged or immediate MDT. In signaling-based MDT, data is collected from a specific UE as specified by an IMEI(SV) or an IMSI. Signaling -based MDT is an enhancement of signaling -based subscriber and equipment trace functionality and can be used with either logged or immediate MDT.

[0099] Figure 4 is a block diagram illustrating a high-level architecture for UE positioning in NR networks. NG-RAN (420) can include gNBs (e.g., 422) and ng-eNBs (e.g., 421), similar to the architecture shown in Figure 2. Each ng-eNB may control several transmission points (TPs), such as remote radio heads. Similarly, each gNB may control several TRPs. Some or all of the TPs / TRPs may be DL-PRS-only for support of PRS-based TBS.

[0100] In addition, the NG-RAN nodes communicate with an AMF (430) in the 5GC via respective NG-C interfaces (both of which may or may not be present), while the AMF communicates with a location management function (LMF, 440) via an NLs interface (441). An LMF supports various functions related to determination of UE locations, including location determination for a UE and obtaining DL location measurements or a location estimate from the UE, UL location measurements from the NG RAN, and non-UE associated assistance data from the NG RAN. In addition, positioning -related communication between a UE (410) and the NG-RAN nodes occurs via RRC, while positioning-related communication between NG-RAN nodes and LMF occurs via an NRPPa protocol. Optionally, the LMF can also communicate with an enhanced serving mobile location center (E-SMLC, 450) and a secure UP location (SUPL) location platform (SLP, 460) via respective communication interfaces (451, 461), which can utilize and / or be based on standardized protocols, proprietary protocols, or a combination thereof. The E-SMLC is responsible for UE positioning via LTE CP while the SLP is responsible for UE positioning via UP.

[0101] The LMF can also include, or be associated with, various processing circuitry (442), by which the LMF performs various operations described herein. The LMF’s processing circuitry can include similar types of processing circuitry as described herein in relation to other network nodes (see, e.g., descriptions of Figures 11-13). The LMF can also include, or be associated with, a non-transitory computer-readable medium (443) storing instructions (also referred to as a computer program program) that can facilitate the operations of the processing circuitry. The computer-readable medium can include similar types of computer memory as described herein in relation to other network nodes or functions (e.g., descriptions of Figures 14-15). Additionally, the LMF can include various communication interface circuitry (441, e.g., Ethernet, optical, and / or radio transceivers) that can be used, e.g., for communication via the NLs interface. For example, the LMF’s communication interface circuitry can be similar to other interface circuitry described herein in relation to network nodes or functions (e.g., descriptions of Figures 14-15).

[0102] Similarly, the E-SMLC can also include, or be associated with, various processing circuitry (452), by which the E-SMLC performs various operations described herein. The E- SMLC’s processing circuitry can include similar types of processing circuitry as described herein in relation to other network nodes (see, e.g., descriptions of Figures 11-13). The E-SMLC can also include, or be associated with, a non-transitory computer-readable medium (453) storing instructions (also referred to as a computer program program) that can facilitate the operations of the processing circuitry. The computer-readable medium can include similar types of computer memory as described herein in relation to network nodes or functions (e.g., descriptions of Figures 14-15). The E-SMLC can also include communication interface circuitry that is appropriate for communicating via an interface (451), which can be similar to other interface circuitry described herein in relation to network nodes or functions (e.g., descriptions of Figures 14-15).

[0103] Similarly, the SLP can also include, or be associated with, various processing circuitry (462), by which the SLP performs various operations described herein. The SLP’s processing circuitry can include similar types of processing circuitry as described herein in relation to other network nodes (see, e.g., descriptions of Figures 1 1-13). The SLP can also include, or be associated with, a non-transitory computer-readable medium (463) storing instructions (also referred to as a computer program program) that can facilitate the operations of the processing circuitry. The computer-readable medium can include similar types of computer memory as described herein in relation to other network nodes or functions (e.g., descriptions of Figures 14-15). The SLP can also include communication interface circuitry that is appropriate for communicating via an interface (461), which can be similar to other interface circuitry described herein in relation to other network nodes or functions (e.g., descriptions of Figures 14-15).

[0104] In typical operation, the AMF can receive a request for a location service associated with a particular target UE from another entity (e.g., a gateway mobile location center (GMLC)), or the AMF itself can initiate some location service on behalf of a particular target UE (e.g., for an emergency call from the UE). The AMF then sends a location services (LS) request to the LMF. The LMF processes the LS request, which may include transferring assistance data to the target UE to assist with UE-based and / or UE-assisted positioning; and / or positioning of the target UE. The LMF then returns the result of the LS (e.g., a position estimate for the UE and / or an indication of any assistance data transferred to the UE) to the AMF or to another entity (e.g., GMLC) that requested the LS.

[0105] Various interfaces and protocols are used for, or involved in, NR positioning. The LTE Positioning Protocol (LPP) is used between a target device (e.g., UE in the control-plane, or SET in the user-plane) and a positioning server (e.g., LMF in the control-plane, SLP in the user-plane). LPP can use either CP or UP protocols as underlying transport. NRPP is terminated between a target device and the LMF. RRC protocol is used between UE and gNB (via NR radio interface) and between UE and ng-eNB (via LTE radio interface).

[0106] Furthermore, the NR Positioning Protocol A (NRPPa) carries information between the NG-RAN Node and the LMF and is transparent to the AMF. As such, the AMF routes the NRPPa PDUs transparently (e.g., without knowledge of the involved NRPPa transaction) over NG-C interface based on a Routing ID corresponding to the involved LMF. More specifically, the AMF carries the NRPPa PDUs over NG-C interface either in UE associated mode or non-UE associated mode. The NGAP protocol between the AMF and an NG-RAN node (e.g., gNB or ng-eNB) is used as transport for LPP and NRPPa messages over the NG-C interface. NGAP is also used to instigate and terminate NG-RAN-related positioning procedures.

[0107] LPP / NRPP are used to deliver messages such as positioning capability request, positioning measurements request, and assistance data to the UE from a positioning node (e.g., LMF). LPP / NRPP are also used to deliver messages from the UE to the positioning node including, e.g., UE capability, UE measurements for UE-assisted positioning, UE request for additional assistance data, UE configuration parameter(s) to be used to create UE -specific assistance data, etc. NRPPa is used to deliver the information between ng-eNB / gNB and LMF in both directions. This can include LMF requesting some information from ng-eNB / gNB, and ng-eNB / gNB providing some information to LMF. For example, this can include information about PRS transmitted by ng- eNB / gNB that are to be used for positioning measurements by the UE.

[0108] The following positioning methods are supported in NR:

[0109] • Enhanced Cell ID (E-CID). Utilizes information to associate the UE with the geographical area of a serving cell, and then additional information to determine a finer granularity position. The following measurements are supported for E-CID: AoA (base station only), UE Rx-Tx time difference, timing advance (TA) types 1 and 2, reference signal received power (RSRP), and reference signal received quality (RSRQ).

[0110] • Assisted GNSS. The UE receives and measures Global Navigation Satellite System (GNSS) signals, supported by assistance information provided to the UE from E-SMLC.

[0111] • DL Time Difference of Arrival (DL-TDoA). The UE measures reference signal time differences (RSTD) between DL RS (e.g., PRS) transmitted by different RAN nodes.

[0112] UL Relative Time of Arrival (UL-RToA). The UE is requested to transmit a specific waveform that is detected by multiple location measurement units (LMUs, which may be standalone, co-located or integrated into an eNB) at known positions. These measurements are forwarded to the E-SMLC for multilateration.

[0113] • Multi-Round Trip Time (RTT): The UE computes UE Rx-Tx time difference and gNBs compute gNB Rx-Tx time difference. The results are combined to find the UE position based upon round trip time (RTT) calculation.

[0114] • DL angle of departure (DL-AoD): gNB or LMF calculates the UE angular position based upon UE DL RSRP measurement results (e.g., of PRS transmitted by RAN nodes).

[0115] • UL angle of arrival (UL-AoA): gNB calculates the UL AoA based upon measurements of a UE’s UL SRS transmissions.

[0116] In addition, one or more of the following positioning modes can be utilized in each of the positioning methods listed above:

[0117] • UE -Assisted: The UE performs measurements with or without assistance from the network and sends these measurements to the E-SMLC where the position calculation may take place.

[0118] • UE -Based: The UE performs measurements and calculates its own position with assistance from the network. • Standalone: The UE performs measurements and calculates its own position without network assistance.

[0119] In the NR positioning methods listed above, RTT uses bidirectional timing measurements including UE Rx-Tx time difference, gNB Rx-Tx time difference, time advance (TA), etc. In the NR positioning methods listed above, unidirectional timing measurements include RSTD performed by the UE, UL RToA performed by the gNB, etc. These are explained in more detail below.

[0120] • RSTD: measured by UE on the DL PRS signals transmitted by positioning node j and reference positioning node i. RSTD always involves two cells (also referred to as TRPs).

[0121] • UE Rx-Tx time difference: defined as TUE-RX -TUE-TX, where: o TUE-RX is UE receive timing of DL subframe # / , defined by the first path detected in time, measured on PRS received from the gNB. o TUE-TX is UE transmit timing of UL subframe #j closest in time to DL subframe #i.

[0122] • gNB Rx-Tx time difference: define as TgNB-Rx -TgNB-rx, where: o TgNB-Rx is gNB received timing of UL subframe #i containing SRS transmitted by / received from the UE, defined by the first path detected in time, is measured on SRS signals received from the UE. o TgNB-rx is gNB transmit timing of DL subframe #j closest in time to UL subframe #i.

[0123] • Timing advance (TADV), defined as TADV = (TSNB-RX - TSNB-TX), where: o TgNB-Rx is TRP received timing of UL subframe #i containing PRACH transmitted by / received from the UE, defined by the first path detected in time. o TgNB-rx is TRP transmit timing of DL subframe #j closest in time to UL subframe #i. o Detected PRACH is used to determine the start of a subframe containing that PRACH.

[0124] • UL-RToA: gNB / TRP reception timing of beginning of subframe i containing SRS transmitted by / received from the UE, relative to a configurable reference time.

[0125] In some cases, a UE may also perform positioning measurements on sidelink (SL) PRS transmitted by other UE(s). In this scenario, the UE performing the positioning measurement is called “target UE” and each UE transmitting the SL PRS is called “anchor UE” or “assisting UE.”

[0126] Positioning reference units (PRUs) may also be deployed in a 5G network. A PRU is a network node or device, at a known location, which can transmit uplink (UL) signals and perform positioning measurements. In this manner, PRUs can help identify positioning errors and facilitate compensation for these errors in positions determined for UEs that are proximate in the network. PRUs are also enablers for SL-based positioning. For example, a UE without line of sight (i.e., non-LOS) to a network node (e.g., gNB) may use a PRU as a positioning reference. 3 GPP TS 38.305 (vl8.1.0) describes PRUs in more detail. As briefly mentioned above, AI / ML algorithms (or models) can be used in a wide variety of applications (e.g., medicine, email filtering, speech recognition, etc.) in which it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks. A subset of ML is closely related to computational statistics. The process of development, deployment, and maintenance of an AI / ML model is often referred to as lifecycle management (LCM). Deployment includes both training and inference.

[0127] In general, AI / ML models must be trained in order to produce desired results when used for inference based on input data. In online training, the AI / ML model is trained in (near) realtime while being used for inference, with the arrival of new training samples or data. In offline training, the AI / ML model is trained based on collected samples or data (often referred to as “training data”) and then later used for inference.

[0128] AI / ML models can hosted by various entities in a communication network, and can be broadly categorized into the following cases:

[0129] 1. UE-side AI / ML model, with inference performed entirely at the UE.

[0130] 2. Network -side AI / ML model, with inference performed entirely at the network.

[0131] 3. One-sided AI / ML model, i.e., on UE-side or a network-side.

[0132] 4. Two-sided (AI / ML) model, i.e., a paired AI / ML model in which UE and network perform joint inference, such as first part of inference being performed by UE and remaining part of inference being performed by gNB.

[0133] Parameters of an AI / ML model can be transferred or delivered over the air interface from RAN node to UE or vice versa.

[0134] Figure 5 shows an example AI / ML model training pipeline. The pipeline starts with a data ingestion stage, which gathers unprocessed input data from data repositories. Next, the data preprocessing stage identifies high-quality input features for input to the model training stage, which identifies an optimal mapping of the model input features to a desired model output target based on a loss function. The evaluation stage evaluates model performance on both a functional level and a system level according to relevant requirements. Finally, the registration stage makes the ML AI / ML model usable (executable) based on compilation to a specific hardware platform, versioning, packaging, etc.

[0135] As briefly mentioned above, AI / ML models may also be used for UE positioning. For example, a UE or RAN node (e.g., gNB) may use an AI / ML model to estimate measurements needed to determine the UE’s location and / or to directly determine the UE’s location based on measurements performed by the UE or gNB on reference signals transmitted by the other. The AI / ML model may be previously trained or may be trained on-the-fly. As a more specific example, positioning measurements may be performed (or estimated) by a UE or a RAN node using an AI / ML model. After completion, the positioning measurements are then reported to the LMF, which determines the location of the UE. As another specific example, the UE use an AI / ML model to determine its own location based on its own positioning measurements.

[0136] To summarize, AI / ML models used for UE positioning may reside in the UE, in a RAN node (e.g., gNB), in 5GC NFs (e.g., NWDAF), and in network operations / administration / maintenance (OAM) function. However, each of these locations have different data collection requirements and solutions, which creates unnecessary complexity. Furthermore, when the positioning AI / ML model resides in the UE, it is unclear what other network entities should have visibility into the UE AI / ML model parameters and collected data.

[0137] Embodiments of the present disclosure may address these and other problems, issues, and / or difficulties with flexible and efficient techniques for data collection in which a network node or function (e.g., LMF, NWDF, OAM) determines a final recipient of data collected from a UE or a PRU and how the collected should be provided to the final recipient. For example, the data may be collected for the purpose of AI / ML model training, and may be triggered by a server within or outside of the network domain. If the server is outside of the MNO domain, NEF may also be involved in the data collection. In some embodiments, the data collection may also be modelled by an SBA application programming interface (API) in which the server acts as an AF and performs a service request for data collection to NEF, which verifies the request before forwarding to the relevant network node or function (e.g., LMF, GMLC, OAM) for data collection.

[0138] Figure 6 shows a signaling diagram of an example procedure that illustrates some embodiments of the present disclosure. The procedure is between a UE or PRU (610), a gNB (620), a network node or function (NNF, 630, e.g., LMF, NWDAF, OAM), and a data collection server (640). The data collection server may be part of the same mobile (e.g., 5G) network as the gNB and LMF, such that it may be referred to as mobile network operator (MNO) server. In this case, the MNO may specify the visibility criteria of the data to be collected. Alternately, the data collection server may be external to the mobile network; in that case it may be referred to as an over-the-top (OTT) server. In either case, the data collection server is a recipient of data collected by the NNF and it can use the collected data for various reasons such as network observability, evaluation of key performance indicators (KPIs) or failures, for training AI / ML models, etc.

[0139] As shown in Figure 6, the data collection server requests the NNF to initiate data collection. If the data collection server is outside of the mobile network, it may request this collection via an NEF in the mobile network. The requested data collection may be associated with one or more AI / ML models hosted by the UE / PRU, the gNB, and / or the NNF. The one or more AI / ML models may be used for various operations in the mobile network, such as any of the following:

[0140] • Positioning;

[0141] • Prediction of mobility events (e.g., radio link failure, beam failure, handover);

[0142] • Prediction of radio measurements (e.g., beam-level, cell -level, frequency-level, spatiotemporal, etc.).

[0143] In response to the request, the NNF determines one or more of the following, taking into consideration various information that may be included in the request:

[0144] • Configurations and resources needed (e.g., assistance data);

[0145] • Type of data collection, e.g., periodic, aperiodic, semi-persistent, logged, immediate;

[0146] • Start time, end time, duration of data collection;

[0147] • Control plane or user plane transport for the collected data;

[0148] • Security requirements for transport of the collected data, e.g., encryption and / or integrity protection;

[0149] • Identify UEs for data collection (as necessary).

[0150] The operations by the NNF may differ based on the type of data collection and / or the AI / ML functionality for which the data is collected, and may be correspond to a data collection session associated with one or more specific types of data collected and / or with one or more specific AI / ML functionalities. The data collection may be identified by a data collection identifier (ID, e.g., session ID), which may be associated with session-related information such as data collection type, AI / ML functionality for which the data is collected, etc.

[0151] In the case of UE / PRU data collection as shown in Figure 6, the NNF configures the UE / PRU to perform data collection, which may involve immediate or logged MDT positioning measurements. As mentioned above, the MDT framework provides two ways of activating logged or immediate MDT and collecting data from the UEs. In management-based MDT, data is collected from UEs in a specified area defined by a list of cells or tracking / routing / location areas. In this case, the NNF may identify the identify the UE(s) to perform the data collection based on information in the request from the data collection server. In signaling-based MDT, data is collected from a specific UE, which may be identified in the data collection server’s request.

[0152] In response, the UE / PRU performs the configured data collection (e.g., positioning measurements) and sends a report to the NNF with the collected data in accordance with the configuration (e.g., via user plane transport from UE to NNF, as mentioned above). The NNF then forwards these measurements to the data collection server (e.g., via user plane transport) in fulfillment of the data collection request. In some embodiments, the NNF may determine to which server the collected data should be sent, based on the data collection ID.

[0153] Embodiments may provide various benefits and / or advantages. For example, embodiments may provide a unified framework for data collection for both UE-side and networkside AI / ML models. As another example, embodiments may facilitate compliance with MNO requirements for data collection, such as for data security. As another example, embodiments may facilitate distinguishing between multiple ongoing data collection sessions, thereby improving throughput of data collection in mobile networks.

[0154] Figure 7 shows a signaling diagram of an example procedure that further illustrates some embodiments of the present disclosure. The procedure is between a UE or PRU (710), a gNB (720), an NNF (730, e.g., LMF, NWDAF, 0AM), a data collection server (740), and a UPF (750). As described above in relation to Figure 6, the data collection server may be part of the same mobile (e.g., 5G) network as the gNB and NNF, such that it may be referred to as mobile network operator (MNO) server. In this case, the MNO may specify the visibility criteria of the data to be collected. Alternately, the data collection server may be external to the mobile network; in that case it may be referred to as an over-the-top (OTT) server. Figure 7 also illustrates various transport options where the collected data can be transparent or non-transparent to the NNF. Although the operations shown in Figure 7 are given numerical labels, this is done to facilitate the following description rather than to require or imply any particular operational order, unless expressly stated otherwise below.

[0155] In operation 1, the UE / PRU sends to the NNF an indication of its own capability and user consent for data collection. The capability indication may include size of memory available size, measurements supported for logging, support for immediate and / or logged MDT, etc. For example, the indications in operation 1 can be sent to an LMF via LPP signaling. The user consent indication may also be stored in (and obtained from) another node, such as UDM or GMLC, as specified in 3GPP TS 23.273 (vl8.1.0).

[0156] In operation 2, the data collection server sends a data collection request to the NNF. The requested data collection may be associated with one or more AI / ML models hosted by the UE / PRU, the gNB, and / or the NNF. The one or more AI / ML models may be used for various operations in the mobile network, such as positioning, prediction of mobility events (e.g., radio link failure, beam failure, handover), prediction of radio measurements (e.g., beam-level, celllevel, frequency-level, spatio-temporal, etc.), etc. The data collection request may indicate a specific operation requested, such as any of the following: initiate data collection (e.g., start a data collection session), send data collected, • stop or pause data collection, and

[0157] • stop or pause sending collected data.

[0158] For example, an initial request may be for initiating a data collection session and a subsequent request may be for sending data collected during this data collection session. As a more specific example, the subsequent request may be sent in response to receiving indication from the NNF that the previously initiated data collection session has been completed.

[0159] In the case of initiating a data collection session, the data collection request may also include one or more of the following configuration parameters for the data collection session, such as any of the following:

[0160] • Area where the data needs to be collected (e.g., list of cells, list of tracking areas, list of radio network areas, list of TRP IDs, etc.);

[0161] • Duration of the data collection;

[0162] • Whether data collection is by immediate or logged MDT;

[0163] • Positioning methods to be used for the data collection (e.g., UL-TDOA, DL-TDOA, Multi-RTT, AoA, A-GNSS, etc.);

[0164] • Measurements to be collected (e.g., UE / PRU location, RSTD, RSRP, UE Rx-Tx time difference, gNB Rx-Tx time difference, timing advance, UL-RToA, CIR, PDP, DP, etc.); and

[0165] • Security and / or privacy requirements for the data collection (e.g., encryption, integrity protection, user consent, user privacy, etc.).

[0166] Based on the received data collection request, the NNF performs various actions in operation 3 according to various embodiments. For example, the NNF may identify UEs (or PRUs) that can be sources for the data collection and prepare data collection configurations for these identified UEs. Each data collection configuration may include or identify one or more of the following:

[0167] • duration of the data collection;

[0168] • whether data collection is by immediate or logged MDT;

[0169] • positioning methods to be used for the data collection (e.g., DL-TDOA, Multi-RTT, AoA, A-GNSS, etc.);

[0170] • measurements to be collected (e.g., UE / PRU location, RSTD, RSRP, RSRQ, UE Rx-Tx time difference, timing advance, etc.);

[0171] • assistance data for the collection (e.g., for DL-PRS measurement); and

[0172] • one or more data collection criteria.

[0173] The following are some example data collection criteria for UEs / PRUs: • RSRP larger or less than a threshold;

[0174] • Non Line of Sight (NLOS) or Line of Sight (LOS) conditions, as defined in 3GPP TS 37.355 (vl8.L0);

[0175] • Geometric dilution of precision (GDOP) larger or less than a threshold;

[0176] • UE speed larger or less than a threshold; and

[0177] • UE environment, e.g., indoor, outdoor, crossing from indoor to outdoor, etc.).

[0178] In some embodiments, the NNF may determine data collection configurations for one or more gNBs (e.g., 720) that can be measurement sources for the data collection. Such gNB measurements may include UL / DL channel quality estimates, layer-2 measurements, packet error rate, packet delays, throughput, gNB Rx-Tx time difference, UL-RToA, etc.

[0179] In some embodiments, the NNF may determine whether the data to be collected by UE or gNB is to be sent only to the data collection server or whether it may be visible (or comprehensible) to other network entities such as the gNB (in case of UE collected data), the NNF, 0AM function, etc. Based on this determination, the NNF may also select an appropriate transport configuration for the UE (optionally gNB) to send the collected data. The following are some example UE transport configurations selected by the NNF based on various conditions:

[0180] • Select control plane (e.g., RRC) when the collected data should be visible to the gNB.

[0181] • When the NNF is an LMF, it selects control plane (e.g., LPP over NAS) or user plane, when the collected data should be visible to the LMF.

[0182] • Selects user plane transport to data collection server when the collected data should not be visible (i.e., should be transparent) to the NNF and the gNB.

[0183] In some embodiments, the transport configuration may also include an indication of whether encryption and / or integrity protection of the collected data should be enabled or disabled and, if enabled, the algorithms and / or keys to use. In some embodiments, the transport configuration may also include one or more of the following:

[0184] • quality-of-service requirements for any flow or radio bearer used to report the collected data;

[0185] • an indication of a particular flow or radio bearer on which the collected data should be reported, e.g., to avoid mixing with any other UE proprietary information;

[0186] • when the collected data should be transparent to other network entities, an IP address to which the data should be sent by the UE, which may correspond to the data collection server or a different server within or external to the mobile network; and • routing instructions for data collected by gNB or UPF or UE collected data received by gNB, e.g., whether to send the collected data to the NNF (e.g., OAM, LMF, NWDAF) or to the data collection server.

[0187] The actions by the NNF in operation 3 may differ based on the type of data collection and / or the AI / ML functionality for which the data is collected, and may be correspond to a data collection session associated with one or more specific types of data collected and / or with one or more specific AI / ML functionalities. The data collection session may be identified by a data collection session identifier (ID), which may be associated with session-related information such as data collection type, AI / ML functionality for which the data is collected, etc. The data collection session ID may be included in the data collection configurations sent to the UE / PRU and / or the gNB. In many cases, the same data collection session ID may be included in the data collection configurations sent to the UE / PRU and the gNB, but it is possible that different IDs may be used.

[0188] In operation 4, the NNF sends the data collection configuration determined in operation 3 to the identified UEs / PRUs (e.g., 710). In case the NNF determined a data collection configuration for the gNB, the NNF may also send this data collection configuration to the gNB in operation 4 (not shown). Subsequently, the UE / PRUs and / or gNBs perform data collection in accordance with the received data collection configuration. When the data collection session ID is included in the data collection configuration, each of the UEs / PRUs and the gNBs associate the measurements performed for the data collection with this data collection session ID.

[0189] In operation 5, when the transport configuration indicates user plane transport for the collected data, the UE establishes a protocol data unit (PDU) session with the UPF for the purpose of sending the collected data. If the transport configuration also includes flow-related requirements, the UE establishes the PDU session in accordance with these requirements. In operation 6, the UE sends (or reports) the collected data (i.e., measurements) to the UPF via the PDU session established in operation 5. In case the UE associated the data collection session ID with the collected data, the UE includes the data collection session ID in the report sent in operation 6.

[0190] Figure 7 shows two possibilities for operation 7 in. In operation 7a, the UPF forwards the collected data (along with data collection session ID) to the data collection server directly. In operation 7b, the UPF forwards the collected data to the NNF, which then forwards it to the data collection server.

[0191] The reporting of the collected data to UPF as shown in Figure 7 is merely one example. As another example, if the UE reports the collected data to the gNB via RRC (e.g., as logged or immediate MDT measurements), the UE may include the data collection session ID in the RRC message with the collected data. Similarly, if the UE reports the collected data to the LMF via LPP, the UE may include the data collection session ID in an LPP message with the collected data.

[0192] Likewise, if the gNB also performs data collection, the gNB includes the data collection session ID along with the collected data in a message sent to the NNF, to the data collection server, or to a second NNF (e.g., ADRF, LMF, OAM, etc.) in accordance with the transport configuration included in the gNB’s data collection configuration. Note that the gNB’s data collection may include measurements that it performs, measurements that it collects from other nodes (e.g., TRPs), and / or measurements collected from UEs.

[0193] When the gNB sends the collected data to the NNF or the second NNF, the recipient NNF may then send the collected data to the data collection server. In some embodiments, when the collected data is non-transparent to (i.e., comprehensible by) the recipient NNF, the recipient NNF may determine based on the included data collection session ID what part of the collected data it should retain and what part of the collected data should be sent to the data collection server. For example, the recipient NNF may want to use at least part of the collected data for its own operations, such as for network observability, evaluation of KPIs or failures, etc. Note that when the collected data is transparent to (i.e., not comprehensible by) the recipient NNF, the recipient NNF may send all of the collected data to the data collection server.

[0194] In some embodiments, the data collection session ID may also be used by the recipient NNF to determine where to send the collected data (or part thereof). For example, if data collection session ID indicates that the associated collected data are associated with an AI / ML model hosted by a particular entity (e.g., UE, gNB, LMF), then the recipient NNF may send the collected data to a data collection server responsible for training that AI / ML model. This data collection server may be within the mobile network (e.g., NWDAF), outside of the mobile network (e.g., external AF), or a logical function of the recipient NNF itself (e.g., MTLF when the recipient NNF is NWDAF).

[0195] Various features of some embodiments described above correspond to various operations illustrated in Figures 8-9, which show exemplary methods (e.g., procedures) for an NNF and a data collection server, respectively. In other words, various features of the operations described below correspond to some embodiments described above. Furthermore, the exemplary methods shown in Figures 8-9 can be used cooperatively to provide various benefits, advantages, and / or solutions to problems described herein. Although Figures 8-9 show specific blocks in particular orders, the operations of the exemplary methods can be performed in different orders than shown and can be combined and / or divided into blocks having different functionality than shown. Optional blocks or operations are indicated by dashed lines.

[0196] In particular, Figure 8 shows an exemplary method (e.g., procedure) for an NNF configured to facilitate data collection in a communication network, according to various embodiments of the present disclosure. The exemplary method can be performed by any appropriate NNF (e.g., LMF, NWDAF, OAM, etc.) or network equipment configured to implement such an NNF, as described elsewhere herein.

[0197] The exemplary method includes the operations of block 820, where the NNF receives, from a data collection server, a request to initiate a data collection session in the communication network, wherein the request includes a session configuration for the data collection session. The exemplary method also includes the operations of block 830, where based on the session configuration, the NNF selects one or more network entities from which to collect data and determining respective data collection configurations for the selected entities. The exemplary method also includes the operations of block 1040, where the NNF sends the respective data collection configurations to the selected network entities.

[0198] In some embodiments, the requested data collection is associated with machine learning (ML) models hosted by one or more of the following: the NNF, at least one of the selected network entities, and the data collection server. In some embodiments, the session configuration received in block 820 includes or identifies one or more of the following:

[0199] • geographic area where the data needs to be collected;

[0200] • duration of the data collection;

[0201] • positioning methods to be used for the data collection;

[0202] • measurements to be collected;

[0203] • security and / or privacy requirements for the data collection; and

[0204] • a recipient for the collected data.

[0205] In some embodiments, the session configuration includes an identifier (ID) associated with the data collection session, and the data collection session ID is included in or with the data collection configurations sent to the selected network entities. In some embodiments, each data collection configuration includes or identifies one or more of the following:

[0206] • duration of the data collection;

[0207] • minimization of drive testing (MDT) configuration to be used for the data collection;

[0208] • positioning methods to be used for the data collection;

[0209] • measurements to be collected;

[0210] • assistance data for the data collection;

[0211] • transport configuration for reporting the collected data; and

[0212] • one or more data collection criteria. In some of these embodiments, the one or more data collection criteria include one or more of the following: signal strength threshold, geometric dilution of precision (GDOP) threshold, UE speed threshold, UE environment type, and UE line-of-sight (LOS) or non-LOS signal conditions. In some of these embodiments, each data transport configuration includes or indicates one or more of the following:

[0213] • whether control plane or user plane transport should be used;

[0214] • whether encryption and / or integrity protection should be used;

[0215] • whether the collected data in a report should be comprehensible by intermediate network entities through which the report is sent;

[0216] • quality-of-service requirements for any flow or radio bearer used to report the collected data;

[0217] • a particular flow or radio bearer on which the collected data should be reported;

[0218] • routing instructions for the collected data; and

[0219] • an Internet Protocol (IP) address to be used for reporting the collected data.

[0220] In some embodiments, the selected network entities include one or more of the following: one or more UEs, one or more RAN nodes, and a UPF. In some of these embodiments, the exemplary method also includes the operations of block 810, where the NNF receives the following from a UE: a first indication of the UE’s capability for data collection, and a second indication of user consent for data collection. In such case, the UE is selected as one of network entities based on the first and second indications.

[0221] In some embodiments, the exemplary method also includes the following operations, labelled with corresponding block numbers:

[0222] • (850) receiving, from each of the selected network entities, a report that includes data collected according to the data collection configuration sent to the network entity; and

[0223] • (880) sending at least a portion of the collected data in each report to one of the following recipients: the data collection server, a second data collection server, or a second NNF of the communication network.

[0224] In some of these embodiments, each report includes an identifier (ID) associated with the data collection session. In some variants of these embodiments, the recipient of the at least a portion of the collected data in each report is identified by one of the following: implicitly by the data collection session ID, and an explicitly by a recipient identifier in the session configuration.

[0225] In some variants of these embodiments, the exemplary method also includes the operations of block 860, where based on the data collection session ID included in each report, the NNF determines a first portion of the collected data in the report to retain and a second portion of the collected data in the report to send to the recipient. In some further variants, determining the first and second portions in block 860 is further based on whether the collected data in the report is comprehensible by (or transparent / non-transparent to) the NNF.

[0226] In some of these embodiments, the exemplary method also includes the operations of block 870, where the NNF receives from the data collection server a further request to provide data collected for the data collection session. The at least a portion of the collected data in each report is sent in block 880 in response to the further request.

[0227] In some embodiments, the exemplary method also includes the following operations, labelled with corresponding block numbers:

[0228] • (890) receiving from the data collection server a second request to stop or pause the data collection session; and

[0229] • (895) sending respective commands to the selected network entities in accordance with the second request.

[0230] In some embodiments, the data collection server is one of the following: a further NNF of the communication network, or an application function (AF) or server external to the communication network. In some embodiments, the NNF is one of the following: a location management function (LMF), a network data analytics function (NWDAF), or an operations / administration / maintenance (0AM) function.

[0231] In addition, Figure 9 shows an exemplary method (e.g., procedure) for a data collection server for a communication network, according to various embodiments of the present disclosure. The exemplary method can be performed by any appropriate data collection server (e. g. , NWDAF, external AF, etc.) or network equipment configured to implement such as server, as described elsewhere herein.

[0232] The exemplary method can include the operations of block 910, where the data collection server sends, to an NNF of the communication network, a request to initiate a data collection session in the communication network, wherein the request includes a session configuration for the data collection session.

[0233] In some embodiments, the requested data collection is associated with ML models hosted by one or more of the following: the NNF, at least one of the selected network entities, and the data collection server. In some embodiments, the session configuration includes or identifies one or more of the following:

[0234] • geographic area where the data needs to be collected;

[0235] • duration of the data collection;

[0236] • positioning methods to be used for the data collection;

[0237] • measurements to be collected;

[0238] • security and / or privacy requirements for the data collection; and • a recipient for the collected data.

[0239] In some of these embodiments, the recipient for the collected data is one of the following: the data collection server, a second data collection server, or a second NNF of the communication network.

[0240] In some embodiments, the session configuration includes an ID associated with the data collection session. In some embodiments, the exemplary method also includes the operations of block 930, where the data collection server receives, from one or more network entities, respective reports that include data collected according to the session configuration sent to the network entity.

[0241] In some of these embodiments, the one or more network entities include one or more of the following: the NNF, one or more UEs, one or more RAN nodes, and a UPF. In some of these embodiments, each report includes an ID associated with the data collection session. In some of these embodiments, the exemplary method also includes the operations of block 920, where the data collection server sends to the NNF a further request to provide data collected for the data collection session. The one or more reports are received in block 930 in response to the further request.

[0242] In some embodiments, the exemplary method also includes the operations of block 930, where the data collection server sends to the NNF a second request to stop or pause the data collection session.

[0243] In some embodiments, the data collection server is a further NNF of the communication network. In other embodiments, the data collection server is an AF or server external to the communication network. In some embodiments, the NNF is one of the following: an LMF, an NWDAF, or an 0AM function.

[0244] Various features of some embodiments described above correspond to various operations illustrated in Figures 10-11, which show other exemplary methods (e.g, procedures) for an NNF and a data collection server, respectively. In other words, various features of the operations described below correspond to some embodiments described above. Furthermore, the exemplary methods shown in Figures 10-11 can be used cooperatively to provide various benefits, advantages, and / or solutions to problems described herein. Although Figures 10-11 show specific blocks in particular orders, the operations of the exemplary methods can be performed in different orders than shown and can be combined and / or divided into blocks having different functionality than shown. Optional blocks or operations are indicated by dashed lines.

[0245] In particular, Figure 10 shows an exemplary method (e.g., procedure) for an NNF configured to facilitate data collection in a communication network, according to various embodiments of the present disclosure. The exemplary method can be performed by any appropriate NNF (e.g., LMF, NWDAF, 0AM, etc.) or network equipment configured to implement such an NNF, as described elsewhere herein. The exemplary method includes the operations of block 1020, where the NNF receives, from a data collection server, a request to initiate data collection by a UE in the communication network. The exemplary method also includes the operations of block 1030, where the NNF determines a configuration for the data collection by the UE. In particular, the configuration indicates user plane transport of the collected data to the NNF. The exemplary method also includes the operations of block 1040, where the NNF sends the determined configurations to the UE. Figures 6-7 shows examples of these operations.

[0246] In some embodiments, the data collection is associated with machine learning (ML) models hosted by one or more of the following: the NNF, the UE, and the data collection server. In some embodiments, the request received in block 1020 includes or identifies one or more of the following:

[0247] • the UE;

[0248] • geographic area where the data needs to be collected;

[0249] • duration of the data collection;

[0250] • positioning methods to be used for the data collection;

[0251] • measurements to be collected;

[0252] • security and / or privacy requirements for the data collection; and

[0253] • a recipient for the collected data.

[0254] In some embodiments, the configuration also indicates one or more of the following:

[0255] • duration of the data collection;

[0256] • minimization of drive testing (MDT) configuration to be used for the data collection;

[0257] • positioning methods to be used for the data collection;

[0258] • measurements to be collected;

[0259] • assistance data for the data collection; and

[0260] • one or more data collection criteria.

[0261] In some of these embodiments, the one or more data collection criteria include one or more of the following: signal strength threshold, geometric dilution of precision (GDOP) threshold, UE speed threshold, UE environment type, and UE line-of-sight (LOS) or non-LOS signal conditions. In some of these embodiments, the configuration includes or indicates one or more of the following associated with the user plane transport:

[0262] • whether encryption and / or integrity protection should be used;

[0263] • whether the collected data in a report should be comprehensible by intermediate network entities through which the report is sent; • quality-of-service requirements for any flow or radio bearer used to report the collected data;

[0264] • a particular flow or radio bearer on which the collected data should be reported; and

[0265] • an IP address of the data collection server.

[0266] In some embodiments, the exemplary method also includes the operations of block 1005, where the NNF receives from a UE a first indication of the UE’s capability for data collection. In such case, the configuration is determined in block 1030 based on the first indication. In some embodiments, the exemplary method also includes the operations of block 1010, where the NNF obtains, from a UDM of the communication network, a second indication of data collection consent by a user associated with the UE. As mentioned above, the second (or user consent) indication may also be stored in UDM as specified in 3GPP TS 23.273 (vl8. 1.0). In such case, the configuration is determined in block 1030 based on the second indication.

[0267] In some embodiments, the exemplary method also includes the following operations, labelled with corresponding block numbers:

[0268] • (1050) receiving from the UE a report that includes data collected according to the configuration sent to the UE; and

[0269] • (1080) sending at least a portion of the collected data in the report to the data collection server.

[0270] In some of these embodiments, the request includes an identifier (ID) associated with the data collection. Furthermore, the ID is included in or with the configuration sent to the UE, as well as in or with the report received from the UE. In some variants of these embodiments, the ID included in or with the report indicates that the at least a portion of the collected data in the report should be sent to the data collection server.

[0271] In some variants of these embodiments, the exemplary method also includes the operations of block 1060, where based on the ID included in or with the report, the NNF determines a first portion of the collected data in the report to retain and a second portion of the collected data in the report to send to the recipient. In some further variants, determining the first and second portions in block 1060 is further based on whether the collected data in the report is comprehensible by (or transparent / non-transparent to) the NNF.

[0272] In some variants of these embodiments, the exemplary method also includes the operations of block 1070, where the NNF receives from the data collection server a further request to provide data collected in accordance with the request. The at least a portion of the collected data from the report is sent in block 1080 in response to the further request.

[0273] In some embodiments, the exemplary method also includes the following operations, labelled with corresponding block numbers: • (1090) receiving from the data collection server a second request to stop or pause the data collection by the UE; and

[0274] • (1095) sending a stop or pause commands to the UE in accordance with the second request.

[0275] In some embodiments, the data collection server is one of the following: a further NNF of the communication network, or an AF or server external to the communication network. In some embodiments, the NNF is one of the following: an LMF, an NWDAF, or an 0AM function.

[0276] In addition, Figure 11 shows an exemplary method (e.g., procedure) for a data collection server for a communication network, according to various embodiments of the present disclosure. The exemplary method can be performed by any appropriate data collection server (e. g. , NWDAF, external AF, etc.) or network equipment configured to implement such as server, as described elsewhere herein.

[0277] The exemplary method includes the operations of block 1110, where the data collection server sends, to an NNF of the communication network, a request to initiate data collection by a UE in the communication network. The exemplary method also includes the operations of block 1130, where the data collection server receives from the NNF a report that includes data collected by the UE in accordance with the request. Figures 6-7 shows examples of these operations.

[0278] In some embodiments, the data collection is associated with ML models hosted by one or more of the following: the NNF, the UE, and the data collection server. In some embodiments, the request includes or identifies one or more of the following:

[0279] • the UE;

[0280] • geographic area where the data needs to be collected;

[0281] • duration of the data collection;

[0282] • positioning methods to be used for the data collection;

[0283] • measurements to be collected;

[0284] • security and / or privacy requirements for the data collection; and

[0285] • a recipient for the collected data.

[0286] In some embodiments, the request includes an ID associated with the data collection and the ID is also included in or with the received report. In some embodiments, the exemplary method also includes the operations of block 1120, where the data collection server sends to the NNF a further request to provide data collected in accordance with the request. The report is received in block 1130 in response to the further request. In some embodiments, the report is received from the NNF via user plane transport.

[0287] In some embodiments, the exemplary method also includes the operations of block 1130, where the data collection server sends to the NNF a second request to stop or pause the data collection session. In some embodiments, the data collection server is a further NNF of the communication network. In other embodiments, the data collection server is an AF or server external to the communication network. In some embodiments, the NNF is one of the following: an LMF, an NWDAF, or an OAM function.

[0288] Although various embodiments are described above in terms of methods, techniques, and / or procedures, the person of ordinary skill will readily comprehend that such methods, techniques, and / or procedures can be embodied by various combinations of hardware and software in various systems, communication devices, computing devices, control devices, apparatuses, non-transitory computer-readable media, computer program products, etc.

[0289] Figure 12 shows an example of a communication system 1200 in accordance with some embodiments. In this example, communication system 1200 includes a telecommunication network 1202 that includes an access network 1204 (e.g., RAN) and a core network 1206, which includes one or more core network nodes 1208. In some embodiments, telecommunication network 1202 can also include one or more Network Management (NM) nodes 1218, which can be part of an operation support system (OSS), a business support system (BSS), and / or an OAM system. The NM nodes can monitor and / or control operations of other nodes in access network 1204 and core network 1206. Although not shown in Figure 12, NM node 1218 can be configured to communicate with other nodes in access network 1204 and core network 1206 for these purposes.

[0290] Access network 1204 includes one or more access network nodes, such as network nodes 1210a-b (one or more of which may be generally referred to as network nodes 1210), or any other similar 3GPP access nodes or non-3GPP access points. Moreover, as will be appreciated by those of skill in the art, a network node is not necessarily limited to an implementation in which a radio portion and a baseband portion are supplied and integrated by a single vendor. Thus, it will be understood that network nodes include disaggregated implementations or portions thereof. For example, in some embodiments, telecommunication network 1202 includes one or more Open- RAN (ORAN) network nodes. An ORAN network node is a node in telecommunication network 1202 that supports an ORAN specification (e.g., a specification published by the O-RAN Alliance, or any similar organization) and may operate alone or together with other nodes to implement one or more functionalities of any node in telecommunication network 1202, including one or more network nodes 1210 and / or core network nodes 1208.

[0291] Examples of an ORAN network node include an open radio unit (O-RU), an open distributed unit (O-DU), an open central unit (O-CU), including an O-CU control plane (O-CU- CP) or an O-CU user plane (O-CU-UP), a RAN intelligent controller (near-real time or non-real time) hosting software or software plug-ins, such as a near-real time control application (e.g., xApp) or a non-real time control application (e.g., rApp), or any combination thereof (the adjective “open” designating support of an ORAN specification). The network node may support a specification by, for example, supporting an interface defined by the ORAN specification, such as an Al, Fl, Wl, El, E2, X2, Xn interface, an open fronthaul user plane interface, or an open fronthaul management plane interface. Moreover, an ORAN access node may be a logical node in a physical node. Furthermore, an ORAN network node may be implemented in a virtualization environment (described further below) in which one or more network functions are virtualized. For example, the virtualization environment may include an O-Cloud computing platform orchestrated by a Service Management and Orchestration Framework via an 0-2 interface defined by the 0-RAN Alliance or comparable technologies. Network nodes 1210 facilitate direct or indirect connection of UEs, such as by connecting UEs 1212a-d (one or more of which may be generally referred to as UEs 1212) to core network 1206 over one or more wireless connections.

[0292] Example wireless communications over a wireless connection include transmitting and / or receiving wireless signals using electromagnetic waves, radio waves, infrared waves, and / or other types of signals suitable for conveying information without the use of wires, cables, or other material conductors. Moreover, in different embodiments, communication system 1200 may include any number of wired or wireless networks, network nodes, UEs, and / or any other components or systems that may facilitate or participate in the communication of data and / or signals whether via wired or wireless connections. Communication system 1200 may include and / or interface with any type of communication, telecommunication, data, cellular, radio network, and / or other similar type of system.

[0293] UEs 1212 may be any of a wide variety of communication devices, including wireless devices arranged, configured, and / or operable to communicate wirelessly with network nodes 1210 and other communication devices. Similarly, network nodes 1210 are arranged, capable, configured, and / or operable to communicate directly or indirectly with UEs 1212 and / or with other network nodes or equipment in telecommunication network 1202 to enable and / or provide network access, such as wireless network access, and / or to perform other functions, such as administration in telecommunication network 1202.

[0294] In the depicted example, core network 1206 connects network nodes 1210 to one or more hosts, such as host 1216. These connections may be direct or indirect via one or more intermediary networks or devices. In other examples, network nodes may be directly coupled to hosts. Core network 1206 includes one or more core network nodes (e.g., 1208) that are structured with hardware and software components. Features of these components may be substantially similar to those described with respect to the UEs, network nodes, and / or hosts, such that the descriptions thereof are generally applicable to the corresponding components of core network node 1208. Example core network nodes include functions of one or more of a Mobile Switching Center (MSC), Mobility Management Entity (MME), Home Subscriber Server (HSS), Access and Mobility Management Function (AMF), Session Management Function (SMF), Authentication Server Function (AUSF), Subscription Identifier De-concealing function (SIDF), Unified Data Management (UDM), Security Edge Protection Proxy (SEPP), Network Exposure Function (NEF), and / or a User Plane Function (UPF).

[0295] Host 1216 may be under the ownership or control of a service provider other than an operator or provider of access network 1204 and / or telecommunication network 1202, and may be operated by the service provider or on behalf of the service provider. Host 1216 may host a variety of applications to provide one or more service. Examples of such applications include live and pre-recorded audio / video content, data collection services such as retrieving and compiling data on various ambient conditions detected by a plurality of UEs, analytics functionality, social media, functions for controlling or otherwise interacting with remote devices, functions for an alarm and surveillance center, or any other such function performed by a server.

[0296] As a whole, communication system 1200 of Figure 12 enables connectivity between the UEs, network nodes, and hosts. In that sense, the communication system may be configured to operate according to predefined rules or procedures, such as specific standards that include, but are not limited to: Global System for Mobile Communications (GSM); Universal Mobile Telecommunications System (UMTS); Long Term Evolution (LTE), and / or other suitable 2G, 3G, 4G, 5G standards, or any applicable future generation standard (e.g., 6G); wireless local area network (WLAN) standards, such as the Institute of Electrical and Electronics Engineers (IEEE) 802.11 standards (WiFi); and / or any other appropriate wireless communication standard, such as the Worldwide Interoperability for Microwave Access (WiMax), Bluetooth, Z-Wave, Near Field Communication (NFC) ZigBee, LiFi, and / or any low-power wide-area network (LPWAN) standards such as LoRa and Sigfox.

[0297] In some examples, telecommunication network 1202 is a cellular network that implements 3 GPP standardized features. Accordingly, telecommunication network 1202 may support network slicing to provide different logical networks to different devices that are connected to telecommunication network 1202. For example, telecommunication network 1202 may provide Ultra Reliable Low Latency Communication (URLLC) services to some UEs, while providing Enhanced Mobile Broadband (eMBB) services to other UEs, and / or Massive Machine Type Communication (mMTC)ZMassive loT services to yet further UEs.

[0298] In some examples, UEs 1212 are configured to transmit and / or receive information without direct human interaction. For instance, a UE may be designed to transmit information to access network 1204 on a predetermined schedule, when triggered by an internal or external event, or in response to requests from access network 1204. Additionally, a UE may be configured for operating in single- or multi-RAT or multi-standard mode. For example, a UE may operate with any one or combination of Wi-Fi, NR (New Radio) and LTE, i.e., being configured for multi-radio dual connectivity (MR-DC), such as E-UTRAN (Evolved-UMTS Terrestrial Radio Access Network) New Radio - Dual Connectivity (EN-DC).

[0299] In the example, hub 1214 communicates with access network 1204 to facilitate indirect communication between one or more UEs (e.g., 1212c and / or 1212d) and network nodes (e.g., 1210b). In some examples, hub 1214 may be a controller, router, content source and analytics, or any of the other communication devices described herein regarding UEs. For example, hub 1214 may be a broadband router enabling access to core network 1206 for the UEs. As another example, hub 1214 may be a controller that sends commands or instructions to one or more actuators in the UEs. Commands or instructions may be received from the UEs, network nodes 1210, or by executable code, script, process, or other instructions in hub 1214. As another example, hub 1214 may be a data collector that acts as temporary storage for UE data and, in some embodiments, may perform analysis or other processing of the data. As another example, hub 1214 may be a content source. For example, for a UE that is a VR headset, display, loudspeaker or other media delivery device, hub 1214 may retrieve VR assets, video, audio, or other media or data related to sensory information via a network node, which hub 1214 then provides to the UE either directly, after performing local processing, and / or after adding additional local content. In still another example, hub 1214 acts as a proxy server or orchestrator for the UEs, in particular if one or more of the UEs are low energy loT devices.

[0300] Hub 1214 may have a constant / persistent or intermittent connection to network node 1210b. Hub 1214 may also allow for a different communication scheme and / or schedule between hub 1214 and UEs (e.g., 1212c and / or 1212d), and between hub 1214 and core network 1206. In other examples, hub 1214 is connected to core network 1206 and / or one or more UEs via a wired connection. Moreover, hub 1214 may be configured to connect to an M2M service provider over access network 1204 and / or to another UE over a direct connection. In some scenarios, UEs may establish a wireless connection with network nodes 1210 while still connected via hub 1214 via a wired or wireless connection. In some embodiments, hub 1214 may be a dedicated hub - that is, a hub whose primary function is to route communications to / from the UEs from / to network node 1210b. In other embodiments, hub 1214 may be a non-dedicated hub - that is, a device which is capable of operating to route communications between the UEs and network node 1210b, but which is additionally capable of operating as a communication start and / or end point for certain data channels. In some embodiments, core network node 1208 or NM node 1218 may be configured to perform methods, procedures, or operations attributed to an NNF in the above descriptions of various embodiments (e.g., Figures 6-8 and 10). In some embodiments, core network node 1208 or host 1216 may be configured to perform methods, procedures, or operations attributed to a data collection server in the above descriptions of various embodiments (e.g., Figures 6-7, 9, and 11).

[0301] Figure 13 is a block diagram of a host 1300, which may be an embodiment of host 1016 of Figure 10, in accordance with various aspects described herein. Host 1300 may be or comprise various combinations hardware and / or software, including a standalone server, a blade server, a cloud-implemented server, a distributed server, a virtual machine, container, or processing resources in a server farm. Host 1300 may provide one or more services to one or more UEs.

[0302] Host 1300 includes processing circuitry 1302 that is operatively coupled via a bus 1304 to an input / output interface 1306, a network interface 1308, a power source 1310, and a memory 1312. Other components may be included in other embodiments. Features of these components may be substantially similar to those described with respect to other figures, such as Figure 12, such that the descriptions thereof are generally applicable to the corresponding components of host 1300.

[0303] Memory 1312 may include one or more computer programs including one or more host application programs 1314 and data 1316, which may include user data, e.g., data generated by a UE for host 1300 or data generated by host 1300 for a UE. Embodiments of host 1300 may utilize only a subset or all of the components shown. Host application programs 1314 may be implemented in a container-based architecture and may provide support for video codecs (e.g., Versatile Video Coding (VVC), High Efficiency Video Coding (HEVC), Advanced Video Coding (AVC), MPEG, VP9) and audio codecs (e.g., FLAC, Advanced Audio Coding (AAC), MPEG, G.713), including transcoding for multiple different classes, types, or implementations of UEs (e.g., handsets, desktop computers, wearable display systems, heads-up display systems, headsup display systems). Host application programs 1314 may also provide for user authentication and licensing checks and may periodically report health, routes, and content availability to a central node, such as a device in or on the edge of a core network. Accordingly, host 1300 may select and / or indicate a different host for over-the-top services for a UE. Host application programs 1314 may support various protocols, such as the HTTP Live Streaming (HLS) protocol, Real-Time Messaging Protocol (RTMP), Real-Time Streaming Protocol (RTSP), Dynamic Adaptive Streaming over HTTP (MPEG-DASH), etc.

[0304] In some embodiments, host 1300 may be configured to perform methods, procedures, or operations attributed to a data collection server in the above descriptions of various embodiments (e.g., Figures 6-7, 9, and 13). Figure 14 shows a network node 1400 in accordance with some embodiments. Examples of network nodes include, but are not limited to, access points (APs) (e.g., radio access points), base stations (e.g., radio base stations, Node Bs, eNBs, gNBs), and O-RAN nodes or components of an O-RAN node (e.g., O-RU, O-DU, O-CU).

[0305] Base stations may be categorized based on the amount of coverage they provide (or, stated differently, their transmit power level) and so, depending on the provided amount of coverage, may be referred to as femto base stations, pico base stations, micro base stations, or macro base stations. A base station may be a relay node or a relay donor node controlling a relay. A network node may also include one or more (or all) parts of a distributed radio base station such as centralized digital units, distributed units (e.g., in an O-RAN access node) and / or remote radio units (RRUs), sometimes referred to as Remote Radio Heads (RRHs). Such remote radio units may or may not be integrated with an antenna as an antenna integrated radio. Parts of a distributed radio base station may also be referred to as nodes in a distributed antenna system (DAS).

[0306] Other examples of network nodes include multiple transmission point (multi-TRP) 5G access nodes, multi-standard radio (MSR) equipment such as MSR BSs, network controllers such as radio network controllers (RNCs) or base station controllers (BSCs), base transceiver stations (BTSs), transmission points, transmission nodes, multi-cell / multicast coordination entities (MCEs), Operation and Maintenance (O&M) nodes, Operations Support System (OSS) nodes, Self-Organizing Network (SON) nodes, positioning nodes (e.g., Evolved Serving Mobile Location Centers (E-SMLCs)), and / or Minimization of Drive Tests (MDTs).

[0307] Network node 1400 includes processing circuitry 1402, memory 1404, communication interface 1406, and power source 1408. Network node 1400 may be composed of multiple physically separate components (e.g., a NodeB component and a RNC component, or a BTS component and a BSC component, etc.), which may each have their own respective components. In certain scenarios in which network node 1400 comprises multiple separate components (e.g., BTS and BSC components), one or more of the separate components may be shared among several network nodes. For example, a single RNC may control multiple NodeBs. In such a scenario, each unique NodeB and RNC pair, may in some instances be considered a single separate network node. In some embodiments, network node 1400 may be configured to support multiple radio access technologies (RATs). In such embodiments, some components may be duplicated (e.g., separate memory 1404 for different RATs) and some components may be reused (e.g., a same antenna 1410 may be shared by different RATs). Network node 1400 may also include multiple sets of the various illustrated components for different wireless technologies integrated into network node 1400, for example GSM, WCDMA, LTE, NR, WiFi, Zigbee, Z-wave, LoRaWAN, Radio Frequency Identification (RFID) or Bluetooth wireless technologies. These wireless technologies may be integrated into the same or different chip or set of chips and other components within network node 1400.

[0308] Processing circuitry 1402 may comprise a combination of one or more of a microprocessor, controller, microcontroller, central processing unit, digital signal processor, application-specific integrated circuit, field programmable gate array, or any other suitable computing device, resource, or combination of hardware, software and / or encoded logic operable to provide, either alone or in conjunction with other network node 1400 components, such as memory 1404, to provide network node 1400 functionality.

[0309] In some embodiments, processing circuitry 1402 includes a system on a chip (SOC). In some embodiments, processing circuitry 1402 includes one or more of radio frequency (RF) transceiver circuitry 1412 and baseband processing circuitry 1414. In some embodiments, RF transceiver circuitry 1412 and baseband processing circuitry 1414 may be on separate chips (or sets of chips), boards, or units, such as radio units and digital units. In alternative embodiments, part or all of RF transceiver circuitry 1412 and baseband processing circuitry 1414 may be on the same chip or set of chips, boards, or units.

[0310] Memory 1404 may comprise any form of volatile or non-volatile computer-readable memory including, without limitation, persistent storage, solid-state memory, remotely mounted memory, magnetic media, optical media, random access memory (RAM), read-only memory (ROM), mass storage media (for example, a hard disk), removable storage media (for example, a flash drive, a Compact Disk (CD) or a Digital Video Disk (DVD)), and / or any other volatile or non-volatile, non-transitory device-readable and / or computer-executable memory devices that store information, data, and / or instructions that may be used by processing circuitry 1402. Memory 1404 may store any suitable instructions, data, or information, including a computer program, software, an application including one or more of logic, rules, code, tables, and / or other instructions (collected denoted computer program 1404a, which may be in the form of a computer program product) capable of being executed by processing circuitry 1402 and utilized by network node 1400. Memory 1404 may be used to store any calculations made by processing circuitry 1402 and / or any data received via communication interface 1406. In some embodiments, processing circuitry 1402 and memory 1404 is integrated.

[0311] Communication interface 1406 is used in wired or wireless communication of signaling and / or data between a network node, access network, and / or UE. As illustrated, communication interface 1406 comprises port(s) / terminal(s) 1416 to send and receive data, for example to and from a network over a wired connection. Communication interface 1406 also includes radio frontend circuitry 1418 that may be coupled to, or in certain embodiments a part of, antenna 1410. Radio front-end circuitry 1418 comprises filters 1420 and amplifiers 1422. Radio front-end circuitry 1418 may be connected to an antenna 1410 and processing circuitry 1402. The radio front-end circuitry may be configured to condition signals communicated between antenna 1410 and processing circuitry 1402. Radio front-end circuitry 1418 may receive digital data that is to be sent out to other network nodes or UEs via a wireless connection. Radio front-end circuitry 1418 may convert the digital data into a radio signal having the appropriate channel and bandwidth parameters using a combination of filters 1420 and / or amplifiers 1422. The radio signal may then be transmitted via antenna 1410. Similarly, when receiving data, antenna 1410 may collect radio signals which are then converted into digital data by radio front-end circuitry 1418. The digital data may be passed to processing circuitry 1402. In other embodiments, the communication interface may comprise different components and / or different combinations of components.

[0312] In certain alternative embodiments, network node 1400 does not include separate radio front-end circuitry 1418, instead, processing circuitry 1402 includes radio front-end circuitry and is connected to antenna 1410. Similarly, in some embodiments, all or some of RF transceiver circuitry 1412 is part of communication interface 1406. In still other embodiments, communication interface 1406 includes one or more ports or terminals 1416, radio front-end circuitry 1418, and RF transceiver circuitry 1412, as part of a radio unit (not shown), and communication interface 1406 communicates with baseband processing circuitry 1414, which is part of a digital unit (not shown).

[0313] Antenna 1410 may include one or more antennas, or antenna arrays, configured to send and / or receive wireless signals. Antenna 1410 may be coupled to radio front-end circuitry 1418 and may be any type of antenna capable of transmitting and receiving data and / or signals wirelessly. In certain embodiments, antenna 1410 is separate from network node 1400 and connectable to network node 1400 through an interface or port.

[0314] Antenna 1410, communication interface 1406, and / or processing circuitry 1402 may be configured to perform any receiving operations and / or certain obtaining operations described herein as being performed by the network node. Any information, data and / or signals may be received from a UE, another network node and / or any other network equipment. Similarly, antenna 1410, communication interface 1406, and / or processing circuitry 1402 may be configured to perform any transmitting operations described herein as being performed by the network node. Any information, data and / or signals may be transmitted to a UE, another network node and / or any other network equipment.

[0315] Power source 1408 provides power to the various components of network node 1400 in a form suitable for the respective components (e.g., at a voltage and current level needed for each respective component). Power source 1408 may further comprise, or be coupled to, power management circuitry to supply the components of network node 1400 with power for performing the functionality described herein. For example, network node 1400 may be connectable to an external power source (e.g., the power grid, an electricity outlet) via an input circuitry or interface such as an electrical cable, whereby the external power source supplies power to power circuitry of power source 1408. As a further example, power source 1408 may comprise a source of power in the form of a battery or battery pack which is connected to, or integrated in, power circuitry. The battery may provide backup power should the external power source fail.

[0316] Embodiments of network node 1400 may include additional components beyond those shown in Figure 14 for providing certain aspects of the network node’s functionality, including any of the functionality described herein and / or any functionality necessary to support the subject matter described herein. For example, network node 1400 may include user interface equipment to allow input of information into network node 1400 and to allow output of information from network node 1400. This may allow a user to perform diagnostic, maintenance, repair, and other administrative functions for network node 1400.

[0317] In some embodiments, network node 1400 may be configured to perform methods, procedures, or operations attributed to an NNF in the above descriptions of various embodiments (e.g., Figures 6-8 and 10). In some embodiments, network node 1400 may be configured to perform methods, procedures, or operations attributed to a data collection server in the above descriptions of various embodiments (e.g., Figures 6-7, 9, and 11).

[0318] Figure 15 is a block diagram illustrating a virtualization environment 1500 in which functions implemented by some embodiments may be virtualized. In the present context, virtualizing means creating virtual versions of apparatuses or devices which may include virtualizing hardware platforms, storage devices and networking resources. As used herein, virtualization can be applied to any device described herein, or components thereof, and relates to an implementation in which at least a portion of the functionality is implemented as one or more virtual components. Some or all of the functions described herein may be implemented as virtual components executed by one or more virtual machines (VMs) implemented in one or more virtual environments 1500 hosted by one or more of hardware nodes, such as a hardware computing device that operates as a network node, UE, core network node, or host. Further, in embodiments in which the virtual node does not require radio connectivity (e.g., a core network node or host), then the node may be entirely virtualized. In some embodiments, the virtualization environment 1500 includes components defined by the O-RAN Alliance, such as an O-Cloud environment orchestrated by a Service Management and Orchestration Framework via an O-2 interface.

[0319] Applications 1502 (which may alternatively be called software instances, virtual appliances, network functions, virtual nodes, virtual network functions, etc.) are run in the virtualization environment 1500 to implement some of the features, functions, and / or benefits of some of the embodiments disclosed herein. For example, one or more virtual nodes 1502 may be configured to perform methods, procedures, or operations attributed to an NNF in the above descriptions of various embodiments (e.g., Figures 6-8 and 10). As another example, one or more virtual nodes 1502 may be configured to perform methods, procedures, or operations attributed to a data collection server in the above descriptions of various embodiments (e.g., Figures 6-7, 9, and 11).

[0320] Hardware 1504 includes processing circuitry, memory that stores software and / or instructions (collected denoted computer program 1504a, which may be in the form of a computer program product) executable by hardware processing circuitry, and / or other hardware devices as described herein, such as a network interface, input / output interface, and so forth. Software may be executed by the processing circuitry to instantiate one or more virtualization layers 1506 (also referred to as hypervisors or virtual machine monitors (VMMs)), provide VMs 1508a and 1508b (one or more of which may be generally referred to as VMs 1508), and / or perform any of the functions, features and / or benefits described in relation with some embodiments described herein. Virtualization layer 1506 may present a virtual operating platform that appears like networking hardware to the VMs 1508.

[0321] VMs 1508 comprise virtual processing, virtual memory, virtual networking or interface and virtual storage, and may be run by a corresponding virtualization layer 1506. Different embodiments of the instance of a virtual appliance 1502 may be implemented on one or more of VMs 1508, and the implementations may be made in different ways. Virtualization of the hardware is in some contexts referred to as network function virtualization (NFV). NFV may be used to consolidate many network equipment types onto industry standard high volume server hardware, physical switches, and physical storage, which can be located in data centers, and customer premise equipment.

[0322] In the context of NFV, each VM 1508 may be a software implementation of a physical machine that runs programs as if they were executing on a physical, non-virtualized machine. Each VM 1508, and that part of hardware 1504 that executes that VM, be it hardware dedicated to that VM and / or hardware shared by that VM with others of the VMs, forms separate virtual network elements. Still in the context of NFV, a virtual network function is responsible for handling specific network functions that run in one or more VMs 1508 on top of the hardware 1504 and corresponds to the application 1502.

[0323] Hardware 1504 may be implemented in a standalone network node with generic or specific components. Hardware 1504 may implement some functions via virtualization. Alternatively, hardware 1504 may be part of a larger cluster of hardware (e.g., such as in a data center or CPE) where many hardware nodes work together and are managed via management and orchestration function 1510, which, among others, oversees lifecycle management of applications 1502. In some embodiments, hardware 1504 is coupled to one or more radio units that each include one or more transmitters and one or more receivers that may be coupled to one or more antennas. Radio units may communicate directly with other hardware nodes via one or more appropriate network interfaces and may be used in combination with the virtual components to provide a virtual node with radio capabilities, such as a radio access node or a base station. In some embodiments, some signaling can be provided with the use of a control system 1512 which may alternatively be used for communication between hardware nodes and radio units.

[0324] The foregoing merely illustrates the principles of the disclosure. Various modifications and alterations to the described embodiments will be apparent to those skilled in the art in view of the teachings herein. It will thus be appreciated that those skilled in the art will be able to devise numerous systems, arrangements, and procedures that, although not explicitly shown or described herein, embody the principles of the disclosure and can be thus within the spirit and scope of the disclosure. Various exemplary embodiments can be used together with one another, as well as interchangeably therewith, as should be understood by those having ordinary skill in the art.

[0325] The term unit, as used herein, can have conventional meaning in the field of electronics, electrical devices and / or electronic devices and can include, for example, electrical and / or electronic circuitry, devices, modules, processors, memories, logic solid state and / or discrete devices, computer programs or instructions for carrying out respective tasks, procedures, computations, outputs, and / or displaying functions, and so on, as such as those that are described herein.

[0326] Any appropriate steps, methods, features, functions, or benefits disclosed herein may be performed through one or more functional units or modules of one or more virtual apparatuses. Each virtual apparatus may comprise a number of these functional units. These functional units may be implemented via processing circuitry, which may include one or more microprocessor or microcontrollers, as well as other digital hardware, which may include Digital Signal Processor (DSPs), special -purpose digital logic, and the like. The processing circuitry may be configured to execute program code stored in memory, which may include one or several types of memory such as Read Only Memory (ROM), Random Access Memory (RAM), cache memory, flash memory devices, optical storage devices, etc. Program code stored in memory includes program instructions for executing one or more telecommunications and / or data communications protocols as well as instructions for carrying out one or more of the techniques described herein. In some implementations, the processing circuitry may be used to cause the respective functional unit to perform corresponding functions according to one or more embodiments of the present disclosure. As described herein, device and / or apparatus can be represented by a semiconductor chip, a chipset, or a (hardware) module comprising such chip or chipset; this, however, does not exclude the possibility that a functionality of a device or apparatus, instead of being hardware implemented, be implemented as a software module such as a computer program or a computer program product comprising executable software code portions for execution or being run on a processor. Furthermore, functionality of a device or apparatus can be implemented by any combination of hardware and software. A device or apparatus can also be regarded as an assembly of multiple devices and / or apparatuses, whether functionally in cooperation with or independently of each other. Moreover, devices and apparatuses can be implemented in a distributed fashion throughout a system, so long as the functionality of the device or apparatus is preserved. Such and similar principles are considered as known to a skilled person.

[0327] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs. It will be further understood that terms used herein should be interpreted as having a meaning that is consistent with their meaning in the context of this specification and the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.

[0328] In addition, certain terms used in the present disclosure, including the specification and drawings, can be used synonymously in certain instances (e.g., “data” and “information”). It should be understood that although such terms may be used synonymously herein, there may be instances such terms are not intended to be used synonymously.

[0329] Embodiments of the techniques and apparatus described herein also include, but are not limited to, the following enumerated examples:

[0330] Al . A method performed by a network node or function (NNF) configured to facilitate data collection in a communication network, the method comprising: receiving, from a data collection server, a request to initiate a data collection session in the communication network, wherein the request includes a session configuration for the data collection session; based on the session configuration, selecting one or more network entities from which to collect data and determining respective data collection configurations for the selected entities; and sending the respective data collection configurations to the selected network entities. Ala. The method of embodiment Al, wherein the requested data collection is associated with machine learning (ML) models hosted by one or more of the following: the NNF, at least one of the selected network entities, and the data collection server.

[0331] A2. The method of any of embodiments Al -A la, wherein the session configuration includes or identifies one or more of the following: geographic area where the data needs to be collected; duration of the data collection; positioning methods to be used for the data collection; measurements to be collected; security and / or privacy requirements for the data collection; and a recipient for the collected data.

[0332] A3. The method of any of embodiments A1-A2, wherein the session configuration includes an identifier (ID) associated with the data collection session, and the data collection session ID is included in or with the data collection configurations sent to the selected network entities.

[0333] A4. The method of any of embodiments Al -A3, wherein each data collection configuration includes or identifies one or more of the following: duration of the data collection; minimization of drive testing (MDT) configuration to be used for the data collection; positioning methods to be used for the data collection; measurements to be collected; assistance data for the data collection; transport configuration for reporting the collected data; and one or more data collection criteria.

[0334] A4a. The method of embodiment A4, wherein the one or more data collection criteria include one or more of the following: signal strength threshold, geometric dilution of precision (GDOP) threshold, user equipment (UE) speed threshold, UE environment type, and UE line-of-sight (LOS) or non-LOS signal conditions.

[0335] A4b. The method of any of embodiments A4-A4a, wherein each data transport configuration includes or indicates one or more of the following: whether control plane or user plane transport should be used; whether encryption and / or integrity protection should be used; whether the collected data in a report should be comprehensible by intermediate network entities through which the report is sent; quality -of-service requirements for any flow or radio bearer used to report the collected data; a particular flow or radio bearer on which the collected data should be reported; routing instructions for the collected data; and an Internet Protocol (IP) address to be used for reporting the collected data.

[0336] A5. The method of any of embodiments Al-A4b, wherein the selected network entities include one or more of the following: one or more user equipment (UE), one or more radio access network (RAN) nodes, and a user plane function (UPF).

[0337] A5a. The method of embodiment A5, wherein: the method further comprises receiving the following from a UE: a first indication of the UE’s capability for data collection, and a second indication of user consent for data collection; and the UE is selected as one of network entities based on the first and second indications.

[0338] A6. The method of any of embodiments Al-A5a, further comprising: receiving, from each of the selected network entities, a report that includes data collected according to the data collection configuration sent to the network entity; and sending at least a portion of the collected data in each report to one of the following recipients: the data collection server, a second data collection server, or a second NNF of the communication network.

[0339] A6a. The method of embodiment A6, wherein each report includes an identifier (ID) associated with the data collection session.

[0340] A6b. The method of embodiment A6a, further comprising, based on the data collection session ID included in each report, determining a first portion of the collected data in the report to retain and a second portion of the collected data in the report to send to the recipient.

[0341] A6c. The method of embodiment A6b, wherein determining the first and second portions is further based on whether the collected data in the report is comprehensible by the NNF. A6d. The method of any of embodiments A6a-A6c, wherein the recipient of the at least a portion of the collected data in each report is identified by one of the following: implicitly by the data collection session ID, and an explicitly by a recipient identifier in the session configuration.

[0342] A6e. The method of any of embodiments A6-A6d, further comprising receiving from the data collection server a further request to provide data collected for the data collection session, wherein the at least a portion of the collected data in each report is sent in response to the further request.

[0343] A7. The method of any of embodiments Al-A6e, further comprising: receiving from the data collection server a second request to stop or pause the data collection session; and sending respective commands to the selected network entities in accordance with the second request.

[0344] A8. The method of any of embodiments A1-A7, wherein the data collection server is one of the following: a further NNF of the communication network, or an application function (AF) or server external to the communication network.

[0345] A9. The method of any of embodiments A1-A8, wherein the NNF is one of the following: a location management function (LMF), a network data analytics function (NWDAF), or an operations / administration / maintenance (OAM) function.

[0346] Bl. A method performed by a data collection server for a communication network, the method comprising: sending, to a network node or function (NNF) of the communication network, a request to initiate a data collection session in the communication network, wherein the request includes a session configuration for the data collection session.

[0347] Bia. The method of embodiment Bl, wherein the requested data collection is associated with machine learning (ML) models hosted by one or more of the following: the NNF, at least one of the selected network entities, and the data collection server. B2. The method of any of embodiments B 1 -B la, wherein the session configuration includes or identifies one or more of the following: geographic area where the data needs to be collected; duration of the data collection; positioning methods to be used for the data collection; measurements to be collected; security and / or privacy requirements for the data collection; and a recipient for the collected data.

[0348] B2a. The method of embodiment B2, wherein the recipient for the collected data is one of the following: the data collection server, a second data collection server, or a second NNF of the communication network.

[0349] B3. The method of any of embodiments B 1 -B2a, wherein the session configuration includes an identifier (ID) associated with the data collection session.

[0350] B4. The method of any of embodiments B1-B3, further comprising receiving, from one or more network entities, respective reports that include data collected according to the session configuration sent to the network entity.

[0351] B4a. The method of embodiment B4, wherein the one or more network entities include one or more of the following: the NNF, one or more user equipment (UEs), one or more radio access network (RAN) nodes, and a user plane function (UPF).

[0352] B4b. The method of any of embodiments B4-B4a, wherein each report includes an identifier (ID) associated with the data collection session.

[0353] B4c. The method of any of embodiments B4-B4b, further comprising sending to the NNF a further request to provide data collected for the data collection session, wherein the one or more reports are received in response to the further request.

[0354] B5. The method of any of embodiments B 1 -B4c, further comprising sending to the NNF a second request to stop or pause the data collection session. B6. The method of any of embodiments B1-B5, wherein the data collection server is one of the following: a further network node or function (NNF) of the communication network, or an application function (AF) or server external to the communication network.

[0355] B7. The method of any of embodiments B1-B6, wherein the NNF is one of the following: a location management function (LMF), a network data analytics function (NWDAF), or an operations / administration / maintenance (OAM) function.

[0356] C 1. Network equipment arranged to implement a network node or function (NNF) configured to facilitate data collection in a communication network, the network equipment comprising: communication interface circuitry configured to communicate with the data collection server, with a radio access network (RAN) of the communication network, and with user equipment (UEs) via the RAN; and processing circuitry operably coupled to the communication interface circuitry, wherein the processing circuitry and the communication interface circuitry are configured to perform operations corresponding to any of the methods of embodiments Al- A9.

[0357] C2. Network equipment arranged to implement a network node or function (NNF) configured to facilitate data collection in a communication network, the network equipment being configured to perform operations corresponding to any of the methods of embodiments A1-A9.

[0358] C3. A non-transitory, computer-readable medium storing computer-executable instructions that, when executed by processing circuitry associated with a network node or function (NNF) configured to facilitate data collection in a communication network, configure the NNF to perform operations corresponding to any of the methods of embodiments A1-A9.

[0359] C4. A computer program product comprising computer-executable instructions that, when executed by processing circuitry associated with a network node or function (NNF) configured to facilitate data collection in a communication network, configure the NNF to perform operations corresponding to any of the methods of embodiments A1-A9. D 1. Network equipment arranged to implement a data collection server for a communication network, the network equipment comprising: communication interface circuitry configured to communicate with a network node or function (NNF) configured to facilitate data collection in a communication network; and processing circuitry operably coupled to the communication interface circuitry, wherein the processing circuitry and the communication interface circuitry are configured to perform operations corresponding to any of the methods of embodiments Bl- B7.

[0360] D2. Network equipment arranged to implement a data collection server for a communication network, the network equipment being configured to perform operations corresponding to any of the methods of embodiments B1-B7.

[0361] D3. A non-transitory, computer-readable medium storing computer-executable instructions that, when executed by processing circuitry associated with a data collection server for a communication network, configure the data collection server to perform operations corresponding to any of the methods of embodiments B1-B7.

[0362] D4. A computer program product comprising computer-executable instructions that, when executed by processing circuitry associated with a data collection server for a communication network, configure the data collection server to perform operations corresponding to any of the methods of embodiments B1-B7.

Claims

CLAIMS1 . A method performed by a network node or function, NNF, configured to facilitate data collection in a communication network, the method comprising: receiving (1020), from a data collection server, a request to initiate data collection by a user equipment (UE) in the communication network; determining (1030) a configuration for the data collection by the UE, wherein the configuration indicates user plane transport of the collected data to the NNF; and sending (1040) the determined configuration to the UE.

2. The method of claim 1, wherein the data collection is associated with machine learning, ML, models hosted by one or more of the following: the NNF, the UE, and the data collection server.

3. The method of any of claims 1-2, wherein the request includes or identifies one or more of the following: the UE; geographic area where the data needs to be collected; duration of the data collection; positioning methods to be used for the data collection; measurements to be collected; security and / or privacy requirements for the data collection; and a recipient for the collected data.

4. The method of any of claims 1-3, wherein the configuration also includes or indicates one or more of the following: duration of the data collection; minimization of drive testing, MDT, configuration to be used for the data collection; positioning methods to be used for the data collection; measurements to be collected; assistance data for the data collection; and one or more data collection criteria.

5. The method of claim 4, wherein the one or more data collection criteria include one or more of the following: signal strength threshold; geometric dilution of precision, GDOP,threshold; UE speed threshold; UE environment type; and UE line-of-sight, LOS, or non-LOS signal conditions.

6. The method of any of claims 1-5, wherein the configuration includes or indicates one or more of the following associated with the user plane transport: whether encryption and / or integrity protection should be used; whether the collected data in a report should be comprehensible by intermediate network entities through which the report is sent; quality -of-service requirements for any flow or radio bearer used to report the collected data; a particular flow or radio bearer on which the collected data should be reported; and an Internet Protocol, IP, address of the data collection server7. The method of any of claims 1-6, further comprising receiving (1005) from the UE a first indication of the UE’s capability for data collection, wherein the configuration is determined based on the first indication.

8. The method of any of claims 1-7, further comprising obtaining (1010), from a unified data management function, UDM, of the communication network, a second indication of data collection consent by a user associated with the UE, wherein the configuration is determined based on the second indication.

9. The method of any of claims 1-8, further comprising: receiving (1050), from the UE via the user plane, a report that includes data collected according to the configuration sent to the UE; and sending (1080) at least a portion of the collected data in the report to the data collection server.

10. The method of claim 9, wherein: the request includes an identifier, ID, associated with the data collection, and the ID is included in or with the configuration sent to the UE, and the ID is included in or with the report received from the UE.

11. The method of claim 10, further comprising, based on the ID included in or with the report, determining (1060) a first portion of the collected data in the report to retain and a second portion of the collected data in the report to send to the data collection server.

12. The method of claim 11, wherein determining (1060) the first and second portions is further based on whether the collected data in the report is comprehensible by the NNF.

13. The method of any of claims 10-12, wherein the ID included in or with the report indicates that the at least a portion of the collected data in the report should be sent to the data collection server.

14. The method of any of claims 9-13, further comprising receiving (1070) from the data collection server a further request to provide data collected in accordance with the request, wherein the at least a portion of the collected data in the report is sent in response to the further request.

15. The method of any of claims 1-14, further comprising: receiving (1090) from the data collection server a second request to stop or pause the data collection by the UE; and sending (1095) a stop or pause command to the UE in accordance with the second request.

16. The method of any of claims 1-15, wherein the data collection server is one of the following: a further NNF of the communication network; or an application function, AF, or server external to the communication network.

17. The method of any of claims 1-16, wherein the NNF is one of the following: a location management function, LMF, a network data analytics function, NWDAF; or an operations / administration / maintenance, 0AM, function.

18. A method performed by a data collection server for a communication network, the method comprising: sending (1110), to a network node or function, NNF, of the communication network, a request to initiate data collection by a user equipment, UE, in the communication network; andreceiving (1130), from the NNF, a report that includes data collected by the UE in accordance with the request.

19. The method of claim 18, wherein the data collection is associated with machine learning, ML, models hosted by one or more of the following: the NNF, the UE, and the data collection server.

20. The method of any of claims 18-19, wherein the request includes or identifies one or more of the following: the UE; geographic area where the data needs to be collected; duration of the data collection; positioning methods to be used for the data collection; measurements to be collected; security and / or privacy requirements for the data collection; and a recipient for the collected data.

21. The method of any of claims 18-20, wherein the request includes an identifier, ID, associated with the data collection, and the ID is included in or with the received report.

22. The method of any of claims 18-21, further comprising sending (1120) to the NNF a further request to provide data collected in accordance with the request, wherein the report is received in response to the further request.

23. The method of any of claims 18-22, wherein the report is received from the NNF via user plane transport.

24. The method of any of claims 18-23, further comprising sending (1140) to the NNF a second request to stop or pause the data collection by the UE.

25. The method of any of claims 18-24, wherein the data collection server is one of the following: a further NNF of the communication network; or an application function, AF, or server external to the communication network.

26. The method of any of claims 18-25, wherein the NNF is one of the following: a location management function, LMF, a network data analytics function, NWDAF; or an operations / administration / maintenance, OAM, function.

27. Network equipment (1400, 1500) arranged to implement a network node or function, NNF (210, 220, 440, 630, 730, 1208, 1218) configured to facilitate data collection in a communication network (200, 1202), the network equipment comprising: communication interface circuitry (1406, 1504) configured to communicate with a data collection server (250, 640, 740, 1208, 1216) and with user equipment, UEs (240, 410, 610, 710, 1212); and processing circuitry (1402, 1404) operably coupled to the communication interface circuitry, wherein the processing circuitry and the communication interface circuitry are configured to: receive, from the data collection server, a request to initiate data collection by a UE in the communication network; determine a configuration for the data collection by the UE, wherein the configuration indicates user plane transport of the collected data to the NNF; and send the determined configuration to the UE.

28. The network equipment of claim 27, wherein the processing circuitry and the communication interface circuitry are further configured to perform operations corresponding to any of the methods of claims 2-17.

29. Network equipment (1400, 1500) arranged to implement a network node or function, NNF (210, 220, 440, 630, 730, 1208, 1218) configured to facilitate data collection in a communication network (200, 1202), the network equipment being configured to: receive, from a data collection server (250, 640, 740, 1208, 1216), a request to initiate data collection by a user equipment, UE (240, 410, 610, 710, 1212) in the communication network; determine a configuration for the data collection by the UE, wherein the configuration indicates user plane transport of the collected data to the NNF; and send the determined configuration to the UE.

30. The network equipment of claim 29, being further configured to perform operations corresponding to any of the methods of claims 2-17.

31. Non-transitory, computer-readable medium (1404, 1504) storing computer-executable instructions that, when executed by processing circuitry (1402, 1504) associated with a network node or function, NNF (210, 220, 440, 630, 730, 1208, 1218) configured to facilitate data collection in a communication network (200, 1202), configure the NNF to perform operations corresponding to any of the methods of claims 1-17.

32. Computer program product (1404a, 1504a) comprising computer-executable instructions that, when executed by processing circuitry (1402, 1504) associated with a network node or function, NNF (210, 220, 440, 630, 730, 1208, 1218) configured to facilitate data collection in a communication network (200, 1202), configure the NNF to perform operations corresponding to any of the methods of claims 1-17.

33. Network equipment (1300, 1400, 1500) arranged to implement a data collection server (250, 640, 740, 1208, 1216) for a communication network (200, 1202), the network equipment comprising: communication interface circuitry (1306, 1308, 1406, 1504) configured to communicate with a network node or function, NNF (210, 220, 440, 630, 730, 1208, 1218) of the communication network; and processing circuitry (1302, 1402, 1504) operably coupled to the communication interface circuitry, wherein the processing circuitry and the communication interface circuitry are configured to: send, to the NNF, a request to initiate data collection by a user equipment, UE (240, 410, 610, 710, 1212) in the communication network; and receive, from the NNF, a report that includes data collected by the UE in accordance with the request.

34. The network equipment of claim 33, wherein the processing circuitry and the communication interface circuitry are further configured to perform operations corresponding to any of the methods of claims 19-26.

35. Network equipment (1300, 1400, 1500) arranged to implement a data collection server (250, 640, 740, 1208, 1216) for a communication network (200, 1202), the network equipment being configured to: send, to a network node or function, NNF (210, 220, 440, 630, 730, 1208, 1218) of the communication network, a request to initiate data collection by a user equipment, UE (240, 410, 610, 710, 1212) in the communication network; and receive, from the NNF, a report that includes data collected by the UE in accordance with the request.

36. The network equipment of claim 35, being further configured to perform operations corresponding to any of the methods of claims 19-26.

37. Non-transitory, computer-readable medium (1312, 1404, 1504) storing computerexecutable instructions that, when executed by processing circuitry (1302, 1402, 1504) associated with a data collection server (250, 640, 740, 1208, 1216) for a communication network (200, 1202), configure the data collection server to perform operations corresponding to any of the methods of claims 18-26.

38. Computer program product (1314, 1404a, 1504a) comprising computer-executable instructions that, when executed by processing circuitry (1102, 1402, 1504) associated with a data collection server (250, 640, 740, 1008, 1016) for a communication network (200, 1202), configure the data collection server to perform operations corresponding to any of the methods of claims 18-26.

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