WO2023217557A1

WO2023217557A1 - Artificial intelligence/machine learning (ai/ml) translator for 5g core network (5gc)

Info

Publication number: WO2023217557A1
Application number: PCT/EP2023/061292
Authority: WO
Inventors: Jing Yue; Zhang FU; Antonio Iniesta Gonzalez; Belen Pancorbo Marcos; Ulf Mattsson; Stefan HÅKANSSON
Original assignee: Telefonaktiebolaget Lm Ericsson (Publ)
Priority date: 2022-05-13
Filing date: 2023-04-28
Publication date: 2023-11-16

Abstract

The specification provides for a method for an artificial intelligence/machine learning translator function (AIML-T) of a communication network. This method comprises: receiving one or more requests for AI/ML assistance information for one or more nodes that are endpoints for application layer AI/ML operations; translating the one or more requests for AI/ML assistance information into one or more requests for information and/or analytics from the communication network; sending the one or more requests for information and/or analytics to one or more network functions (NTs) of the communication network; receiving the requested information and/or analytics from the one or more NFs; translating the received information and/or analytics into the requested AI/ML assistance information; and sending the requested AI/ML assistance information to one of the following: the one or more nodes; or an application function (AT) of the communication network. Apart from that, the specification also provides for an artificial intelligence/machine learning translator function (AIML-T) of a communication network, wherein: the AIML-T is implemented by communication interface circuitry and processing circuitry that are operably coupled; and the processing circuitry and interface circuitry are configured to perform operations corresponding to any of the above methods.

Description

ARTIFICIAL INTELLIGENCE/MACHINE LEARNING (AI/ML) TRANSLATOR FOR 5G CORE NETWORK (5GC)

TECHNICAL FIELD

The present application relates generally to the field of communication networks, and more specifically to techniques for applying application layer artificial intelligence/machine learning (AI/ML) to information and/or data analytics provided by a 5G core (5GC) network.

INTRODUCTION

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). 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.

At a high level, the 5G System (5GS) consists of an Access Network (AN) and a Core Network (CN). The AN provides UEs connectivity to the CN, e.g., via base stations such as gNBs or ng-eNBs described below. The CN includes a variety of Network Functions (NF) that provide a wide range of different functionalities such as session management, connection management, charging, authentication, etc.

Figure 1 illustrates a high-level view of an exemplary 5G network architecture, consisting of a Next Generation Radio Access Network (NG-RAN) 199 and a 5G Core (5GC) 198. NG-RAN 199 can include one or more gNodeB’s (gNBs) connected to the 5GC via one or more NG interfaces, such as gNBs 100, 150 connected via interfaces 102, 152, respectively. More specifically, gNBs 100, 150 can be connected to one or more Access and Mobility Management Functions (AMFs) in the 5GC 198 via respective NG-C interfaces. Similarly, gNBs 100, 150 can be connected to one or more User Plane Functions (UPFs) in 5GC 198 via respective NG-U interfaces. Various other network functions (NFs) can be included in the 5GC 198, as described in more detail below.

In addition, the gNBs can be connected to each other via one or more Xn interfaces, such as Xn interface 140 between gNBs 100 and 150. The radio technology for the NG-RAN is often referred to as “New Radio” (NR). With respect the NR interface to UEs, each of the gNBs can support frequency division duplexing (FDD), time division duplexing (TDD), or a combination thereof. Each of the gNBs can serve a geographic coverage area including one more cells and, in some cases, can also use various directional beams to provide coverage in the respective cells. NG-RAN 199 is layered into a Radio Network Layer (RNL) and a Transport Network Layer (TNL). The NG-RAN architecture, i.e., the NG-RAN logical nodes and interfaces between them, is defined as part of the RNL. For each NG-RAN interface (NG, Xn, Fl) the related TNL protocol and the functionality are specified. The TNL provides services for user plane transport and signaling transport. In some exemplary configurations, each gNB is connected to all 5GC nodes within an “AMF Region” with the term “AMF” referring to an access and mobility management function in the 5GC.

The NG RAN logical nodes shown in Figure 1 include a Central Unit (CU or gNB-CU) and one or more Distributed Units (DU or gNB-DU). For example, gNB 100 includes gNB-CU 110 and gNB-DUs 120 and 130. CUs (e.g., gNB-CU 110) are logical nodes that host higher-layer protocols and perform various gNB functions such controlling the operation of DUs. A DU (e.g., gNB-DUs 120, 130) is a decentralized logical node that hosts lower layer protocols and can include, depending on the functional split option, various subsets of the gNB functions. As such, each of the CUs and DUs can include various circuitry needed to perform their respective functions, including processing circuitry, transceiver circuitry (e.g., for communication), and power supply circuitry.

A gNB-CU connects to one or more gNB-DUs over respective Fl logical interfaces, such as interfaces 122 and 132 shown in Figure 1. However, a gNB-DU can be connected to only a single gNB-CU. The gNB-CU and connected gNB-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.

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 (SB A) in which Network Functions (NFs) provide one or more services to one or more service consumers. This can be done, for example, by Hyper Text Transfer Protocol/Representational State Transfer (HTTP/REST) application programming interfaces (APIs). In general, the various services are self-contained functionalities that can be changed and modified in an isolated manner without affecting other services.

Furthermore, the services are composed of various “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 5G SBA, network repository functions (NRF) allow every network function to discover the services offered by other network functions, and Data Storage Functions (DSF) allow every network function to store its context. This 5G SBA model is based on principles including modularity, reusability and self-containment of NFs, which can enable network deployments to take advantage of the latest virtualization and software technologies. Machine learning (ML) 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 its accuracy. ML algorithms build models based on sample (or “training”) data, with the models being used subsequently to make predictions or decisions. ML algorithms 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.

3GPP TR 22.874 (vl 8.2.0) specifies that 5GS can support three different types of AI/ML operations: AI/ML operation splitting between AI/ML endpoints; AI/ML model/data distribution and sharing over 5GS; and distributed/Federated Learning over 5GS. 3GPP TS 22.261 (vl8.6.0) specifies that 5GS should support AI/ML-based services. For example, based on operator policy, 5GS shall provide an indication about a planned change of bitrate, latency, or reliability for a quality-of-service (QoS) flow to an authorized third party so that an AI/ML application of the third party can adjust application layer behavior if time allows.

Based on these documents, 3GPP TR 23.700-80 (v0.2.0) describes how AI/ML service providers can leverage 5GS as a platform to provide the intelligent transmission support for application layer AI/ML operation based on various objectives. 3GPP TR 23.700-80 (v0.2.0) also identifies the following issues to be studied:

■ What are the information or data analytics that can be provided by 5GC to a UE to assist Application AI/ML operation at the UE?

■ How does the 5GC exposes such information or data analytics to the UE?

■ What assistance information and events are exposed by 5GS to an application function

(AF) that participates in the AI/ML operation? How is such information exposed?

SUMMARY

To address the above-listed issues, it is important to clarify the interaction between 5GC and AI/ML endpoints, including the UE and the authorized third party (e.g., AF). For example, there is a need to define how 5GC receives requirements on assistance information for application-layer AI/ML operations from the UE and/or from the authorized third party, and how 5GC exposes the required assistance information to the UE and/or the authorized third party. Additionally, there is a need to define what information or analytics produced in 5GC could be used to generate the assistance information required for the application-layer AI/ML operations at the UE and/or the authorized third party (e g., AF). Additionally, there is a need to define mapping of requests from the UE or the authorized third party to requests to the 5GC, and how to map or convert information or analytics produced in 5GC to assistance information required by the UE and/or the authorized third party.

Embodiments of the present disclosure address these and other problems, issues, and/or difficulties, thereby facilitating the otherwise-advantageous deployment of application layer AI/ML that utilizes network information/analytics.

Some embodiments of the present disclosure include methods (e.g., procedures) for an artificial intelligence/machine learning translator function (AIML-T) of a communication network (e.g., 5GC). These exemplary methods can include receiving one or more requests for AI/ML assistance information for one or more nodes that are endpoints for application layer AI/ML operations. These exemplary methods can also include translating the one or more requests for AI/ML assistance information into one or more requests for information and/or analytics from the communication network These exemplary methods can also include sending the one or more requests for information and/or analytics to one or more network functions (NFs) of the communication network.

These exemplary methods can also include receiving the requested information and/or analytics from the one or more NFs and translating the received information and/or analytics into the requested AI/ML assistance information. These exemplary methods can also include sending the requested AI/ML assistance information to one of the following: the one or more nodes; or an application function (AF) of the communication network.

In some embodiments, the one or more nodes include one or more of the following: one or more user equipment (UEs), and one or more authorized third party servers.

In some embodiments, translating the one or more requests for AI/ML assistance information can include the following operations: determining the information and/or analytics to be requested based on the requests for AI/ML assistance information; selecting or discovering the one or more NFs based on capabilities to provide the information and/or analytics; and creating the one or more requests for information and/or analytics based on respective service operations of the one or more NFs.

In some of these embodiments, the one or more requests for information and/or analytics are sent to the one or more NFs via a network exposure function (NEF) of the communication network. The requested information and/or analytics is also received via the NEF.

In some embodiments, the AIML-T is part of a NEF of the communication network. These embodiments can make use of existing NEF capabilities to translate requests from external applications to 5GC services and/or service operations.

In some embodiments, the one or more NFs include any of the following: network data analytics function (NWDAF), session management function (SMF), access and mobility management function (AMF), data collection coordination function (DCCF), analytics data repository function (ADRF). The list of applicable types of NF is not exhaustive, however. That is, the one or more NFs may also include other types of NF in 5GC

In some embodiments, the AIML-T is part of an AF of the communication network that provides data collection and/or AI/ML operational assistance. In other embodiments, one or more of the following applies:

• the one or more requests for AI/ML assistance information are received from the AF, and the requested AI/ML assistance information to the one or more nodes is sent to the one or more nodes via the AF; and

• the one or more requests for information and/or analytics are sent to the one or more NFs via the AF, and the requested information and/or analytics are received from the one or more NFs via the AF.

In other embodiments, the AIML-T is part of a NEF of the communication network. The one or more requests for AI/ML assistance information are received from the AF as respective Nnef_EventExposure_Subscribe or other service operations. The requested AI/ML assistance information is sent to the AF using one or more Nnef_EventExposure_Notify service or other operations.

In other embodiments, the one or more NFs include first and second NFs, with the AIML- T being part of the first NF. A first portion of the requested information and/or analytics is computed or obtained by the first NF. A request for information and/or analytics is sent to the second NF and a second portion of the requested information and/or analytics is received from the second NF. In some of these embodiments, the one or more requests for AI/ML assistance information are received from the one or more nodes that are endpoints for AI/ML operations. In other of these embodiments, the one or more requests for AI/ML assistance information are received from the AF of the communication network.

In some embodiments, each request for AI/ML assistance information includes one or more of the following:

• type of application layer AI/ML operation for which AI/ML assistance information is requested;

• an identifier of the requesting node;

• type of AI/ML assistance information needed; and

• time window or interval for which AI/ML assistance information is requested.

In some of these embodiments, the type of application layer AI/ML operation is one of the following: split or distributed AI/ML operation, distribution or sharing of AI/ML models or data, and federated learning (FL). In some of these embodiments, the request for AI/ML assistance information also includes one or more of the following parameters related to sending or receiving data to be used in the application layer AI/ML operation:

• one or more data rates;

• one or more thresholds for data transmission reliability, which could be per-data type or for all data types;

• one or more thresholds for communication service availability;

• one or more time periods of interest; and

• one or more areas of interest.

In some of these embodiments, the application layer AI/ML operation includes a plurality of transaction rounds among the endpoints, and each of the parameters included in the request is representative of one of the following: all of the transaction rounds, or respective individual transaction rounds.

In some of these embodiments, the request identifies a plurality of data types comprising the AI/ML assistance information, and the one or more thresholds for data transmission reliability include one of the following: a single threshold associated with all of the data types; or a plurality of thresholds, with each threshold associated with a different one of the data types.

In some embodiments, the AI/ML assistance information includes one or more of the following:

• first predictions about conditions in the communication network such as latency, bitrate, communication service availability, and reliability;

• second predictions about changes to the conditions in the communication network;

• third predictions about need for downloading ML models and/or data to be used with ML models existing in the endpoints;

• information about resource utilization in the communication network; and

• one or more time windows associated with one or more of the following that are present in the AI/ML assistance information: the first predictions, the second predictions, the third predictions, and the information about resource utilization.

Other embodiments include NEFs (or network nodes hosting the same) of a communication network, wherein the NEFs comprise AIML-Ts that are configured to perform the operations corresponding to any of the exemplary methods described herein. Other embodiments include AIML-Ts (or network nodes hosting the same) that are configured to perform the operations corresponding to any of the exemplary methods described herein. Other embodiments also include non-transitory, computer-readable media storing computerexecutable instructions that, when executed by processing circuitry, configure such AIML-Ts to perform operations corresponding to any of the exemplary methods described herein. These and other disclosed embodiments expose required assistance information to a UE and/or an authorized third party, thereby facilitating application layer AI/ML operations by these entities. Moreover, embodiments eliminate the need for other NFs in 5GC to understand application layer AI/ML requests and corresponding assistance information. Rather, 5GC NFs can derive and produce information/analytics based on 5GC-compatible requests. This reduces complexity and increases flexibility for 5GC to assist application layer AI/ML operations performed by UE (e g., application client) and authorized third party (e.g., application server). More generally, embodiments facilitate deployment of application layer AI/ML that relies on information from a communication network (e g., 5GC), which can improve performance of applications that communicate via the communication network.

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

BRIEF DESCRIPTION OF THE DRAWINGS

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

Figure 3 shows an exemplary AI/ML inference split between UE and network.

Figure 4 shows an exemplary arrangement for AI/ML model downloading over 5GS.

Figure 5 shows an exemplary federated learning arrangement based on 5GS.

Figures 6-10 show various block diagrams of interactions between AI/ML endpoints, NF s in the 5GC, and an AI/ML translator function (AIML-T), according to various embodiments of the present disclosure.

Figures 11A-B and 12-14 show signaling diagrams of various procedures for translation from AI/ML requests to 5GC requests and from analytics to AI/ML assistance information, and for assistance information exposure, according to various embodiments of the present disclosure.

Figure 15 shows an exemplary method e.g., procedure) for an AIML-T of a communication network, according to various embodiments of the present disclosure.

Figure 16 shows a communication system according to various embodiments of the present disclosure.

Figure 17 shows a UE according to various embodiments of the present disclosure.

Figure 18 shows a network node according to various embodiments of the present disclosure.

Figure 19 shows a host computing system according to various embodiments of the present disclosure. Figure 20 is a block diagram of a virtualization environment in which functions implemented by some embodiments of the present disclosure may be virtualized.

Figure 21 illustrates communication between a host computing system, a network node, and a UE via multiple connections, according to various embodiments of the present disclosure.

DETAILED DESCRIPTION

Embodiments briefly summarized above will now be described more fully with reference to the accompanying drawings. These descriptions are provided by way of example to explain the subject matter to those skilled in the art and should not be construed as limiting the scope of the subject matter to only the embodiments described herein. More specifically, examples are provided below that illustrate the operation of various embodiments according to the advantages discussed above.

Generally, all terms used herein are to be interpreted according to their ordinary meaning in the relevant technical field, unless a different meaning is clearly given and/or is implied from the context in which it is used. 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. The steps of any methods and/or procedures disclosed herein do not have to be performed in the exact order disclosed, unless a step is explicitly described as following or preceding another step and/or where it is implicit that a step must follow or precede another step. Any feature of any of the embodiments disclosed herein can be applied to any other embodiment, wherever appropriate. Likewise, any advantage of any of the embodiments can apply to any other embodiments, and vice versa. Other objects, features and advantages of the disclosed embodiments will be apparent from the following description.

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

• 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) of a cellular communications network 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., a New Radio (NR) base station (gNB) in a 3GPP Fifth Generation (5G) NR network or an enhanced or evolved Node B (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 (or component thereof such as MT or DU), a transmission point, a remote radio unit (RRU or RRH), and a relay node. • 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 Packet Data Network Gateway (P-GW), etc. A core network node can also be a node that implements a particular core network function (NF), such as an access and mobility management function (AMF), a session management function (AMF), a user plane function (UPF), a Service Capability Exposure Function (SCEF), or the like.

• Wireless Device: As used herein, a “wireless device” (or “WD” for short) is any type of device that has access to (i.e., is served by) a cellular communications network by communicating 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 “user equipment” (or “UE” for short). Some examples of a wireless device include, but are not limited to, smart phones, mobile phones, cell phones, voice over IP (VoIP) phones, wireless local loop phones, desktop computers, personal digital assistants (PDAs), wireless cameras, gaming consoles or devices, music storage devices, playback appliances, wearable devices, wireless endpoints, mobile stations, tablets, laptops, laptop-embedded equipment (LEE), laptop-mounted equipment (LME), smart devices, wireless customer-premise equipment (CPE), mobile-type communication (MTC) devices, Internet-of-Things (loT) devices, vehicle-mounted wireless terminal devices, mobile terminals (MTs), etc.

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

• 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.

• Node: As used herein, the term “node” (without any prefix) can be any type of node that is capable of operating in or with a wireless network (including a RAN and/or a core network), including a radio access node (or equivalent term), core network node, or wireless device.

• Service: As used herein, the term “service” refers generally to a set of data, associated with one or more applications, that is to be transferred via a network with certain specific delivery requirements that need to be fulfilled in order to make the applications successful.

• Component: As used herein, the term “component” refers generally to any component needed for the delivery of a service. Examples of component are RANs (e.g, E-UTRAN, NG-RAN, or portions thereof such as eNBs, gNBs, base stations (BS), etc. CNs (e.g., EPC, 5GC, or portions thereof, including all type of links between RAN and CN entities), and cloud infrastructure with related resources such as computation, storage. In general, each component can have a “manager”, which is an entity that can collect historical information about utilization of resources as well as provide information about the current and the predicted future availability of resources associated with that component (e.g., a RAN manager).

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 generally used. However, the concepts disclosed herein are not limited to a 3GPP system. Other wireless systems, including without limitation Wide Band Code Division Multiple Access (W CDMA), Worldwide Interoperability for Microwave Access (WiMax), Ultra Mobile Broadband (UMB) and Global System for Mobile Communications (GSM), may also benefit from the concepts, principles, and/or embodiments described herein.

In addition, functions and/or operations described herein as being performed by a wireless device or a network node may be distributed over a plurality of wireless devices and/or network nodes. Furthermore, although the term “cell” is used herein, it should be understood that (particularly with respect to 5G NR) beams may be used instead of cells and, as such, concepts described herein apply equally to both cells and beams.

Figure 2 shows an exemplary non-roaming architecture of a 5G network (200) with service-based interfaces. This architecture includes the following 3 GPP-defined NF s:

• Application Function (AF, 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.

• 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.

User Plane Function (UPF)- supports handling of 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 (e.g., NG-RNA) 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.

• Session Management Function (SMF, with Nsmf interface) interacts with the decoupled traffic (or user) plane, including creating, updating, and removing Protocol Data Unit (PDU) sessions and managing session context with the User Plane Function (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.

• Charging Function (CHF, with Nchf interface) is 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.

Access and Mobility Management Function (AMF, with Namf interface) terminates the RAN CP interface and handles all mobility and connection management of UEs (similar to MME in EPC). AMFs communicate with UEs via the N1 reference point and with the RAN (e.g., NG-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 NF s 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. • Network Repository Function (NRF) with Nnrf interface - provides service registration and discovery, enabling NFs to identify appropriate services available from other NFs.

• Network Slice Selection Function (NSSF) with Nnssf interface - 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. The NSSF enables other NFs (e.g., AMF) to identify a network slice instance that is appropriate for a UE’ s desired service.

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

• Network Data Analytics Function (NWDAF) with Nnwdaf interface - provides network analytics reports (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. Any NF can obtain analytics from an NWDAF using a DCCF and associated Ndccf services. The NWDAF can also perform storage and retrieval of analytics information from an Analytics Data Repository Function (ADRF).

• 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.

• Location Management Function (LMF) 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.

The Unified Data Management (UDM) function supports generation of 3 GPP authentication credentials, user identification handling, access authorization based on subscription data, and other subscriber-related functions. To provide this functionality, the UDM uses subscription data (including authentication data) stored in the 5GC unified data repository (UDR). In addition to the UDM, the UDR supports storage and retrieval of policy data by the PCF, as well as storage and retrieval of application data by NEF. The NRF allows every NF to discover the services offered by other NFs, and Data Storage Functions (DSF) allow every NF to store its context. In addition, the NEF provides exposure of capabilities and events of the 5GC to AFs within and outside of the 5GC. For example, NEF provides a service that allows an AF to provision specific subscription data (e.g., expected UE behavior) for various UEs.

Communication links between the UE and a 5G network (AN and CN) can be grouped in two different strata. The UE communicates with the CN over the Non-Access Stratum (NAS), and with the AN over the Access Stratum (AS). All the NAS communication takes place between the UE and the AMF via the NAS protocol (N1 interface in Figure 2). Security for the communications over this these strata is provided by the NAS protocol (for NAS) and the PDCP protocol (for AS).

As briefly mentioned above, 3GPP TR 22.874 (vl8.2.0) specifies that 5GS can support three different types of AI/ML operations: AI/ML operation (e.g., inference) split between AI/ML endpoints; AI/ML model/data distribution and sharing over 5GS; and di stributed/F ederated Learning over 5GS. Figure 3 shows an exemplary AI/ML inference split between UE and network. In this exemplary arrangement, the AI/ML operation/model is split into multiple parts according to the current task and environment. The intention is to offload the computation-intensive and energy-consuming parts to an endpoint in the network, while leaving the privacy- and delay-sensitive parts in the UE. The UE endpoint executes the AI/ML operation/model up to a specific part (or layer) and then sends the intermediate data to the corresponding network endpoint, which executes the remaining parts (or layers) and returns inference results to the UE.

Figure 4 shows an exemplary arrangement for AI/ML model downloading over 5GS. A multi-function UE may need to switch AI/ML models in response to variations in task and/or environment, based on models that are available for the UE. Assuming AI/ML models will become increasingly diverse and that the UE has limited storage resources, not all candidate AI/ML models will be pre-loaded to the UE. Instead, downloading can be used to distribute AI/ML models from network based on UE needs. For this purpose, the model performance at the UE needs to be monitored.

Figure 5 shows an exemplary federated learning (FL) arrangement based on 5GS. The cloud server trains a global model by aggregating local models partially-trained by each UE. UEs perform the training based on AI/ML model downloaded from the Al server, using locally stored (or available) training data. A UE reports its interim training results to the server via 5G network. The server aggregates the interim training results from the respective UEs and updates the global AI/ML model accordingly. The updated global model is then distributed back to the UEs, which can then perform the next training iteration.

As briefly mentioned above, 3GPP TS 22.261 (vl8.6.0) specifies that 5GS should support AI/ML-based services. For example, based on operator policy, the 5GS shall allow an authorized third-party to monitor resource utilization of the network service associated with the third-party. Note that resource utilization in this context refers to measurements relevant to a UE’s performance, such as data throughput provided by the network to the UE.

Furthermore, based on operator policy, 5GS shall provide an indication about a planned change of bitrate, latency, or reliability for a quality-of-service (QoS) flow to an authorized third party so that an AI/ML application of the third party can adjust application layer behavior if time allows. The indication shall provide expected time and location of the change, as well as target QoS parameters. The 5G system shall expose aggregated QoS parameter values for a group of UEs to an authorized third party and enable the authorized third party to change aggregated QoS parameter values associated with the group of UEs.

Also, based on operator policy, 5GS shall provide means to predict and expose predicted network condition (e.g., of bitrate, latency, reliability) changes per UE, to the authorized third party. Subject to user consent, operator policy, and regulatory constraints, 5GS shall expose monitoring and status information of an AI/ML session to a third-party AI/ML application. For example, this can be used by the AI/ML application to determine an in-time transfer of an AI/ML model.

Additionally, the 5GS shall provide alerting about events (e.g., traffic congestion, UE moving into/out of a different geographical area, etc.) to authorized third parties, together with predicted time of the event. For example, a third-party AI/ML application may use the prediction information to minimize disturbance in the transfer of learning data and AI/ML model data.

As briefly mentioned above, 3GPP TR 23.700-80 (v0.2.0) describes how AI/ML service providers can leverage 5GS as a platform to provide the intelligent transmission support for application layer AI/ML operation. One objective is to study the possible architectural and functional extensions to support the application layer AI/ML operations defined in 3GPP TS 22.261 (vl8.6.0), specifically:

• Monitoring of network resource utilization in the 5G system relevant to the UE in order to support application layer AI/ML operation.

• Whether and how to extend 5GS information exposure for 5GC NF(s) to expose UE and/or network conditions and performance prediction (e.g., location, QoS, load, congestion, etc.) to the UE and/or to the authorized 3^rd party to assist the application layer AI/ML operation. • Enhancements of external parameter provisioning to 5GC (e.g., expected UE activity behavior, expected UE mobility, etc.) based on application layer AI/ML operation.

• Enhancements of other 5GC features that could be used to assist the application layer AI/ML operations as described in 3GPP TS 22.261 (V18.6.0) section 6.40.

Another objective is to study possible QoS policy enhancements needed to support application layer AI/ML operational traffic while supporting other user traffic in the 5GS. Another objective is to study whether and how 5GS can provide assistance to AF and the UE for the AF and UE to manage FL and model distribution/redistribution (i.e., FL members selection, group performance monitoring, adequate network resources allocation and guarantee, etc.) to facilitate collaborative application layer AI/ML-based FL operation between application servers and application clients running on UEs.

3GPP TR 23.700-80 (v0.2.0) also identifies the following key issues (KI) to be studied:

1. What are the information or data analytics that can be provided by 5GC to a UE to assist Application AI/ML operation at the UE?

2. How does the 5GC exposes such information or data analytics to the UE?

3. What assistance information and events are exposed by 5GS to an application function (AF) that participates in the AI/ML operation? How is such information exposed?

To address the above-listed KIs, it is important to clarify the interaction between 5GC and AI/ML endpoints, including the UE and the authorized third party (e.g., AF). For example, there is a need to define how 5GC receives requests for assistance information for applicationlayer AI/ML operations from the UE and/or from the authorized third party, and how 5GC exposes the required assistance information to the UE and/or the authorized third party. Additionally, there is a need to define what information or analytics produced in 5GC could be used to generate the assistance information required for the application-layer AI/ML operations at the UE and/or the authorized third party (e.g., AF). Additionally, there is a need to define mapping of requests from the UE or the authorized third party to requests to the 5GC, and how to map or convert information or analytics produced in 5GC to assistance information required by the UE and/or the authorized third party.

3GPP TR 23.700-80 (V0.2.0) also proposes various solutions (labelled 1-7) for the KIs. For example, for KI2, solutions 2 and 4 provide analytics and information on the 5GS performance to the UE via an AF, while solutions 3 and 5 provide such analytics and information to the UE via SMF and non-access stratum (NAS) signaling. As another example, for KI#3, solution 6 enables the AF to request 5GS assistance information and analytics and solution 7 enables the AF requests to monitor bitrates to be able to select the AI/ML model to execute. Applicants have recognized a problem with proposed solutions 1-7, namely that different mechanisms are required in the AF to map application layer AI/ML requests received from UE or authorized third party into requests for 5GC, even though both types of requests are fairly similar.

Embodiments of the present disclosure address these and other problems, issues, and/or difficulties by providing an assistance function for AI/ML and FL in 5GC, which will be referred to as AI/ML translator (AIML-T). This function is responsible for translating (or mapping) the application layer AI/ML requests received from a UE or from an authorized third party to requests sent to 5GC, i.e , from AI/ML request to 5GC request AIML-T also converts information or analytics produced by 5GC to AI/ML assistance information sent to the UE and/or to the authorized third party, i.e., from 5GC information/analytics to AI/ML assistance information.

In various embodiments, AIML-T can be standalone or integrated into AF, with AF(AIML-T) denoting an AF containing AIML-T or with capability to access and control AIML-T. Alternatively, AIML-T can be integrated into NEF or other 5GC NFs such as SMF, NWDAF, etc. The notation of NEF(AIML-T) and NF (AIML-T) are used to denote the NEF or other NF that includes or has capability to access and control the AIML-T. In some embodiments, AIML-T can be upgrade and update according to new types of AI/ML requests and assistance information that are needed for application layer AI/ML operations.

Embodiments can provide various benefits and/or advantages. For example, by exposing required assistance information to the UE and/or to the authorized third party, the AIML-T facilitates application layer AI/ML operations by these entities. As another example, AIML-T removes the need for other NFs in 5GC to understand application layer AI/ML requests and corresponding assistance information. Rather, 5GC NFs (e.g., NWDAF) can derive and produce information/analytics based on 5GC-compatible requests from an AF(AIML-T), as defined in 3GPP TS 23.288 (V17.4.0). As such, AF(AIML-T) will behave as a consumer of NWDAF analytics. The UE or authorized third party can perform application layer AI/ML operations based on the AI/ML assistance information provided by AF(AIML-T). This reduces complexity and increases flexibility for 5GC to assist the application layer AI/ML operations performed by UE (e.g., application client) and authorized third party (e.g., application server).

Some typical application layer AI/ML use cases include image/media/speech recognition, media quality enhancement, automotive networked applications, split control, etc. In these various use cases, there can be various splits of functionality between the AI/ML endpoints (e.g., UE, authorized third party). Furthermore, some application layer AI/ML operations require several rounds for completion, with one round being a transaction between AI/ML endpoints (e.g., client and server) along with the corresponding processes at the AI/ML endpoints. For example, one round could be the server sending data to the client(s), which performs computation on the data and returns some results to the server. The data sent and results returned can include raw data (e.g., images, video, audio, tasks, sensor data, etc.), intermediate ML parameters (e.g., weights, gradient, etc.), ML models, ML model topology, etc. Each round may exchange different data and results, e g., as the application layer AI/ML operations proceed toward completion.

Thus, the AI/ML endpoints may have various requirements for information about network conditions, etc. needed to perform their respective application layer AI/ML operations. In various embodiments, the AI/ML endpoints may request and obtain from 5GC different assistance information for their various application layer AI/ML operations.

However, it is expected that the number of application layer AI/ML use cases will continue to increase, along with the corresponding need for information from 5GC This places an increasing burden on 5GC to understand all requests from AI/ML endpoints and provide corresponding assistance information. These challenges can be addressed by the AIML-T, which translates (e.g., maps and/or converts) requests from AI/ML endpoints to requests sent to 5GC, and corresponding information/analytics from 5GC to AI/ML assistance information provided to AI/ML endpoints.

Figure 6 shows a block diagram of interactions between AI/ML endpoints, NFs in the 5GC, and AIML-T, according to various embodiments of the present disclosure. In particular, Figure 6 shows a generic architecture that covers various embodiments that are described in more detail below.

In some embodiments, AIML-T could be integrated into an AF or could be accessed and controlled by an AF. Both of these cases will be denoted “AF(AIML-T)”. For example, the AF in these embodiments could be a Data Collection Application Function DCAF, a FL assist AF, an AI/ML assist AF, an AI/ML NF, or other AFs with similar functionality. AI/ML endpoints (e.g., UE and authorized third party) request assistance information from 5GC via AF(AIML- T) for their local application layer AI/ML operations. Alternately, AI/ML endpoints can subscribe to AF(AIML-T) for such assistance information, which AF (AIML-T) can provide when it becomes available.

AF(AIML-T) translates requests from the AI/ML endpoints (also referred to as AIML requests) to corresponding requests for 5GC (also referred to as 5GC requests) and interacts with 5GC NFs that can provide the requested information/analytics. If the AF(AIML-T) is in a trusted domain, it can interact with 5GC NFs. If AF(AIML-T) is not in a trusted domain (e.g., in an untrusted domain), it must interact with 5GC NFs via NEF. Alternately, AF(AIML-T) translates AIML requests to corresponding subscription requests for information from 5GC. According to the 5GC request (or subscription request), the other NFs perform operations such as collection, monitoring, computation, etc. to produce the requested information/analytics. The other NFs provide the requested information/analytics to the AF(AIML-T) in a response (or a notification associated with a subscription request). After receiving the requested information/analytics, AF(AIML-T) converts it into AI/ML assistance information provides the AI/ML assistance information to the AI/ML endpoints as a response (or a notification associated with a subscription request). The AI/ML endpoints perform application layer AI/ML operations based on the AI/ML assistance information.

In some embodiments, AIML-T can be part of NEF, which will be denoted “NEF(AIML- T)”. These embodiments can make use of existing NEF capabilities to translate requests from external applications to 5GC services and/or service operations. In such case, NEF(AIML-T) will be in a trusted domain.

Figures 7-8 illustrate different embodiments for AF(AIML-T). In particular, Figure 7 shows an AF that contains or includes AIML-T while Figure 8 shows an AF that has capability to access and control AIML-T. Figure 9 shows an embodiment in which AIML-T is included or contained in NEF while Figure 10 shows an embodiment where AIML-T is included or contained in another NF such as SMF, NWDAF, etc.

For embodiments where AF contains AIML-T (e.g., Figure 7), AI/ML-related requests from the AI/ML endpoints (e.g., UE, authorized third Party) can be obtained and processed at the integrated AI/ML-T part of the AF. In contrast, non- AI/ML-related requests could be obtained and processed directly by the AF.

For embodiments where AF has capability to access and control AIML-T (e.g., Figure 8), some variations are possible. For example, the AF(AIML-T) could receive AI/ML requests from the AI/ML endpoints (e.g., UE, Authorized 3^rd Party) directly and then send the AI/ML request to the AIML-T. As a variation, the AIML-T can receive AI/ML requests from AI/ML endpoints directly.

In these embodiments, the AF controls AIML-T to translate (e.g., convert or map) an AI/ML request into one or more corresponding 5GC requests. AF(AIML-T) then requests (or subscribes) to information/analytics from other 5GC NFs (e.g., NWDAF, etc.). The translated 5GC request could be contained in the request or subscription request and be sent to the other NFs through the AF. Alternatively, the AF could control the AI/ML-T to send the 5GC request to the other NFs.

According to the 5GC request (or subscription request), the other NFs perform operations such as collection, monitoring, computation, etc. to produce the requested information/analytics. The other NFs provide the requested information/analytics to AF(AIML-T). The AF may receive the requested information/analytics and forward it to AIML-T for conversion. Alternatively, the AF may control the AIML-T to receive the requested information/analytics directly from the other NFs and then perform the conversion into AI/ML assistance information. The AF may control the AIML-T to send the assistance information directly to the requesting AI/ML endpoint(s), or to the AF which forwards it to the requesting AI/ML endpoint(s).

Figure 11 (which includes Figures 11 A-B) shows a signaling diagram of a procedure for translating AI/ML requests to 5GC requests and translating analytics to AI/ML assistance information, according to some embodiments of the present disclosure. The procedure is between one or more authorized third parties, one or more UEs, an AF(AIML-T), an NEF, and one or more other NFs of a 5GC. Although the operations in Figure 11 are given numerical labels, this is done to facilitate the following description rather than to require or imply a sequential order of the operations, unless stated to the contrary For simplicity, the description will refer to one or more entities (e.g., UEs) as a single entity (e.g., UE).

In operation la, the UE requests or subscribes to AF (AIML-T) for AI/ML assistance information for application layer AI/ML operations at the UE. The request (e.g., information request or subscription request) can include any of the following parameters:

• type of application layer AI/ML operations at UE(s), such as AI/ML operation splitting, AI/ML model/data distribution and sharing, distributed/federated learning, etc.

• UE identifier(s), such as Application layer ID, GPSI, SUPI, IP address, etc.

• Type of assistance information needed, e.g., statistics, predictions, information of network conditions and changes, resource utilization (e.g., measurements or analytics), etc.

• Time window (for one-time) or time interval (for periodically) required for which the assistance information is needed.

• For type AI/ML operation splitting, any of the following parameters may be included in relation to uploading (or sending) and/or downloading (or receiving) data to be used in the application layer AI/ML operation: o latency to the next splitting endpoint, which could be cloud server, edge server, etc.; o data rate(s), bitrate(s), etc., which could be per-round or for all rounds; o threshold(s) for data transmission reliability, which could be per-round or for all rounds, and/or per-data type or for all data types; o threshold(s) for communication service availability, which could be per-round or for all rounds; o time period(s) of interest, which could be per-round or for all rounds; and o area(s) of interest of interest, which could be per-round or for all rounds. • For type AI/ML model/data distribution and sharing, any of the following parameters may be included in a request for downloading data to be used by the UE: o latency (e.g., maximum latency); o data rate(s), bitrate(s), etc., which could be per-round or for all rounds; o threshold(s) for data transmission reliability, which could be per-round or for all rounds, and/or per-data type or for all data types; o threshold(s) for communication service availability, which could be per-round or for all rounds; o time period(s) of interest, which could be per-round or for all rounds; and o area(s) of interest, which could be per-round or for all rounds. o For type distributed/federated learning, any of the following parameters may be included in relation to uploading (or sending) and/or downloading (or receiving) data used in UE AI/ML operation: o latency (e.g., maximum latency); o data rate(s), bitrate(s), etc., which can be per-round or for all rounds; o threshold(s) for data transmission reliability, which can be per-round or for all rounds, and/or per-data type or for all data types; o threshold(s) for communication service availability, which can be per-round or for all rounds; o time period(s) of interest, which can be per-round or for all rounds; and o area(s) of interest, which can be per-round or for all rounds. o available time and available resource (e.g., computation, energy, etc.) to provide as a group member of distributed/federated learning.

In operation lb, the authorized third party requests or subscribes to AF(AIML-T) for assistance information for local application layer AI/ML operations. The request (e g., information request or subscription request) can include any of the following parameters: o type of application layer AI/ML operations at AF, such as AI/ML operation splitting, AI/ML model/data distribution and sharing, distributed/federated learning, etc. o AF identifier(s). o Type of assistance information needed, e.g., statistics, predictions, information of network conditions and changes, resource utilization (e.g., measurements or analytics), etc. o Time window (for one-time) or time interval (for periodically) required for which the assistance information is needed. o For type AI/ML operation splitting, any of the following parameters may be included in relation to uploading (or receiving) and/or downloading (or sending) data to be used in the application layer AI/ML operations: o latency to the next splitting endpoint, which can be cloud server, edge server, UE, etc. o data rate(s), bitrate(s), etc., which can be per-round or for all rounds; o threshold(s) for data transmission reliability, which can be per-round or for all rounds, per-group member or for all group members, and/or per-data type or for all data types; o threshold(s) for communication service availability, which can be per-round or for all rounds and/or per-group member or for all group members; o time period(s) of interest, which could be per-round or for all rounds; and o area(s) of interest, which could be per-round or for all rounds.

• For type AI/ML model/data distribution and sharing, any of the following parameters may be included in the request for downloading (DL) o latency (e.g., maximum latency); o data rate(s), bitrate(s), etc., which could be per-round or for all rounds; o threshold(s) for data transmission reliability, which could be per-round or for all rounds, and/or per-data type or for all data types; o threshold(s) for communication service availability, which could be per-round or for all rounds; o time period(s) of interest, which could be per-round or for all rounds; and o area(s) of interest, which could be per-round or for all rounds. o For type distributed/federated learning, any of the following parameters may be included in relation to uploading (or receiving) and/or downloading (or sending) data used in group member AI/ML operations: o latency (e.g., maximum latency), which can be per-group member or for all group members; o data rate(s), bitrate(s), etc., which can be per-round or for all rounds; o threshold(s) for data transmission reliability, which can be per-round or for all rounds, per-group member or for all group members, and/or per-data type or for all data types; o threshold(s) for communication service availability, which can be per-round or for all rounds and/or per-group member or for all group members; o time period(s) of interest, which could be per-round or for all rounds; o area(s) of interest, which could be per-round or for all rounds, and o for each group member, time and resource (e.g., computation, energy, etc.) available to provide as a group member of distributed/federated learning. In operation 2, after receiving the request from UE(s) and/or authorized third party, the AF(AIML-T) translates (e.g., maps or converts) the AI/ML request to a 5GC request according to the parameters in the AI/ML request The specific techniques for mapping the parameters into 5GC services and services operations (e.g., for Nnwdaf, Npcf, or Nsmf services) are servicespecific and beyond the scope of the present disclosure. As general examples, the 5GC request(s) may be to perform one or more of the following operations: o Collect information, data, analytics, etc. o Monitor network conditions and changes, resource utilization, QoS, etc. o Analytics (statistics, predictions) for one or multiple Analytics IDs, etc.

In operation 3, AF(AIML-T) discovers NF(s) (e.g., NWDAF, etc.) that can perform the operations determined in operation 2. Some examples include:

• ADRF, DCCF, etc. for data collecting;

• SMF, AMF, etc. for network condition, resource utilization, QoS monitoring, etc.

• NWDAF(s) for analytics based on one or more Analytics IDs, etc.

In operation 4a, assuming AF(AIML-T) is in a trusted domain, AF(AIML-T) requests or subscribes to the discovered NFs for information/analytics according to the translated AI/ML request. Alternatively in operations 4b-c, assuming AF(AIML-T) is not in a trusted domain, AF(AIML-T) invokes Nnef_EventExposure_Subscribe or other service operation to subscribe to NF event exposure from NEF, which in turn requests or subscribes to the NFs for information/analytics.

In operation 5, the NFs perform operations according to the requests such as collection, monitoring, analytics generation, etc. In operation 6a, assuming that AF(AIML-T) is in a trusted domain, the NFs respond to or notify AF(AIML-T) with the requested information/analytics. Alternatively in operations 6b-c, assuming AF(AIML-T) is not in a trusted domain, the NFs invoke Nnf_EventExposure_Notify or other service operation to provide the requested information/analytics to NEF, which in turn sends this information to AF(AIML-T) by using Nnef_EventExposure_Notify or other service operation.

In operation 7, AF(AIML-T) translates (e g., maps and/or converts) the received information/analytics to AI/ML assistance information according to the AI/ML request. For example, one set or batch of AI/ML assistance information may be produced based on one or more information/analytics received from 5GC NF s. The specific techniques for translating the received information/analytics to AI/ML assistance information are specific to the particular inputs/ outputs of this operation and beyond the scope of the present disclosure. As general examples, the AI/ML assistance information may include one or more of the following: • Predictions on network conditions (e.g., latency, bitrate, communication service availability, reliability, etc.) and corresponding time window;

• Predictions on network condition changes and corresponding time;

• Predictions on need for downloading ML model or some amount of data; and

• Information about resource utilization (e.g., measurements, analytics, etc.).

In operations 8a-b, AF(AIML-T) sends the AI/ML assistance information to the UE and the authorized third party, e g., as a response or notification. In operation 9a, the UE performs application layer AI/ML operations based on the AI/ML assistance information. These operations can include any of the following:

• Make decision on split point and time for training, inference, or control.

• Make decision on uploading or downloading information such as images, video, tasks, intermediate ML parameters (e g., weights, gradient, etc.), ML models, ML model topologies, etc.

In operation 9b, the authorized third party performs application layer AI/ML operations based on the AI/ML assistance information. These operations can include any of the following:

• Make decision on split point and time for training, inference, or control.

• Select or adjust group members for specific AI/ML operations.

• Make decision to broadcast or multicast image, video, tasks, intermediate ML parameters (e.g., weights, gradient, etc.), ML models, ML model topology, etc. to other AI/ML endpoints (e.g., UE).

In operation 9c, the UE and the authorized third party exchange ML parameters or models, etc. based on the determinations make in operations 9a-b.

Figure 12 shows a signaling diagram of another procedure for translating AI/ML requests to 5GC requests and translating analytics to AI/ML assistance information, according to other embodiments of the present disclosure. The procedure is between AI/ML endpoints (e.g., UE and authorized third party), an AF and an AIML-T (which jointly form an AF(AIML-T)), and one or more 5GC NFs. Although the operations in Figure 12 are given numerical labels, this is done to facilitate the following description rather than to require or imply a sequential order of the operations, unless stated to the contrary.

In operation 1, the AI/ML endpoints request or subscribe to the AF for AI/ML assistance information to support local application layer AI/ML operations. These requests can include any of the same parameters as corresponding requests described above in relation to Figure 11. In operation 2, the AF sends the AI/ML requests to AIML-T (directly or via NEF). In operation 3, AIML-T translates (maps) the AI/ML request to 5GC request according to the parameters in the AI/ML request. This can be done in a similar manner as described above in relation to Figure 11. Operations 4a-b assume that the AF is responsible for interaction with the NFs. In this case, AIML-T sends the 5GC request to the AF (directly or via NEF), the AF discovers the corresponding NFs, and requests or subscribes to the discovered NFs (directly or via NEF) for information/analytics according to the translated AI/ML request. Alternately, in operation 4c, AIML-T discovers the corresponding NFs and requests or subscribes to the discovered NFs (directly or via NEF) for information/analytics according to the translated AI/ML request.

In operation 5, the NFs perform operations according to the requests such as collection, monitoring, analytics generation, etc. Operations 6a-b assume that the AF is responsible for interaction with the NFs. In this case, the NFs respond to or notify the AF with the requested information/analytics, and the AF forwards this information to AIML-T (directly or via NEF). Alternately, in operation 6c, the NFs respond to or notify the AF (AIML-T) with the requested information/analytics (directly or via NEF).

In operation 7, AF(AIML-T) translates (e g., maps and/or converts) the received information/analytics to AI/ML assistance information according to the AI/ML request. This can be done in a similar manner as described in relation to Figure 11 operation 7.

Operations 8a-b assume that the AF is responsible for interaction with the NFs. In this case, AIML-T sends the AI/ML assistance information to the AF (directly or via NEF) and the AF responds to or notifies the AI/ML endpoints with the AI/ML assistance information. Alternately, in operation 8c, the AIML-T responds to or notifies the AI/ML endpoints with the AI/ML assistance information. Although not shown, the procedure can include operations similar to operation 9a-c shown in Figure 11.

Figure 13 shows a signaling diagram of another procedure for translating AI/ML requests to 5GC requests and translating analytics to AI/ML assistance information, according to other embodiments of the present disclosure. The procedure is between AI/ML endpoints (e g., UE and authorized third party), an AF, an NEF(AIML-T), and one or more 5GC NFs. Although the operations in Figure 13 are given numerical labels, this is done to facilitate the following description rather than to require or imply a sequential order of the operations, unless stated to the contrary.

In operation 1, the AI/ML endpoints request or subscribe to the AF for AI/ML assistance information to support local application layer AI/ML operations. These requests can include any of the same parameters as corresponding requests described above in relation to Figure 11. In operation 2, the AF invokes Nnef_EventExposure_Subscribe or other service operation to subscribe to NF event exposure from NEF corresponding to the AI/ML assistance information.

In operation 3, NEF (AIML-T) translates (maps) the AI/ML request to 5GC request according to the parameters in the AI/ML request (i.e., in the Nnef_EventExposure_Subscribe or other service operation). The specific techniques for mapping the parameters into 5GC services and services operations (e.g., for Nnwdaf, Npcf, orNsmf services) are service-specific and beyond the scope of the present disclosure. Some examples were discussed above in relation to Figure 11.

In operation 4, NEF(AIML-T) discovers NF(s) (e.g., NWDAF, etc.) that can perform the operations determined in operation 3. Some examples were discussed above in relation to Figure 11. In operation 5, NEF(AIML-T) requests or subscribes to the discovered NFs for information/analytics according to the translated AI/ML request. In operation 6, the NFs perform operations according to the requests such as collection, monitoring, analytics generation, etc. In operation 7, the NFs respond to or notify NEF(AIML-T) with the requested (or subscribed) information/analytics. In operation 8, NEF(AIML-T) translates (e.g., maps and/or converts) the received information/analytics to AI/ML assistance information according to the AI/ML request. This can be done in a similar manner as described in relation to Figure 11 operation 7.

In operation 9, NEF(AIML-T) invokes the Nnef_EventExposure_Notify or other service operation to notify the AF with the AI/ML assistance information, which the AF sends to the AI/ML endpoints in operation 10. In operation 11, the AI/ML endpoints perform application layer AI/ML operations based on the AI/ML assistance information. These operations can include any of the corresponding operations discussed above in relation to Figure 11.

Figure 14 shows a signaling diagram of another procedure for translating AI/ML requests to 5GC requests and translating analytics to AI/ML assistance information, according to other embodiments of the present disclosure. The procedure is between AI/ML endpoints (e.g., UE and authorized third party), an AF, a 5GC NF that includes AIML-T (denoted NF(AIML-T)), and one or more other 5GC NFs. Although the operations in Figure 14 are given numerical labels, this is done to facilitate the following description rather than to require or imply a sequential order of the operations, unless stated to the contrary.

In operation 1, the AI/ML endpoints request or subscribe to NF(AIML-T) for AI/ML assistance information to support local application layer AI/ML operations. These requests can include any of the same parameters as corresponding requests described above in relation to Figure 11. Alternately, in operations 2a-b, the AI/ML endpoints can request or subscribe to the AF for AI/ML assistance information, and the AF further requests or subscribes to NF(AIML-T).

In operation 3, NF(AIML-T) translates (e.g., maps or converts) the AI/ML request received in operation 1 or 2b to a 5GC request according to the parameters in the AI/ML request. This can be done in a similar manner as described above in relation to Figure 11. In operation 4, the NF(AIML-T) performs operations according to the 5GC requests such as collection, monitoring, analytics generation, etc. The NF (AIML-T) may interact with the other NFs in these operations, such as to perform the operations jointly or to collect or received information generated by the other NFs in accordance with the 5GC requests. The result of operation 4 is information/ analytics in accordance with the 5GC requests.

In operation 5, NF(AIML-T) translates (e ., maps or converts) the information/analytics generated and/or collected in operation 4 to AI/ML assistance information according to the AI/ML request. This can be done in a similar manner as described in relation to Figure 11 operation 7.

In operation 6, NF(AIML-T) responds to or notifies the AI/ML endpoints with the AI/ML assistance information according to the original AI/ML request. Alternately, in operations 7a-b, the NF(AIML-T) responds to or notifies the AF with the AI/ML assistance information and the AF responds to or notifies the AI/ML endpoints accordingly.

In operation 8, the AI/ML endpoints perform application layer AI/ML operations based on the AI/ML assistance information. These operations can similar to operations 9a-b of Figure 11, discussed above.

These embodiments described above can be further illustrated with reference to Figure 15, which depicts an exemplary method (e.g., procedure) for an artificial intelligence/machine learning translator function (AIML-T) of a communication network, according to various embodiments of the present disclosure. Put differently, various features of the operations described below correspond to various embodiments described above. The exemplary method shown in Figure 15 can be performed by an AIML-T (or network node or computing system hosting the same) such as described elsewhere herein.

Although the exemplary method is illustrated in Figure 15 by specific blocks in a particular order, the operations corresponding to the blocks can be performed in different orders than shown and can be combined and/or divided into blocks and/or operations having different functionality than shown. Optional blocks and/or operations are indicated by dashed lines.

The exemplary method can include the operations of block 1510, where the AIML-T can receive one or more requests for AI/ML assistance information for one or more nodes that are endpoints for application layer AI/ML operations. The exemplary method can also include the operations of block 1520, where the AIML-T can translate the one or more requests for AI/ML assistance information into one or more requests for information and/or analytics from the communication network. The exemplary method can also include the operations of block 1530, where the AIML-T can send the one or more requests for information and/or analytics to one or more network functions (NFs) of the communication network.

The exemplary method can also include the operations of blocks 1540-1550, where the AIML-T can receive the requested information and/or analytics from the one or more NFs and translate the received information and/or analytics into the requested AI/ML assistance information. The exemplary method can also include the operations of block 1560, where the AIML-T can send the requested AI/ML assistance information to one of the following: the one or more nodes; or an application function (AF) of the communication network.

In some embodiments, translating the one or more requests for AI/ML assistance information in block 1520 can include the operations of sub-blocks 1521-1523. In sub-block 1521, the AIML-T can determine the information and/or analytics to be requested based on the requests for AI/ML assistance information. In sub-block 1522, the AIML-T can select or discover the one or more NFs based on capabilities to provide the information and/or analytics. In sub-block 1523, the AIML-T can create the one or more requests for information and/or analytics based on respective service operations of the one or more NFs.

In some of these embodiments, the one or more requests for information and/or analytics are sent to the one or more NFs (e.g., in block 1530) via a network exposure function (NEF) of the communication network. The requested information and/or analytics is also received (e.g., in block 1540) via the NEF.

In some embodiments, the one or more NFs include any of the following: network data analytics function (NWDAF), session management function (SMF), access and mobility management function (AMF), data collection coordination function (DCCF), analytics data repository function (ADRF).

In some embodiments, the AIML-T is part of an AF of the communication network that provides data collection and/or AI/ML operational assistance. Figures 7 and 11 show examples of these embodiments.

In other embodiments, one or more of the following applies:

Different ones of these embodiments are illustrated in Figure 12 as options.

In other embodiments, the AIML-T is part of a NEF of the communication network. The one or more requests for AI/ML assistance information are received from the AF (e.g., in block 1510) as respective Nnef_EventExposure_Subscribe or other service operations. The requested AI/ML assistance information is sent to the AF (e.g., in block 1560) using one or more Nnef_EventExposure_Notify or other service operations. Figure 13 shows an example of these embodiments.

In other embodiments, the one or more NFs include first and second NFs, with the AIML- T being part of the first NF. A first portion of the requested information and/or analytics is computed or obtained by the first NF. A request for information and/or analytics is sent (e.g., in block 1530) to the second NF and a second portion of the requested information and/or analytics is received from the second NF (e.g., in block 1540). Figure 14 shows an example of these embodiments. In some of these embodiments, the one or more requests for AI/ML assistance information are received (e g., in block 1510) from the one or more nodes that are endpoints for AI/ML operations. In other of these embodiments, the one or more requests for AI/ML assistance information are received from the AF of the communication network.

• an identifier of the requesting node;

• type of AI/ML assistance information needed; and

• one or more data rates;

• one or more thresholds for communication service availability;

• one or more time periods of interest; and

• one or more areas of interest.

In some of these embodiments, the request identifies a plurality of data types comprising the AI/ML assistance information, and the one or more thresholds for data transmission reliability include one of the following: a single threshold associated with all of the data types; or a plurality of thresholds, with each threshold associated with a different one of the data types. Some example data types were discussed above.

• second predictions about changes to the conditions in the communication network,

• information about resource utilization in the communication network; and

In some embodiments, the AIML-T is part of a NEF of the communication network (as mentioned above; see also for example Fig. 9, entity “NEF (AIML-T)”) so that the NEF translates (by mapping or converting) one or more requests for AI/ML assistance information from an AF (see for example Fig. 9) to one or more requests for information and/or analytics (e.g., statistics or predictions) that can be understood by 5GC NFs for assisting Application Layer AI/ML operations. For example, based on criteria (for example filtering criteria) provided by the AF, the NEF determines what information is to be collected, which 5GC NFs is/are to be selected for information/analytics collection/subscription (e.g., one or more analytics IDs, statistics, predictions, etc.), and how to interact with the 5GC NFs (e.g., invoke services to create request/subscription to the 5GC NFs for information/analytics).

Although various embodiments are described herein above in terms of methods, apparatus, devices, computer-readable medium and receivers, the person of ordinary skill will readily comprehend that such methods 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, etc.

Figure 16 shows an example of a communication system 1600 in accordance with some embodiments. In this example, the communication system 1600 includes a telecommunication network 1602 that includes an access network 1604, such as a radio access network (RAN), and a core network 1606, which includes one or more core network nodes 1608. The access network 1604 includes one or more access network nodes, such as network nodes 1610a and 1610b (one or more of which may be generally referred to as network nodes 1610), or any other similar 3^rd Generation Partnership Project (3GPP) access node or non-3GPP access point. The network nodes 1610 facilitate direct or indirect connection of user equipment (UE), such as by connecting UEs 1612a, 1612b, 1612c, and 1612d (one or more of which may be generally referred to as UEs 1612) to the core network 1606 over one or more wireless connections.

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, the communication system 1600 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. The communication system 1600 may include and/or interface with any type of communication, telecommunication, data, cellular, radio network, and/or other similar type of system.

The UEs 1612 may be any of a wide variety of communication devices, including wireless devices arranged, configured, and/or operable to communicate wirelessly with the network nodes 1610 and other communication devices. Similarly, the network nodes 1610 are arranged, capable, configured, and/or operable to communicate directly or indirectly with the UEs 1612 and/or with other network nodes or equipment in the telecommunication network 1602 to enable and/or provide network access, such as wireless network access, and/or to perform other functions, such as administration in the telecommunication network 1602.

In the depicted example, the core network 1606 connects the network nodes 1610 to one or more hosts, such as host 1616. 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. The core network 1606 includes one more core network nodes (e.g., core network node 1608) 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 the core network node 1608. 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).

The host 1616 may be under the ownership or control of a service provider other than an operator or provider of the access network 1604 and/or the telecommunication network 1602, and may be operated by the service provider or on behalf of the service provider. The host 1616 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.

As a whole, the communication system 1600 of Figure 16 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.

In some examples, the telecommunication network 1602 is a cellular network that implements 3GPP standardized features. Accordingly, the telecommunications network 1602 may support network slicing to provide different logical networks to different devices that are connected to the telecommunication network 1602. For example, the telecommunications network 1602 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)/Massive loT services to yet further UEs.

In some examples, the UEs 1612 are configured to transmit and/or receive information without direct human interaction. For instance, a UE may be designed to transmit information to the access network 1604 on a predetermined schedule, when triggered by an internal or external event, or in response to requests from the access network 1604. 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).

In the example, the hub 1614 communicates with the access network 1604 to facilitate indirect communication between one or more UEs (e.g., UE 1612c and/or 1612d) and network nodes (e.g., network node 1610b). In some examples, the hub 1614 may be a controller, router, content source and analytics, or any of the other communication devices described herein regarding UEs. For example, the hub 1614 may be a broadband router enabling access to the core network 1606 for the UEs. As another example, the hub 1614 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 1610, or by executable code, script, process, or other instructions in the hub 1614. As another example, the hub 1614 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, the hub 1614 may be a content source. For example, for a UE that is a VR headset, display, loudspeaker or other media delivery device, the hub 1614 may retrieve VR assets, video, audio, or other media or data related to sensory information via a network node, which the hub 1614 then provides to the UE either directly, after performing local processing, and/or after adding additional local content. In still another example, the hub 1614 acts as a proxy server or orchestrator for the UEs, in particular in if one or more of the UEs are low energy loT devices.

The hub 1614 may have a constant/persi stent or intermittent connection to the network node 1610b. The hub 1614 may also allow for a different communication scheme and/or schedule between the hub 1614 and UEs (e.g., UE 1612c and/or 1612d), and between the hub 1614 and the core network 1606. In other examples, the hub 1614 is connected to the core network 1606 and/or one or more UEs via a wired connection. Moreover, the hub 1614 may be configured to connect to an M2M service provider over the access network 1604 and/or to another UE over a direct connection. In some scenarios, UEs may establish a wireless connection with the network nodes 1610 while still connected via the hub 1614 via a wired or wireless connection. In some embodiments, the hub 1614 may be a dedicated hub - that is, a hub whose primary function is to route communications to/from the UEs from/to the network node 1610b. In other embodiments, the hub 1614 may be a non-dedicated hub - that is, a device which is capable of operating to route communications between the UEs and network node 1610b, but which is additionally capable of operating as a communication start and/or end point for certain data channels.

As a specific example, host 1616 and UEs 1612 can be configured as endpoints for application layer AI/ML operations described herein. As such, host 1616 and UEs 1612 can perform operations attributed to such endpoints in various methods or procedures described above.

Figure 17 shows a UE 1700 in accordance with some embodiments. As used herein, a UE refers to a device capable, configured, arranged and/or operable to communicate wirelessly with network nodes and/or other UEs. Examples of a UE include, but are not limited to, a smart phone, mobile phone, cell phone, voice over IP (VoIP) phone, wireless local loop phone, desktop computer, personal digital assistant (PDA), wireless cameras, gaming console or device, music storage device, playback appliance, wearable terminal device, wireless endpoint, mobile station, tablet, laptop, laptop-embedded equipment (LEE), laptop-mounted equipment (LME), smart device, wireless customer-premise equipment (CPE), vehicle-mounted or vehicle embedded/integrated wireless device, etc. Other examples include any UE identified by the 3rd Generation Partnership Project (3 GPP), including a narrow band internet of things (NB-IoT) UE, a machine type communication (MTC) UE, and/or an enhanced MTC (eMTC) UE.

A UE may support device-to-device (D2D) communication, for example by implementing a 3GPP standard for sidelink communication, Dedicated Short-Range Communication (DSRC), vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), or vehicle-to-everything (V2X). In other examples, a UE may not necessarily have a user in the sense of a human user who owns and/or operates the relevant device. Instead, a UE may represent a device that is intended for sale to, or operation by, a human user but which may not, or which may not initially, be associated with a specific human user (e.g., a smart sprinkler controller). Alternatively, a UE may represent a device that is not intended for sale to, or operation by, an end user but which may be associated with or operated for the benefit of a user (e.g., a smart power meter).

The UE 1700 includes processing circuitry 1702 that is operatively coupled via a bus 1704 to an input/output interface 1706, a power source 1708, a memory 1710, a communication interface 1712, and/or any other component, or any combination thereof. Certain UEs may utilize all or a subset of the components shown in Figure 17. The level of integration between the components may vary from one UE to another UE. Further, certain UEs may contain multiple instances of a component, such as multiple processors, memories, transceivers, transmitters, receivers, etc.

The processing circuitry 1702 is configured to process instructions and data and may be configured to implement any sequential state machine operative to execute instructions stored as machine-readable computer programs in the memory 1710. The processing circuitry 1702 may be implemented as one or more hardware-implemented state machines (e.g., in discrete logic, field- programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), etc.); programmable logic together with appropriate firmware; one or more stored computer programs, general-purpose processors, such as a microprocessor or digital signal processor (DSP), together with appropriate software; or any combination of the above. For example, the processing circuitry 1702 may include multiple central processing units (CPUs).

In the example, the input/output interface 1706 may be configured to provide an interface or interfaces to an input device, output device, or one or more input and/or output devices. Examples of an output device include a speaker, a sound card, a video card, a display, a monitor, a printer, an actuator, an emitter, a smartcard, another output device, or any combination thereof. An input device may allow a user to capture information into the UE 1700. Examples of an input device include a touch-sensitive or presence-sensitive display, a camera (e.g., a digital camera, a digital video camera, a web camera, etc ), a microphone, a sensor, a mouse, a trackball, a directional pad, a trackpad, a scroll wheel, a smartcard, and the like. The presence-sensitive display may include a capacitive or resistive touch sensor to sense input from a user. A sensor may be, for instance, an accelerometer, a gyroscope, a tilt sensor, a force sensor, a magnetometer, an optical sensor, a proximity sensor, a biometric sensor, etc., or any combination thereof. An output device may use the same type of interface port as an input device. For example, a Universal Serial Bus (USB) port may be used to provide an input device and an output device.

In some embodiments, the power source 1708 is structured as a battery or battery pack. Other types of power sources, such as an external power source (e.g., an electricity outlet), photovoltaic device, or power cell, may be used. The power source 1708 may further include power circuitry for delivering power from the power source 1708 itself, and/or an external power source, to the various parts of the UE 1700 via input circuitry or an interface such as an electrical power cable. Delivering power may be, for example, for charging of the power source 1708. Power circuitry may perform any formatting, converting, or other modification to the power from the power source 1708 to make the power suitable for the respective components of the UE 1700 to which power is supplied.

The memory 1710 may be or be configured to include memory such as random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic disks, optical disks, hard disks, removable cartridges, flash drives, and so forth. In one example, the memory 1710 includes one or more application programs 1714, such as an operating system, web browser application, a widget, gadget engine, or other application, and corresponding data 1716. The memory 1710 may store, for use by the UE 1700, any of a variety of various operating systems or combinations of operating systems.

The memory 1710 may be configured to include a number of physical drive units, such as redundant array of independent disks (RAID), flash memory, USB flash drive, external hard disk drive, thumb drive, pen drive, key drive, high-density digital versatile disc (HD-DVD) optical disc drive, internal hard disk drive, Blu-Ray optical disc drive, holographic digital data storage (HDDS) optical disc drive, external mini-dual in-line memory module (DIMM), synchronous dynamic random access memory (SDRAM), external micro-DIMM SDRAM, smartcard memory such as tamper resistant module in the form of a universal integrated circuit card (UICC) including one or more subscriber identity modules (SIMs), such as a USIM and/or ISIM, other memory, or any combination thereof. The UICC may for example be an embedded UICC (eUICC), integrated UICC (iUICC) or a removable UICC commonly known as ‘SIM card.’ The memory 1710 may allow the UE 1700 to access instructions, application programs and the like, stored on transitory or non-transitory memory media, to off-load data, or to upload data. An article of manufacture, such as one utilizing a communication system may be tangibly embodied as or in the memory 1710, which may be or comprise a device-readable storage medium.

The processing circuitry 1702 may be configured to communicate with an access network or other network using the communication interface 1712. The communication interface 1712 may comprise one or more communication subsystems and may include or be communicatively coupled to an antenna 1722. The communication interface 1712 may include one or more transceivers used to communicate, such as by communicating with one or more remote transceivers of another device capable of wireless communication (e.g., another UE or a network node in an access network). Each transceiver may include a transmitter 1718 and/or a receiver 1720 appropriate to provide network communications (e.g., optical, electrical, frequency allocations, and so forth). Moreover, the transmitter 1718 and receiver 1720 may be coupled to one or more antennas (e.g., antenna 1722) and may share circuit components, software or firmware, or alternatively be implemented separately.

In the illustrated embodiment, communication functions of the communication interface 1712 may include cellular communication, Wi-Fi communication, LPWAN communication, data communication, voice communication, multimedia communication, short-range communications such as Bluetooth, near-field communication, location-based communication such as the use of the global positioning system (GPS) to determine a location, another like communication function, or any combination thereof. Communications may be implemented in according to one or more communication protocols and/or standards, such as IEEE 802.11, Code Division Multiplexing Access (CDMA), Wideband Code Division Multiple Access (WCDMA), GSM, LTE, New Radio (NR), UMTS, WiMax, Ethernet, transmission control protocol/internet protocol (TCP/IP), synchronous optical networking (SONET), Asynchronous Transfer Mode (ATM), QUIC, Hypertext Transfer Protocol (HTTP), and so forth.

Regardless of the type of sensor, a UE may provide an output of data captured by its sensors, through its communication interface 1712, via a wireless connection to a network node. Data captured by sensors of a UE can be communicated through a wireless connection to a network node via another UE. The output may be periodic (e.g., once every 15 minutes if it reports the sensed temperature), random (e.g., to even out the load from reporting from several sensors), in response to a triggering event (e.g., when moisture is detected an alert is sent), in response to a request (e.g., a user initiated request), or a continuous stream (e.g., a live video feed of a patient). As another example, a UE comprises an actuator, a motor, or a switch, related to a communication interface configured to receive wireless input from a network node via a wireless connection. In response to the received wireless input the states of the actuator, the motor, or the switch may change. For example, the UE may comprise a motor that adjusts the control surfaces or rotors of a drone in flight according to the received input or to a robotic arm performing a medical procedure according to the received input.

A UE, when in the form of an Internet of Things (loT) device, may be a device for use in one or more application domains, these domains comprising, but not limited to, city wearable technology, extended industrial application and healthcare. Non-limiting examples of such an loT device are a device which is or which is embedded in: a connected refrigerator or freezer, a TV, a connected lighting device, an electricity meter, a robot vacuum cleaner, a voice controlled smart speaker, a home security camera, a motion detector, a thermostat, a smoke detector, a door/window sensor, a flood/moisture sensor, an electrical door lock, a connected doorbell, an air conditioning system like a heat pump, an autonomous vehicle, a surveillance system, a weather monitoring device, a vehicle parking monitoring device, an electric vehicle charging station, a smart watch, a fitness tracker, a head-mounted display for Augmented Reality (AR) or Virtual Reality (VR), a wearable for tactile augmentation or sensory enhancement, a water sprinkler, an animal- or item-tracking device, a sensor for monitoring a plant or animal, an industrial robot, an Unmanned Aerial Vehicle (UAV), and any kind of medical device, like a heart rate monitor or a remote controlled surgical robot. A UE in the form of an loT device comprises circuitry and/or software in dependence of the intended application of the loT device in addition to other components as described in relation to the UE 1700 shown in Figure 17.

As yet another specific example, in an loT scenario, a UE may represent a machine or other device that performs monitoring and/or measurements, and transmits the results of such monitoring and/or measurements to another UE and/or a network node. The UE may in this case be an M2M device, which may in a 3GPP context be referred to as an MTC device. As one particular example, the UE may implement the 3GPP NB-IoT standard. In other scenarios, a UE may represent a vehicle, such as a car, a bus, a truck, a ship and an airplane, or other equipment that is capable of monitoring and/or reporting on its operational status or other functions associated with its operation.

In practice, any number of UEs may be used together with respect to a single use case. For example, a first UE might be or be integrated in a drone and provide the drone’ s speed information (obtained through a speed sensor) to a second UE that is a remote controller operating the drone. When the user makes changes from the remote controller, the first UE may adjust the throttle on the drone (e.g., by controlling an actuator) to increase or decrease the drone’s speed. The first and/or the second UE can also include more than one of the functionalities described above. For example, a UE might comprise the sensor and the actuator, and handle communication of data for both the speed sensor and the actuators.

As a specific example, UE 1700 can be configured as an endpoint for application layer AI/ME operations described herein. As such, UE 1700 can perform operations attributed to UE endpoints in various methods or procedures described above.

Figure 18 shows a network node 1800 in accordance with some embodiments. As used herein, network node refers to equipment capable, configured, arranged and/or operable to communicate directly or indirectly with a UE and/or with other network nodes or equipment, in a telecommunication network. Examples of network nodes include, but are not limited to, access points (APs) (e.g., radio access points), base stations (BSs) (e.g., radio base stations, Node Bs, evolved Node Bs (eNBs) and NRNodeBs (gNBs)).

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 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).

Other examples of network nodes include multiple transmission point (multi-TRP) 5G access nodes, multi-standard radio (MSR) equipment such as MSRBSs, network controllers such as radio network controllers (RNCs) or base station controllers (BSCs), base transceiver stations (BTSs), transmission points, transmission nodes, multi -cell/multi cast 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).

As a specific example, network node 1800 can be configured as, or to host, an artificial intelligence/machine learning translator function (AIML-T) described herein. As such, network node 1800 (or its components described below) can be configured to perform operations attributed to AIML-T in various methods or procedures described above.

The network node 1800 includes a processing circuitry 1802, a memory 1804, a communication interface 1806, and a power source 1808. The network node 1800 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 the network node 1800 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, the network node 1800 may be configured to support multiple radio access technologies (RATs). In such embodiments, some components may be duplicated (e.g., separate memory 1804 for different RATs) and some components may be reused (e g., a same antenna 1810 may be shared by different RATs). The network node 1800 may also include multiple sets of the various illustrated components for different wireless technologies integrated into network node 1800, 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 1800.

The processing circuitry 1802 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 1800 components, such as the memory 1804, to provide network node 1800 functionality.

In some embodiments, the processing circuitry 1802 includes a system on a chip (SOC). In some embodiments, the processing circuitry 1802 includes one or more of radio frequency (RF) transceiver circuitry 1812 and baseband processing circuitry 1814. In some embodiments, the radio frequency (RF) transceiver circuitry 1812 and the baseband processing circuitry 1814 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 1812 and baseband processing circuitry 1814 may be on the same chip or set of chips, boards, or units.

The memory 1804 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 the processing circuitry 1802. The memory 1804 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 (collectively denoted computer program product 1804a) capable of being executed by the processing circuitry 1802 and utilized by the network node 1800. The memory 1804 may be used to store any calculations made by the processing circuitry 1802 and/or any data received via the communication interface 1806. In some embodiments, the processing circuitry 1802 and memory 1804 is integrated.

The communication interface 1806 is used in wired or wireless communication of signaling and/or data between a network node, access network, and/or UE. As illustrated, the communication interface 1806 comprises port(s)/terminal(s) 1816 to send and receive data, for example to and from a network over a wired connection. The communication interface 1806 also includes radio front-end circuitry 1818 that may be coupled to, or in certain embodiments a part of, the antenna 1810. Radio front-end circuitry 1818 comprises filters 1820 and amplifiers 1822. The radio front-end circuitry 1818 may be connected to an antenna 1810 and processing circuitry 1802. The radio front-end circuitry may be configured to condition signals communicated between antenna 1810 and processing circuitry 1802. The radio front-end circuitry 1818 may receive digital data that is to be sent out to other network nodes or UEs via a wireless connection. The radio frontend circuitry 1818 may convert the digital data into a radio signal having the appropriate channel and bandwidth parameters using a combination of filters 1820 and/or amplifiers 1822. The radio signal may then be transmitted via the antenna 1810. Similarly, when receiving data, the antenna 1810 may collect radio signals which are then converted into digital data by the radio front-end circuitry 1818. The digital data may be passed to the processing circuitry 1802. In other embodiments, the communication interface may comprise different components and/or different combinations of components.

In certain alternative embodiments, the network node 1800 does not include separate radio front-end circuitry 1818, instead, the processing circuitry 1802 includes radio front-end circuitry and is connected to the antenna 1810. Similarly, in some embodiments, all or some of the RF transceiver circuitry 1812 is part of the communication interface 1806. In still other embodiments, the communication interface 1806 includes one or more ports or terminals 1816, the radio frontend circuitry 1818, and the RF transceiver circuitry 1812, as part of a radio unit (not shown), and the communication interface 1806 communicates with the baseband processing circuitry 1814, which is part of a digital unit (not shown).

The antenna 1810 may include one or more antennas, or antenna arrays, configured to send and/or receive wireless signals. The antenna 1810 may be coupled to the radio front-end circuitry 1818 and may be any type of antenna capable of transmitting and receiving data and/or signals wirelessly. In certain embodiments, the antenna 1810 is separate from the network node 1800 and connectable to the network node 1800 through an interface or port.

The antenna 1810, communication interface 1806, and/or the processing circuitry 1802 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, the antenna 1810, the communication interface 1806, and/or the processing circuitry 1802 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.

The power source 1808 provides power to the various components of network node 1800 in a form suitable for the respective components (e g., at a voltage and current level needed for each respective component). The power source 1808 may further comprise, or be coupled to, power management circuitry to supply the components of the network node 1800 with power for performing the functionality described herein. For example, the network node 1800 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 the power source 1808. As a further example, the power source 1808 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.

Embodiments of the network node 1800 may include additional components beyond those shown in Figure 18 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, the network node 1800 may include user interface equipment to allow input of information into the network node 1800 and to allow output of information from the network node 1800. This may allow a user to perform diagnostic, maintenance, repair, and other administrative functions for the network node 1800.

Figure 19 is a block diagram of a host 1900, which may be an embodiment of the host 1616 of Figure 16, in accordance with various aspects described herein. As used herein, the host 1900 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. The host 1900 may provide one or more services to one or more UEs. The host 1900 includes processing circuitry 1902 that is operatively coupled via a bus 1904 to an input/output interface 1906, a network interface 1908, a power source 1910, and a memory 1912. Other components may be included in other embodiments. Features of these components may be substantially similar to those described with respect to the devices of previous figures, such as Figures 17 and 18, such that the descriptions thereof are generally applicable to the corresponding components of host 1900.

The memory 1912 may include one or more computer programs including one or more host application programs 1914 and data 1916, which may include user data, e.g., data generated by a UE for the host 1900 or data generated by the host 1900 for a UE Embodiments of the host 1900 may utilize only a subset or all of the components shown. The host application programs 1914 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.711), including transcoding for multiple different classes, types, or implementations of UEs (e.g., handsets, desktop computers, wearable display systems, heads-up display systems). The host application programs 1914 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, the host 1900 may select and/or indicate a different host for over-the-top services for a UE. The host application programs 1914 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.

As a specific example, host 1900 can be configured as an endpoint for application layer AI/ML operations described herein. As such, host 1900 (or its components described above) can be configured to perform operations attributed to authorized third party endpoints in various methods or procedures described above.

Figure 20 is a block diagram illustrating a virtualization environment 2000 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 2000 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.

Applications 2002 (which may alternatively be called software instances, virtual appliances, network functions, virtual nodes, virtual network functions, etc.) are run in the virtualization environment 1900 to implement some of the features, functions, and/or benefits of some of the embodiments disclosed herein.

As a specific example, artificial intelligence/machine learning translator functions (AIML- T) described herein can be implemented as a software instance, a virtual appliance, a network function, a virtual node, or a virtual network function in virtualization environment 2000. As such, hardware 2004 can perform operations attributed to AIML-T in various methods or procedures described above.

Hardware 2004 includes processing circuitry, memory that stores software and/or instructions (collectively denoted computer program product 2004a) 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 2006 (also referred to as hypervisors or virtual machine monitors (VMMs)), provide VMs 2008a and 2008b (one or more of which may be generally referred to as VMs 2008), and/or perform any of the functions, features and/or benefits described in relation with some embodiments described herein. The virtualization layer 2006 may present a virtual operating platform that appears like networking hardware to the VMs 2008.

The VMs 2008 comprise virtual processing, virtual memory, virtual networking or interface and virtual storage, and may be run by a corresponding virtualization layer 2006. Different embodiments of the instance of a virtual appliance 2002 may be implemented on one or more of VMs 2008, 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.

In the context of NFV, a VM 2008 may be a software implementation of a physical machine that runs programs as if they were executing on a physical, non-virtualized machine. Each of the VMs 2008, and that part of hardware 2004 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 2008 on top of the hardware 2004 and corresponds to the application 2002.

Hardware 2004 may be implemented in a standalone network node with generic or specific components. Hardware 2004 may implement some functions via virtualization. Alternatively, hardware 2004 may be part of a larger cluster of hardware (e.g., in a data center or CPE) where many hardware nodes work together and are managed via management and orchestration 2010, which, among others, oversees lifecycle management of applications 2002. In some embodiments, hardware 2004 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 2012 which may alternatively be used for communication between hardware nodes and radio units.

Figure 21 shows a communication diagram of a host 2102 communicating via a network node 2104 with a UE 2106 over a partially wireless connection in accordance with some embodiments. Example implementations, in accordance with various embodiments, of the UE (such as a UE 1612a of Figure 16 and/or UE 1700 of Figure 17), network node (such as network node 1610a of Figure 16 and/or network node 1800 of Figure 18), and host (such as host 1616 of Figure 16 and/or host 1900 of Figure 19) discussed in the preceding paragraphs will now be described with reference to Figure 21.

Like host 1900, embodiments of host 2102 include hardware, such as a communication interface, processing circuitry, and memory. The host 2102 also includes software, which is stored in or accessible by the host 2102 and executable by the processing circuitry. The software includes a host application that may be operable to provide a service to a remote user, such as the UE 2106 connecting via an over-the-top (OTT) connection 2150 extending between the UE 2106 and host 2102. In providing the service to the remote user, a host application may provide user data which is transmitted using the OTT connection 2150.

The network node 2104 includes hardware enabling it to communicate with the host 2102 and UE 2106. The connection 2160 may be direct or pass through a core network (like core network 1606 of Figure 16) and/or one or more other intermediate networks, such as one or more public, private, or hosted networks. For example, an intermediate network may be a backbone network or the Internet.

The UE 2106 includes hardware and software, which is stored in or accessible by UE 2106 and executable by the UE’s processing circuitry. The software includes a client application, such as a web browser or operator-specific “app” that may be operable to provide a service to a human or non-human user via UE 2106 with the support of the host 2102. In the host 2102, an executing host application may communicate with the executing client application via the OTT connection 2150 terminating at the UE 2106 and host 2102. In providing the service to the user, the UE's client application may receive request data from the host's host application and provide user data in response to the request data. The OTT connection 2150 may transfer both the request data and the user data. The UE's client application may interact with the user to generate the user data that it provides to the host application through the OTT connection 2150.

The OTT connection 2150 may extend via a connection 2160 between the host 2102 and the network node 2104 and via a wireless connection 2170 between the network node 2104 and the UE 2106 to provide the connection between the host 2102 and the UE 2106. The connection 2160 and wireless connection 2170, over which the OTT connection 2150 may be provided, have been drawn abstractly to illustrate the communication between the host 2102 and the UE 2106 via the network node 2104, without explicit reference to any intermediary devices and the precise routing of messages via these devices.

As an example of transmitting data via the OTT connection 2150, in step 2108, the host 2102 provides user data, which may be performed by executing a host application. In some embodiments, the user data is associated with a particular human user interacting with the UE 2106. In other embodiments, the user data is associated with a UE 2106 that shares data with the host 2102 without explicit human interaction. In step 2110, the host 2102 initiates a transmission carrying the user data towards the UE 2106. The host 2102 may initiate the transmission responsive to a request transmitted by the UE 2106. The request may be caused by human interaction with the UE 2106 or by operation of the client application executing on the UE 2106. The transmission may pass via the network node 2104, in accordance with the teachings of the embodiments described throughout this disclosure. Accordingly, in step 2112, the network node 2104 transmits to the UE 2106 the user data that was carried in the transmission that the host 2102 initiated, in accordance with the teachings of the embodiments described throughout this disclosure. In step 2114, the UE 2106 receives the user data carried in the transmission, which may be performed by a client application executed on the UE 2106 associated with the host application executed by the host 2102.

In some examples, the UE 2106 executes a client application which provides user data to the host 2102. The user data may be provided in reaction or response to the data received from the host 2102. Accordingly, in step 2116, the UE 2106 may provide user data, which may be performed by executing the client application. In providing the user data, the client application may further consider user input received from the user via an input/output interface of the UE 2106. Regardless of the specific manner in which the user data was provided, the UE 2106 initiates, in step 2118, transmission of the user data towards the host 2102 via the network node 2104. In step 2120, in accordance with the teachings of the embodiments described throughout this disclosure, the network node 2104 receives user data from the UE 2106 and initiates transmission of the received user data towards the host 2102. In step 2122, the host 2102 receives the user data carried in the transmission initiated by the UE 2106.

One or more of the various embodiments improve the performance of OTT services provided to the UE 2106 using the OTT connection 2150, in which the wireless connection 2170 forms the last segment More precisely, embodiments expose required assistance information to a UE and/or an authorized third party, thereby facilitating application layer AI/ML operations by these entities. Moreover, embodiments eliminate the need for other NFs in 5GC to understand application layer AI/ML requests and corresponding assistance information Rather, 5GC NFs can derive and produce information/analytics based on 5GC-compatible requests. This reduces complexity and increases flexibility for 5GC to assist application layer AI/ML operations performed by UE (e.g., application client) and authorized third party (e.g., application server). More generally, embodiments facilitate deployment of application layer AI/ML that relies on information from a communication network (e.g., 5GC), which can improve performance of applications - such as OTT services - that communicate via the communication network. This can increase the value of such OTT services to end users and service providers.

In an example scenario, factory status information may be collected and analyzed by the host 2102. As another example, the host 2102 may process audio and video data which may have been retrieved from a UE for use in creating maps. As another example, the host 2102 may collect and analyze real-time data to assist in controlling vehicle congestion (e.g., controlling traffic lights). As another example, the host 2102 may store surveillance video uploaded by a UE. As another example, the host 2102 may store or control access to media content such as video, audio, VR or AR which it can broadcast, multicast or unicast to UEs. As other examples, the host 2102 may be used for energy pricing, remote control of non-time critical electrical load to balance power generation needs, location services, presentation services (such as compiling diagrams etc. from data collected from remote devices), or any other function of collecting, retrieving, storing, analyzing and/or transmitting data.

In some examples, a measurement procedure may be provided for the purpose of monitoring data rate, latency and other factors on which the one or more embodiments improve. There may further be an optional network functionality for reconfiguring the OTT connection 2150 between the host 2102 and UE 2106, in response to variations in the measurement results. The measurement procedure and/or the network functionality for reconfiguring the OTT connection may be implemented in software and hardware of the host 2102 and/or UE 2106. In some embodiments, sensors (not shown) may be deployed in or in association with other devices through which the OTT connection 2150 passes; the sensors may participate in the measurement procedure by supplying values of the monitored quantities exemplified above, or supplying values of other physical quantities from which software may compute or estimate the monitored quantities The reconfiguring of the OTT connection 2150 may include message format, retransmission settings, preferred routing etc.; the reconfiguring need not directly alter the operation of the network node 2104. Such procedures and functionalities may be known and practiced in the art. In certain embodiments, measurements may involve proprietary UE signaling that facilitates measurements of throughput, propagation times, latency and the like, by the host 2102. The measurements may be implemented in that software causes messages to be transmitted, in particular empty or ‘dummy’ messages, using the OTT connection 2150 while monitoring propagation times, errors, etc.

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.

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.

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 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.

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.

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 these terms (and/or other terms that can be synonymous to one another) can be used synonymously herein, there can be instances when such words can be intended to not be used synonymously. Further, to the extent that the prior art knowledge has not been explicitly incorporated by reference herein above, it is explicitly incorporated herein in its entirety. All publications referenced are incorporated herein by reference in their entireties.

Claims

1. A method for an artificial intelligence/machine learning translator function (AIML-T) of a communication network, the method comprising: receiving one or more requests for AI/ML assistance information for one or more nodes that are endpoints for application layer AI/ML operations; translating the one or more requests for AI/ML assistance information into one or more requests for information and/or analytics from the communication network; sending the one or more requests for information and/or analytics to one or more network functions (NFs) of the communication network; receiving the requested information and/or analytics from the one or more NFs; translating the received information and/or analytics into the requested AI/ML assistance information; and sending the requested AI/ML assistance information to one of the following: the one or more nodes; or an application function (AF) of the communication network.

2. The method of claim 1, wherein the one or more nodes include one or more of the following: one or more user equipment (UEs), and one or more authorized third party servers.

3. The method of claim 1 or 2, wherein translating the one or more requests for AI/ML assistance information into one or more requests for information and/or analytics from the communication network comprises: determining the information and/or analytics to be requested based on the requests for AI/ML assistance information; selecting or discovering the one or more NFs based on capabilities to provide the information and/or analytics; and creating the one or more requests for information and/or analytics based on respective service operations of the one or more NFs.

4. The method of claim 3, wherein the one or more requests for information and/or analytics are sent to the one or more NFs via a network exposure function (NEF) of the communication network, and the requested information and/or analytics is received via the NEF.

5. The method of any of claims 1 to 4, wherein the AIML-T is part of a network exposure function (NEF) of the communication network.

6. The method of any of claims 1 to 4, wherein the AIML-T is part of an AF of the communication network that provides data collection and/or AI/ML operational assistance.

7. The method of any of claims 1 to 5, wherein one or more of the following applies: the one or more requests for AI/ML assistance information are received from the AF, and the requested AI/ML assistance information to the one or more nodes is sent to the one or more nodes via the AF; and the one or more requests for information and/or analytics are sent to the one or more NFs via the AF, and the requested information and/or analytics are received from the one or more NFs via the AF.

8. The method of any of claims 1 to 4, wherein: the AIML-T is part of a network exposure function (NEF) of the communication network; the one or more requests for AI/ML assistance information are received from the AF as respective service operations, such as Nnef_EventExposure_Subscribe service operations; and the requested AI/ML assistance information is sent to the AF using one or more service operations, such as Nnef_EventExposure_Notify service operations.

9. The method of claim 1 or 2, wherein: the one or more NFs include first and second NFs, with the AIML-T being part of the first NF; a first portion of the requested information and/or analytics is computed or obtained by the first NF; a request for information and/or analytics is sent to the second NF; and a second portion of the requested information and/or analytics is received from the second NF.

10. The method of claim 9, wherein the one or more requests for AI/ML assistance information are received from one of the following: the one or more nodes that are endpoints for AI/ML operations; or the AF.

11. The method of any of claims 1 to 10, wherein each request for AI/ML assistance information includes one or more of the following: type of application layer AI/ML operation for which AI/ML assistance information is requested; an identifier of the requesting node; type of AI/ML assistance information needed; and time window or interval for which AI/ML assistance information is requested.

12. The method of claim 11, wherein the type of application layer AI/ML operation is one of the following: split or distributed AI/ML operation, distribution or sharing of AI/ML models or data, and federated learning (FL).

13. The method of claim 11 or 12, wherein the request for AI/ML assistance information also includes one or more of the following parameters related to sending or receiving data to be used in the application layer AI/ML operation: one or more data rates; one or more thresholds for data transmission reliability, which could be per-data type or for all data types; one or more thresholds for communication service availability; one or more time periods of interest; and one or more areas of interest.

14. The method of claim 13, where the application layer AI/ML operation includes a plurality of transaction rounds among the endpoints, and each of the parameters included in the request is representative of one of the following: all of the transaction rounds, or respective individual transaction rounds.

15. The method of claim 13 or 14, wherein the request identifies a plurality of data types comprising the AI/ML assistance information, and the one or more thresholds for data transmission reliability include one of the following: a single threshold associated with all of the data types; or a plurality of thresholds, with each threshold associated with a different one of the data types.

16. The method of any of claims 1 to 15, wherein the one or more NFs include any of the following: network data analytics function (NWDAF), session management function (SMF), access and mobility management function (AMF), data collection coordination function (DCCF), analytics data repository function (ADRF).

17. The method of any of claims 1 to 16, wherein the AI/ML assistance information includes one or more of the following: first predictions about conditions in the communication network such as latency, bitrate, communication service availability, and reliability; second predictions about changes to the conditions in the communication network; third predictions about need for downloading ML models and/or data to be used with ML models existing in the endpoints; information about resource utilization in the communication network; and one or more time windows associated with one or more of the following that are present in the AI/ML assistance information: the first predictions, the second predictions, the third predictions, and the information about resource utilization.

18. A network exposure function (NEF) of a communication network, wherein the NEF comprises an artificial intelligence/machine learning translator function (AIML-T) being configured to perform operations corresponding to any of the methods of claims 1 to 17.

19. An artificial intelligence/machine learning translator function (AIML-T) of a communication network, wherein: the AIML-T is implemented by communication interface circuitry and processing circuitry that are operably coupled; and the processing circuitry and interface circuitry are configured to perform operations corresponding to any of the methods of claims 1 to 17.

20. An artificial intelligence/machine learning translator function (AIML-T) of a communication network, the AIML-T being configured to perform operations corresponding to any of the methods of claims 1 to 17.

21. A non-transitory, computer-readable medium storing computer-executable instructions that, when executed by processing circuitry associated with an artificial intelligence/machine learning translator function (AIML-T) of a communication network, configure the AIML-T to perform operations corresponding to any of the methods of claims 1 to 17.

22. A computer program product comprising computer-executable instructions that, when executed by processing circuitry associated with an artificial intelligence/machine learning translator function (AIML-T) of a communication network, configure the AIML-T to perform operations corresponding to any of the methods of claims 1 to 17.