EP4670336A1

EP4670336A1 - Improved latency performance analytics of a wireless device for supporting artificial intelligence/machine learning (AI/ML) operations at the application layer

Info

Publication number: EP4670336A1
Application number: EP24707936.1A
Authority: EP
Inventors: Jing Yue; Zhang FU
Original assignee: Telefonaktiebolaget LM Ericsson AB
Current assignee: Telefonaktiebolaget LM Ericsson AB
Priority date: 2023-02-21
Filing date: 2024-02-21
Publication date: 2025-12-31
Also published as: JP2026508989A; CN120731584A; WO2024176145A1

Abstract

A method, network data analytics function (NWDAF) and network function (NF) for enhancement wireless device (WD) latency performance analytics to assist application layer artificial intelligence/machine learning (AI/ML) operations are disclosed. According to one aspect, the method in an NWDAF includes collecting WD latency performance inputs from a network function, NF, the latency performance inputs including a plurality of transmitted data volume values and transmission time values. The method includes determining a set of latency performance analytics to assist a federated learning process, the set of latency performance analytics being based at least in part on the latency performance inputs and including at least one of latency predictions and latency statistics related to uplink and downlink transmissions between the NWDAF and the NF. The method also includes transmitting the set of latency performance analytics to the NF.

Description

ENHANCED WIRELESS DEVICE (WD) LATENCY PERFORMANCE ANALYTICS TO ASSIST APPLICATION LAYER ARTIFICIAL INTELLIGENCE/MACHINE

LEARNING (AI/ML) OPERATIONS

TECHNICAL FIELD

The present disclosure relates to wireless communications, and in particular, to enhancement wireless device (WD) latency performance analytics to assist application layer artificial intelligence (Al)/ machine learning (ML) operations.

BACKGROUND

The Third Generation Partnership Project (3GPP) has developed and is developing standards for Fourth Generation (4G) (also referred to as Long Term Evolution (LTE)) and Fifth Generation (5G) (also referred to as New Radio (NR)) wireless communication systems. Such systems provide, among other features, broadband communication between network nodes, such as base stations, and mobile wireless devices (WD) (also referred to as user equipment (UE)), as well as communication between network nodes and between WDs. The 3GPP is also developing standards for Sixth Generation (6G) wireless communication networks.

Conclusions on Latency Performance Analytics in 3GPP Technical Release (TR) 23.700-80

In 3GPP Technical Release (TR) 23.700-80, the conclusion of KI#7 for 5GS Assistance to Federated Learning Operation, the following description was provided:

The Application Function (AF) may request a network data analytics function (NWDAF) (via network exposure function (NEF) if needed) to provide existing, enhanced or new analytics to assist with federated learning operation. NWDAF Analytics services will be enhanced as follows:

- New latency performance analytics are introduced for NWDAF.

NOTE 1: The specific input and output parameters for the latency performance analytics among the group of WDs to assist the Application AI/ML FL operation will be determined during the normative phase.

Analytics ID: WD Latency Performance in CR#0616 (to be added into 3GPP TS 23.288) General

The clause 2.1.2.1 of 3GPP Technical Standard (TS) 23.288 describes how an NWDAF may provide WD latency performance analytics, in the form of statistics or predictions or both, to a service consumer. NWDAF collects WD latency performance related input data from 5G core network functions (NFs), operations, administration and maintenance (OAM) and application function (AF). The consumer may either subscribe to analytics notifications (i.e., a Subscribe-Notify model) or request a single notification (i.e., a Request-Response model).

The WD latency performance refers to a time delay for completing the transmission of a specific data volume from WD to AF, or from AF to WD. If an expected number of repeating data transmissions or an expected time interval between data transmissions is given, the WD latency performance may be provided as an average value of every data packet transmission latency performance within the Analytics target period. The WD latency performance analytics may be used to assist an AF hosting AFML-based services, e.g., for member selection of federated learning.

The WD latency performance analytics may be provided as defined in clause

2.1.2.3 of 3GPP 23.288 for a WD individually or a list of WDs.

The service consumer may be an NF (e.g., AF, or network exposure function (NEF)).

The consumer of these analytics may indicate in the request:

- Analytics ID = "WD Latency Performance";

- Target of Analytics Reporting: a single WD (subscription permanent identifier (SUPI)/generic public subscription identifier (GPSI)) or a group of WDs (a list of SUPIs/GPSIs);

- Analytics Filter Information optionally including:

- Data network name (DNN);

- S-network slice selection assistance information (NSSAI);

- Application ID;

- Area of Interest (AOI(s)): restricts the scope of the WD latency performance analytics to the provided area;

- An optional list of analytics subsets that are requested (see clause 2.1.2.3 of 3GPP TS 23.288);

- Data Volume uplink/downlink (UL/DL): indicates a specific data volume transmitted once from WD to AF, or from AF to WD;

- Quality of service (QoS) requirements (e.g., 5G QoS Identifier (5QI), QoS Characteristics); - Optionally, an expected number of repeating data transmissions within the Analytics target period;

- Optionally, an expected time interval between data transmissions;

- Optionally, a request for geographical distribution (i.e., the areas of interest (Aols)) of the WDs;

- An Analytics target period indicates the time period over which the statistics or predictions are requested;

- In a subscription, the Notification Correlation Id and the Notification Target Address are included;

- Optionally, preferred level of accuracy of the analytics;

- Optional preferred order of results for the list of WD latency performance:

- ordering criterion: "WD latency performance";

- order: ascending or descending;

- Optionally, Reporting Thresholds, which indicate conditions on the level to be reached for respective analytics subsets (see clause 2.1.2.3 of 3GPP TS 23.288) in order to be notified by the NWDAF; e.g., NWDAF may provide the percentage of WDs that have reached certain Reporting Threshold(s);

- Optionally, maximum number of WDs.

Input Data

The NWDAF supporting analytics on WD latency performance should be able to collect WD performance information from AF, operations, administration and maintenance (OAM) and 5GC NFs.

More detailed information collected by the NWDAF from the OAM is defined in the Table 2.1.2.2-1, and from relevant 5GC NFs (i.e., user plane function (UPF), session management function (SMF), access and mobility management function (AMF)) is defined in Table 2.1.2.2-2.

Table 2.1.2.2-1: Input data from OAM related to WD latency performance

NWDAF subscribes the network data from OAM in the Table 2.1.2.2-1 by using the services provided by OAM as described in clause 6.2.3 in 3GPP TS 23.288 .

NOTE 1: Whether the WD(s) supports a Slice may be checked by retrieving the registered access and mobility management function (AMF) details from unified data management (UDM) or by asking AMF about what Slice is used by the WD(s) at the current registration. (Alternatively, if a network slice admission control function (NSACF) is deployed, the NSACF may provide a report on what slices are used by the WD(s)).

NOTE 2: User consent checking from UDM may apply for these analytics.

Table 2.1.2.2-2: Service Data from 5GC NFs for WD latency performance analytics

Output Analytics

The NWDAF supporting WD latency performance analytics provides the analytics results to consumer NFs, e.g., AF, or NEF. The analytics results provided by the NWDAF could be WD latency performance statistics as defined in Table 2.1.2.3-1 or predictions as defined in Table 2.1.2.3-2.

Table 2.1.2.3-1: WD Latency performance statistics

Table 2.1.2.3 -2: WD latency performance predictions

An example procedure for WD latency performance analytics is shown in FIG. 1.

Procedures

The NWDAF may provide WD latency performance analytics to a 5GC NF (e.g., AF, or NEF): 1. The Consumer NF, e.g., AF, or NEF, requests or subscribes to analytics for WD latency performance analytics from NWDAF (possibly via NEF in case the consumer NF is an AF) and provides the input information as specified in 2.1.2.1 to 5GC;

2a-b. The NWDAF subscribes the service data from AMF in Table 2.1.2.2-2 using Namf_EventExposure_Subscribe service for collecting WD location(s) for a WD or a group of WDs; NOTE: If the NWDAF requires WD location information with finer granularity than

TA/cell, then the NWDAF collects the location data from the GMEC instead of the AMF;

2c. NWDAF subscribes to service data from SMF in Table 2.1.2.2-2 by invoking Nsmf_EventExposure_Subscribe (Event ID, SUPI(s) or Application ID).

In order to provide the requested analytics, the NWDAF subscribes to information of the WD and may subscribe to N4 Session related input data from SMFs as defined in Table 2.1.2.2-2;

2d-e. N4 related input data is provided by UPF to SMF;

2f. SMF provides the requested input data to NWDAF;

2g-h. The NWDAF may subscribe to input data in Table 2.1.2.2-1 from the OAM according to the data collection principles from the OAM described in clause 6.2.3 of 3GPP TS 23.288;

3. The NWDAF derives requested analytics, in the form of latency performance statistics or predictions or both;

4. The NWDAF provides requested latency performance analytics to the NF, using either Nnwdaf_AnalyticsInfo_Request response or

Nnwdaf_AnalyticsSubscription_Notify, depending on the service used in step 1 ; and 5-7. If the NF subscribed to WD latency performance analytics at step 1, when the

NWDAF generates new analytics, it notifies the new generated analytics to the consumer.

However, in the current version of the new Analytics ID (WD Latency Performance), the specific characters for the AI/ML traffic (for Federated Learning) are not considered.

The new Analytics ID for WD Latency Performance has been proposed on SA2#154-adhoc-e meeting for assist Application Layer Federated Learning (FL) operations. As described in the approved CR#0616 (to be added into 3GPP TS 23.288 ):

Latency performances of a WD group are output information in the WD latency performance analytics to evaluate the performance of a group WD to assist federated learning. The motivation for introducing WD latency performance analytics is to provide the latency related performance of a group of WDs to assist AI/ML service, particularly member selection of federated learning in this release. The performance of federated learning is based on the service quality of a group of WDs. Therefore, the output based on the performance of a group of WDs is necessarily needed for the NEF and also AF (in this release) to select WD members and evaluate the WD performance during model training. The statics and prediction of the performance of a group of WDs should be done by NWDAF and provided to the consumers.

The characters of AI/ML traffic (e.g., for Federated Learning) are quite different from the other normal traffics, e.g., retransmitting the same AI/ML data or transmitting different AI/ML data in multiple time intervals with and without ultra-low latency requirements, different data volume of various AI/ML data to be transmitted, and different transmission conditions for AI/ML data transmission among different network entities, etc. However, the new Analytics ID does not consider distinguishing the specific characters of AI/ML traffic from the other normal traffic, which may cause the analytics results to be unusable for assisting the application layer AI/ML operations (e.g., FL) effectively.

SUMMARY

Some embodiments advantageously provide methods and network nodes for enhancement wireless device (WD) latency performance analytics to assist application layer artificial intelligence (Al)/ machine learning (ML) operations.

In some embodiments, the specific characters of AI/ML traffic for Federated Learning are considered with latency related analytics on AI/ML traffics. New inputs and outputs are added to the Analytics ID WD Latency Performance.

In some embodiments, the characters of AI/ML traffic for Federated Learning are considered. These may include one or more of the following:

■ The existing inputs are enhanced, e.g., Transmitted data volume and Transmission time;

■ New inputs are added to the Analytics ID WD Latency Performance, including Time relevant to the round trip, (list of) Pair Timestamps (start time and end time), IP filter information, Locations of Application, Application Server Instance address; and/or

■ New outputs are added, including:

Estimated number of transmissions (statistics/predictions);

Statistics/Predictions on UL latency for target transmission; Statistics/Predictions on DL latency for target transmission; Statistics/Predictions on Round trip latency for target transmission; and/or

Statistics/Predictions on Maximum latency for target transmission. Some embodiments consider the specific characters of AI/ML traffic for e.g., Federated Learning (FL), and focus on the latency related analytics on AI/ML traffics. The existing inputs are enhanced and new inputs and outputs are added to the Analytics ID WD Latency Performance to enable the analytics to assist Application layer AI/ML operations (e.g., Federated Learning).

An Analytics ID for WD Latency Performance has been considered in the SA2#154-adhoc-e meeting for assist Application Layer Federated Learning operations. However, the new Analytics ID does not consider distinguishing the specific characters of AI/ML traffic (e.g., for FL) from other traffic. This may result in the analytics results not being used for assisting the application layer AI/ML operations (e.g., FL), efficiently.

According to one aspect, a method in a core network node configured to include a network data analytics function, NWDAF, is provided. The method includes collecting wireless device, WD, latency performance inputs from a network function, NF, the latency performance inputs including a plurality of transmitted data volume values and transmission time values. The method includes determining a set of latency performance analytics to assist a federated learning process, the set of latency performance analytics being based at least in part on the latency performance inputs and including at least one of latency predictions and latency statistics related to uplink and downlink transmission latency between the WD and an application function, AF. The method also includes transmitting the set of latency performance analytics to the NF.

According to this aspect, in some embodiments, the at least one of latency predictions and latency statistics include a time delay for completing a transmission of a target volume of data from a WD to the NF or from the NF to the WD. In some embodiments, the at least one of latency predictions and latency statistics include an estimated number of retransmissions for completing the transmission of the target volume of data. In some embodiments, the at least one of latency predictions and latency statistics include at least one of an uplink packet delay, a downlink patent delay and a round trip packet delay. In some embodiments, the at least one of latency predictions and latency statistics include values that are averaged over an analytics target period. In some embodiments, the at least one of latency predictions and latency statistics include a validity period for the latency performance analytics. In some embodiments, the at least one of latency predictions and latency statistics include a maximum packet delay observed for communicating with the NF. In some embodiments, the at least one of latency predictions and latency statistics include a spatial validity parameter indicative of an area over which the latency performance analytics apply. In some embodiments, the at least one of latency predictions and latency statistics are grouped into latency classes according to latency performance ranges. In some embodiments, the at least one of latency predictions and latency statistics include an indication of a percentage of WDs in each latency class. In some embodiments, the NF is implemented as the application function.

According to another aspect, a core network node configured to include a network data analytics function, NWDAF, is provided. The core network node is configured to collect wireless device, WD, latency performance inputs from a network function, NF, the latency performance inputs including a plurality of transmitted data volume values and transmission time values. The core network node is configured to determine a set of latency performance analytics to assist a federated learning process, the set of latency performance analytics being based at least in part on the latency performance inputs and including at least one of latency predictions and latency statistics related to uplink and downlink transmission latency between the WD and an application function, AF. The core network node is also configured to transmit the set of latency performance analytics to the NF.

According to this aspect, in some embodiments, the at least one of latency predictions and latency statistics include a time delay for completing a transmission of a target volume of data from a WD to the NF or from the NF to the WD. In some embodiments, the at least one of latency predictions and latency statistics include an estimated number of retransmissions for completing the transmission of the target volume of data. In some embodiments, the at least one of latency predictions and latency statistics include at least one of an uplink packet delay, a downlink patent delay and a round trip packet delay. In some embodiments, the at least one of latency predictions and latency statistics include values that are averaged over an analytics target period. In some embodiments, the at least one of latency predictions and latency statistics include a validity period for the latency performance analytics. In some embodiments, the at least one of latency predictions and latency statistics include a maximum packet delay observed for communicating with the NF. In some embodiments, the at least one of latency predictions and latency statistics include a spatial validity parameter indicative of an area over which the latency performance analytics apply. In some embodiments, the at least one of latency predictions and latency statistics are grouped into latency classes according to latency performance ranges. In some embodiments, the at least one of latency predictions and latency statistics include a an indication of a percentage of WDs in each latency class. In some embodiments, the NF is implemented as the application function.

According to yet another aspect, a method is provided in a network node configured to include a network function, NF, and configured to communicate with a network data analytics function, NWDAF. The method includes transmitting to the NWDAF, latency performance inputs, the latency performance inputs including a plurality of transmitted data volume values and transmission time values. The method includes receiving a set of latency performance analytics to assist a federated learning process, the set of latency performance analytics being based at least in part on the latency performance inputs and including at least one of latency predictions and latency statistics related to uplink and downlink transmission latency between the NWDAF and the NF. The method also includes hosting artificial intelligence/machine learning, AFML, -based services based at least in part on the federated learning process.

According to another aspect, a network node configured to include a network function, NF, and configured to communicate with a network data analytics function, NWDAF, is provided. The network node is configured to transmit to the NWDAF, latency performance inputs, the latency performance inputs including a plurality of transmitted data volume values and transmission time values. The network node is also configured to receive a set of latency performance analytics to assist a federated learning process, the set of latency performance analytics being based at least in part on the latency performance inputs and including at least one of latency predictions and latency statistics related to uplink and downlink transmission latency between the NWDAF and the NF. The network node is further configured to host artificial intelligence/machine learning, AI/ML, -based services based at least in part on the federated learning process.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete understanding of the present embodiments, and the attendant advantages and features thereof, will be more readily understood by reference to the following detailed description when considered in conjunction with the accompanying drawings wherein:

FIG. 1 is a procedure WD latency performance analytics;

FIG. 2 is a schematic diagram of an example network architecture illustrating a communication system connected via an intermediate network to a host computer according to the principles in the present disclosure;

FIG. 3 is a block diagram of a host computer communicating via a network node with a wireless device over an at least partially wireless connection according to some embodiments of the present disclosure;

FIG. 4 is a flowchart illustrating example methods implemented in a communication system including a host computer, a network node and a wireless device for executing a client application at a wireless device according to some embodiments of the present disclosure;

FIG. 5 is a flowchart illustrating example methods implemented in a communication system including a host computer, a network node and a wireless device for receiving user data at a wireless device according to some embodiments of the present disclosure;

FIG. 6 is a flowchart illustrating example methods implemented in a communication system including a host computer, a network node and a wireless device for receiving user data from the wireless device at a host computer according to some embodiments of the present disclosure;

FIG. 7 is a flowchart illustrating example methods implemented in a communication system including a host computer, a network node and a wireless device for receiving user data at a host computer according to some embodiments of the present disclosure;

FIG. 8 is a flowchart of an example process in a network node for enhancement wireless device (WD) latency performance analytics to assist application layer artificial intelligence (Al)/ machine learning (ML) operations;

FIG. 9 is a flowchart of an example process in a network node configured to include a network data analytics function, NWDAF, in accordance with principles disclosed herein; and

FIG. 10 is a flowchart of an example process in a network node configured to include a network function, NF, and configured to communicate with a network data analytics function, NWDAF, in accordance with principles disclosed herein. DETAILED DESCRIPTION

Before describing in detail example embodiments, it is noted that the embodiments reside primarily in combinations of apparatus components and processing steps related to enhancement wireless device (WD) latency performance analytics to assist application layer artificial intelligence (Al)/ machine learning (ML) operations. Accordingly, components have been represented where appropriate by conventional symbols in the drawings, showing only those specific details that are pertinent to understanding the embodiments so as not to obscure the disclosure with details that will be readily apparent to those of ordinary skill in the art having the benefit of the description herein. Like numbers refer to like elements throughout the description.

As used herein, relational terms, such as “first” and “second,” “top” and “bottom,” and the like, may be used solely to distinguish one entity or element from another entity or element without necessarily requiring or implying any physical or logical relationship or order between such entities or elements. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the concepts described herein. As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises,” “comprising,” “includes” and/or “including” when used herein, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

In embodiments described herein, the joining term, “in communication with” and the like, may be used to indicate electrical or data communication, which may be accomplished by physical contact, induction, electromagnetic radiation, radio signaling, infrared signaling or optical signaling, for example. One having ordinary skill in the art will appreciate that multiple components may interoperate and modifications and variations are possible of achieving the electrical and data communication.

In some embodiments described herein, the term “coupled,” “connected,” and the like, may be used herein to indicate a connection, although not necessarily directly, and may include wired and/or wireless connections.

The term “network node” used herein may be any kind of network node comprised in a radio network which may further comprise any of base station (BS), radio base station, base transceiver station (BTS), base station controller (BSC), radio network controller (RNC), g Node B (gNB), evolved Node B (eNB or eNodeB), Node B, multistandard radio (MSR) radio node such as MSR BS, multi-cell/multicast coordination entity (MCE), integrated access and backhaul (IAB) node, relay node, donor node controlling relay, radio access point (AP), transmission points, transmission nodes, Remote Radio Unit (RRU) Remote Radio Head (RRH), a core network node (e.g., mobile management entity (MME), self-organizing network (SON) node, a coordinating node, positioning node, MDT node, etc.), an external node (e.g., 3rd party node, a node external to the current network), nodes in distributed antenna system (DAS), a spectrum access system (SAS) node, an element management system (EMS), etc. The network node may also comprise test equipment. The term “radio node” used herein may be used to also denote a wireless device (WD) such as a wireless device (WD) or a radio network node.

In some embodiments, the non-limiting terms wireless device (WD) or a user equipment (UE) are used interchangeably. The WD herein may be any type of wireless device capable of communicating with a network node or another WD over radio signals, such as wireless device (WD). The WD may also be a radio communication device, target device, device to device (D2D) WD, machine type WD or WD capable of machine to machine communication (M2M), low-cost and/or low-complexity WD, a sensor equipped with WD, Tablet, mobile terminals, smart phone, laptop embedded equipped (LEE), laptop mounted equipment (LME), USB dongles, Customer Premises Equipment (CPE), an Internet of Things (loT) device, or a Narrowband loT (NB-IOT) device, etc.

Also, in some embodiments the generic term “radio network node” is used. It may be any kind of a radio network node which may comprise any of base station, radio base station, base transceiver station, base station controller, network controller, RNC, evolved Node B (eNB), Node B, gNB, Multi-cell/multicast Coordination Entity (MCE), IAB node, relay node, access point, radio access point, Remote Radio Unit (RRU) Remote Radio Head (RRH).

Note that although terminology from one particular wireless system, such as, for example, 3GPP LTE and/or New Radio (NR), may be used in this disclosure, this should not be seen as limiting the scope of the disclosure to only the aforementioned system. Other wireless systems, including without limitation Wide Band Code Division Multiple Access (WCDMA), Worldwide Interoperability for Microwave Access (WiMax), Ultra Mobile Broadband (UMB) and Global System for Mobile Communications (GSM), may also benefit from exploiting the ideas covered within this disclosure.

Note further, that functions 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. In other words, it is contemplated that the functions of the network node and wireless device described herein are not limited to performance by a single physical device and, in fact, may be distributed among several physical devices.

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.

Some embodiments provide enhancement wireless device (WD) latency performance analytics to assist application layer artificial intelligence (Al)/ machine learning (ML) operations.

Referring again to the drawing figures, in which like elements are referred to by like reference numerals, there is shown in FIG. 2 a schematic diagram of a communication system 10, according to an embodiment, such as a 3GPP-type cellular network that may support standards such as LTE and/or NR (5G), which comprises an access network 12, such as a radio access network, and a core network 14. The access network 12 comprises a plurality of network nodes 16a, 16b, 16c (referred to collectively as network nodes 16), such as NBs, eNBs, gNBs or other types of wireless access points, each defining a corresponding coverage area 18a, 18b, 18c (referred to collectively as coverage areas 18). Each network node 16a, 16b, 16c is connectable to the core network 14 over a wired or wireless connection 20. A first wireless device (WD) 22a located in coverage area 18a is configured to wirelessly connect to, or be paged by, the corresponding network node 16a. A second WD 22b in coverage area 18b is wirelessly connectable to the corresponding network node 16b. While a plurality of WDs 22a, 22b (collectively referred to as wireless devices 22) are illustrated in this example, the disclosed embodiments are equally applicable to a situation where a sole WD is in the coverage area or where a sole WD is connecting to the corresponding network node 16. Note that although only two WDs 22 and three network nodes 16 are shown for convenience, the communication system may include many more WDs 22 and network nodes 16.

Also, it is contemplated that a WD 22 may be in simultaneous communication and/or configured to separately communicate with more than one network node 16 and more than one type of network node 16. For example, a WD 22 may have dual connectivity with a network node 16 that supports LTE and the same or a different network node 16 that supports NR. As an example, WD 22 may be in communication with an eNB for LTE/E-UTRAN and a gNB for NR/NG-RAN.

The communication system 10 may itself be connected to a host computer 24, which may be embodied in the hardware and/or software of a standalone server, a cloud- implemented server, a distributed server or as processing resources in a server farm. The host computer 24 may be under the ownership or control of a service provider, or may be operated by the service provider or on behalf of the service provider. The connections 26, 28 between the communication system 10 and the host computer 24 may extend directly from the core network 14 to the host computer 24 or may extend via an optional intermediate network 30. The intermediate network 30 may be one of, or a combination of more than one of, a public, private or hosted network. The intermediate network 30, if any, may be a backbone network or the Internet. In some embodiments, the intermediate network 30 may comprise two or more sub-networks (not shown).

The communication system of FIG. 2 as a whole enables connectivity between one of the connected WDs 22a, 22b and the host computer 24. The connectivity may be described as an over-the-top (OTT) connection. The host computer 24 and the connected WDs 22a, 22b are configured to communicate data and/or signaling via the OTT connection, using the access network 12, the core network 14, any intermediate network 30 and possible further infrastructure (not shown) as intermediaries. The OTT connection may be transparent in the sense that at least some of the participating communication devices through which the OTT connection passes are unaware of routing of uplink and downlink communications. For example, a network node 16 may not or need not be informed about the past routing of an incoming downlink communication with data originating from a host computer 24 to be forwarded (e.g., handed over) to a connected WD 22a. Similarly, the network node 16 need not be aware of the future routing of an outgoing uplink communication originating from the WD 22a towards the host computer 24.

A network node 16 is configured to include an AI/ML unit which may be configured to implement a federated learning process that includes multiple-input artificial intelligence/machine learning, AI/ML, the multiple inputs including transmission data information. A network node 16 may be configured to include an NF 32. The NF 32 may be configured to host AI/ML-based services based at least in part on a federated learning process. A core network node 36 in the core network 14 may include an NWDAF 34 while a network node 16 may include the NF 32 that receives the latency performance analytics from the core network node 36. The NWDAF 34 may be configured to determine a set of latency performance analytics to assist a federated learning process, the set of latency performance analytics being based at least in part on the latency performance inputs and including at least one of latency predictions and latency statistics related to uplink and downlink transmission latency between the WD 22 and an application function, AF.

Example implementations, in accordance with an embodiment, of the WD 22, network node 16, host computer 24 and core network node 36 discussed in the preceding paragraphs will now be described with reference to FIG. 3. In a communication system 10, a host computer 24 comprises hardware (HW) 38 including a communication interface 40 configured to set up and maintain a wired or wireless connection with an interface of a different communication device of the communication system 10. The host computer 24 further comprises processing circuitry 42, which may have storage and/or processing capabilities. The processing circuitry 42 may include a processor 44 and memory 46. In particular, in addition to or instead of a processor, such as a central processing unit, and memory, the processing circuitry 42 may comprise integrated circuitry for processing and/or control, e.g., one or more processors and/or processor cores and/or FPGAs (Field Programmable Gate Array) and/or ASICs (Application Specific Integrated Circuitry) adapted to execute instructions. The processor 44 may be configured to access (e.g., write to and/or read from) memory 46, which may comprise any kind of volatile and/or nonvolatile memory, e.g., cache and/or buffer memory and/or RAM (Random Access Memory) and/or ROM (Read-Only Memory) and/or optical memory and/or EPROM (Erasable Programmable Read-Only Memory).

Processing circuitry 42 may be configured to control any of the methods and/or processes described herein and/or to cause such methods, and/or processes to be performed, e.g., by host computer 24. Processor 44 corresponds to one or more processors 44 for performing host computer 24 functions described herein. The host computer 24 includes memory 46 that is configured to store data, programmatic software code and/or other information described herein. In some embodiments, the software 48 and/or the host application 50 may include instructions that, when executed by the processor 44 and/or processing circuitry 42, causes the processor 44 and/or processing circuitry 42 to perform the processes described herein with respect to host computer 24. The instructions may be software associated with the host computer 24. The software 48 may be executable by the processing circuitry 42. The software 48 includes a host application 50. The host application 50 may be operable to provide a service to a remote user, such as a WD 22 connecting via an OTT connection 52 terminating at the WD 22 and the host computer 24. In providing the service to the remote user, the host application 50 may provide user data which is transmitted using the OTT connection 52. The “user data” may be data and information described herein as implementing the described functionality. In one embodiment, the host computer 24 may be configured for providing control and functionality to a service provider and may be operated by the service provider or on behalf of the service provider. The processing circuitry 42 of the host computer 24 may enable the host computer 24 to observe, monitor, control, transmit to and/or receive from the network node 16 and or the wireless device 22.

The communication system 10 further includes a network node 16 provided in a communication system 10 and including hardware 58 enabling it to communicate with the host computer 24 and with the WD 22. The hardware 58 may include a communication interface 60 for setting up and maintaining a wired or wireless connection with an interface of a different communication device of the communication system 10, as well as a radio interface 62 for setting up and maintaining at least a wireless connection 64 with a WD 22 located in a coverage area 18 served by the network node 16. The radio interface 62 may be formed as or may include, for example, one or more RF transmitters, one or more RF receivers, and/or one or more RF transceivers. The communication interface 60 may be configured to facilitate a connection 66 to the host computer 24. The connection 66 may be direct or it may pass through a core network 14 of the communication system 10 and/or through one or more intermediate networks 30 outside the communication system 10.

In the embodiment shown, the hardware 58 of the network node 16 further includes processing circuitry 68. The processing circuitry 68 may include a processor 70 and a memory 72. In particular, in addition to or instead of a processor, such as a central processing unit, and memory, the processing circuitry 68 may comprise integrated circuitry for processing and/or control, e.g., one or more processors and/or processor cores and/or FPGAs (Field Programmable Gate Array) and/or ASICs (Application Specific Integrated Circuitry) adapted to execute instructions. The processor 70 may be configured to access (e.g., write to and/or read from) the memory 72, which may comprise any kind of volatile and/or nonvolatile memory, e.g., cache and/or buffer memory and/or RAM (Random Access Memory) and/or ROM (Read-Only Memory) and/or optical memory and/or EPROM (Erasable Programmable Read-Only Memory).

Thus, the network node 16 further has software 74 stored internally in, for example, memory 72, or stored in external memory (e.g., database, storage array, network storage device, etc.) accessible by the network node 16 via an external connection. The software 74 may be executable by the processing circuitry 68. The processing circuitry 68 may be configured to control any of the methods and/or processes described herein and/or to cause such methods, and/or processes to be performed, e.g., by network node 16. Processor 70 corresponds to one or more processors 70 for performing network node 16 functions described herein. The memory 72 is configured to store data, programmatic software code and/or other information described herein. In some embodiments, the software 74 may include instructions that, when executed by the processor 70 and/or processing circuitry 68, causes the processor 70 and/or processing circuitry 68 to perform the processes described herein with respect to network node 16. For example, processing circuitry 68 of the network node 16 may include an AI/ML unit which may be configured to implement a federated learning process that includes multiple-input artificial intelligence/machine learning, AI/ML, the multiple inputs including transmission data information The NF 32 may be configured to host AI/ML-based services based at least in part on a federated learning process. In some embodiments, the NWDAF 34 may also be configured in the network node 16.

The communication system 10 further includes the WD 22 already referred to. The WD 22 may have hardware 80 that may include a radio interface 82 configured to set up and maintain a wireless connection 64 with a network node 16 serving a coverage area 18 in which the WD 22 is currently located. The radio interface 82 may be formed as or may include, for example, one or more RF transmitters, one or more RF receivers, and/or one or more RF transceivers.

The hardware 80 of the WD 22 further includes processing circuitry 84. The processing circuitry 84 may include a processor 86 and memory 88. In particular, in addition to or instead of a processor, such as a central processing unit, and memory, the processing circuitry 84 may comprise integrated circuitry for processing and/or control, e.g., one or more processors and/or processor cores and/or FPGAs (Field Programmable Gate Array) and/or ASICs (Application Specific Integrated Circuitry) adapted to execute instructions. The processor 86 may be configured to access (e.g., write to and/or read from) memory 88, which may comprise any kind of volatile and/or nonvolatile memory, e.g., cache and/or buffer memory and/or RAM (Random Access Memory) and/or ROM (Read-Only Memory) and/or optical memory and/or EPROM (Erasable Programmable Read-Only Memory).

Thus, the WD 22 may further comprise software 90, which is stored in, for example, memory 88 at the WD 22, or stored in external memory (e.g., database, storage array, network storage device, etc.) accessible by the WD 22. The software 90 may be executable by the processing circuitry 84. The software 90 may include a client application 92. The client application 92 may be operable to provide a service to a human or non-human user via the WD 22, with the support of the host computer 24. In the host computer 24, an executing host application 50 may communicate with the executing client application 92 via the OTT connection 52 terminating at the WD 22 and the host computer 24. In providing the service to the user, the client application 92 may receive request data from the host application 50 and provide user data in response to the request data. The OTT connection 52 may transfer both the request data and the user data. The client application 92 may interact with the user to generate the user data that it provides.

The processing circuitry 84 may be configured to control any of the methods and/or processes described herein and/or to cause such methods, and/or processes to be performed, e.g., by WD 22. The processor 86 corresponds to one or more processors 86 for performing WD 22 functions described herein. The WD 22 includes memory 88 that is configured to store data, programmatic software code and/or other information described herein. In some embodiments, the software 90 and/or the client application 92 may include instructions that, when executed by the processor 86 and/or processing circuitry 84, causes the processor 86 and/or processing circuitry 84 to perform the processes described herein with respect to WD 22.

The core network node 36 may include processing circuitry 94 that includes a processor and memory (not shown). The processor may include a central processing unit and/or processing circuitry that includes integrated circuitry for processing and/or control, e.g., one or more processors and/or processor cores and/or FPGAs (Field Programmable Gate Array) and/or ASICs (Application Specific Integrated Circuitry) adapted to execute instructions. The processor may be configured to access (e.g., write to and/or read from) memory, which may comprise any kind of volatile and/or nonvolatile memory, e.g., cache and/or buffer memory and/or RAM (Random Access Memory) and/or ROM (Read-Only Memory) and/or optical memory and/or EPROM (Erasable Programmable Read-Only Memory).

Thus, the core network node 36 may further comprise software stored in memory at the core network node, or stored in external memory (e.g., database, storage array, network storage device, etc.) accessible by the core network node. The software may be executable by the processing circuitry 94 to perform the functions of the NWDAF as described herein. The processing circuitry 94 may be configured to include the NWDAF 34 which may be configured to determine a set of latency performance analytics to assist a federated learning process. In some embodiments, the NF 32 may also be configured in the core network node 36.

The core network node 36 may also include a radio interface 96 configured to set up and maintain a wireless connection with the network node 16 serving a coverage area 18 in which a WD 22 is currently located. The radio interface 96 may be formed as or may include, for example, one or more RF transmitters, one or more RF receivers, and/or one or more RF transceivers. In some embodiments, the inner workings of the network node 16, WD 22, host computer 24 and core network node 36 may be as shown in FIG. 3 and independently, the surrounding network topology may be that of FIG. 2.

In FIG. 3, the OTT connection 52 has been drawn abstractly to illustrate the communication between the host computer 24 and the wireless device 22 via the network node 16, without explicit reference to any intermediary devices and the precise routing of messages via these devices. Network infrastructure may determine the routing, which it may be configured to hide from the WD 22 or from the service provider operating the host computer 24, or both. While the OTT connection 52 is active, the network infrastructure may further take decisions by which it dynamically changes the routing (e.g., on the basis of load balancing consideration or reconfiguration of the network).

The wireless connection 64 between the WD 22 and the network node 16 is in accordance with the teachings of the embodiments described throughout this disclosure. One or more of the various embodiments improve the performance of OTT services provided to the WD 22 using the OTT connection 52, in which the wireless connection 64 may form the last segment. More precisely, the teachings of some of these embodiments may improve the data rate, latency, and/or power consumption and thereby provide benefits such as reduced user waiting time, relaxed restriction on file size, better responsiveness, extended battery lifetime, etc.

In some embodiments, 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 52 between the host computer 24 and WD 22, in response to variations in the measurement results. The measurement procedure and/or the network functionality for reconfiguring the OTT connection 52 may be implemented in the software 48 of the host computer 24 or in the software 90 of the WD 22, or both. In embodiments, sensors (not shown) may be deployed in or in association with communication devices through which the OTT connection 52 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 48, 90 may compute or estimate the monitored quantities. The reconfiguring of the OTT connection 52 may include message format, retransmission settings, preferred routing etc.; the reconfiguring need not affect the network node 16, and it may be unknown or imperceptible to the network node 16. Some such procedures and functionalities may be known and practiced in the art. In certain embodiments, measurements may involve proprietary WD signaling facilitating the host computer’s 24 measurements of throughput, propagation times, latency and the like. In some embodiments, the measurements may be implemented in that the software 48, 90 causes messages to be transmitted, in particular empty or ‘dummy’ messages, using the OTT connection 52 while it monitors propagation times, errors, etc.

Thus, in some embodiments, the host computer 24 includes processing circuitry 42 configured to provide user data and a communication interface 40 that is configured to forward the user data to a cellular network for transmission to the WD 22. In some embodiments, the cellular network also includes the network node 16 with a radio interface 62. In some embodiments, the network node 16 is configured to, and/or the network node’s 16 processing circuitry 68 is configured to perform the functions and/or methods described herein for preparing/initiating/maintaining/ supporting/ending a transmission to the WD 22, and/or preparing/terminating/ maintaining/supporting/ending in receipt of a transmission from the WD 22.

In some embodiments, the host computer 24 includes processing circuitry 42 and a communication interface 40 that is configured to a communication interface 40 configured to receive user data originating from a transmission from a WD 22 to a network node 16. In some embodiments, the WD 22 is configured to, and/or comprises a radio interface 82 and/or processing circuitry 84 configured to perform the functions and/or methods described herein for preparing/initiating/maintaining/ supporting/ending a transmission to the network node 16, and/or preparing/ terminating/maintaining/supporting/ending in receipt of a transmission from the network node 16.

Although FIGS. 2 and 3 show various “units” such as AI/ML unit 32 as being within a respective processor, it is contemplated that these units may be implemented such that a portion of the unit is stored in a corresponding memory within the processing circuitry. In other words, the units may be implemented in hardware or in a combination of hardware and software within the processing circuitry.

FIG. 4 is a flowchart illustrating an example method implemented in a communication system, such as, for example, the communication system of FIGS. 2 and 3, in accordance with one embodiment. The communication system may include a host computer 24, a network node 16 and a WD 22, which may be those described with reference to FIG. 3. In a first step of the method, the host computer 24 provides user data (Block S100). In an optional substep of the first step, the host computer 24 provides the user data by executing a host application, such as, for example, the host application 50 (Block S102). In a second step, the host computer 24 initiates a transmission carrying the user data to the WD 22 (Block S104). In an optional third step, the network node 16 transmits to the WD 22 the user data which was carried in the transmission that the host computer 24 initiated, in accordance with the teachings of the embodiments described throughout this disclosure (Block S106). In an optional fourth step, the WD 22 executes a client application, such as, for example, the client application 92, associated with the host application 50 executed by the host computer 24 (Block S108).

FIG. 5 is a flowchart illustrating an example method implemented in a communication system, such as, for example, the communication system of FIG. 2, in accordance with one embodiment. The communication system may include a host computer 24, a network node 16 and a WD 22, which may be those described with reference to FIGS. 2 and 3. In a first step of the method, the host computer 24 provides user data (Block SI 10). In an optional substep (not shown) the host computer 24 provides the user data by executing a host application, such as, for example, the host application 50. In a second step, the host computer 24 initiates a transmission carrying the user data to the WD 22 (Block SI 12). The transmission may pass via the network node 16, in accordance with the teachings of the embodiments described throughout this disclosure. In an optional third step, the WD 22 receives the user data carried in the transmission (Block SI 14).

FIG. 6 is a flowchart illustrating an example method implemented in a communication system, such as, for example, the communication system of FIG. 2, in accordance with one embodiment. The communication system may include a host computer 24, a network node 16 and a WD 22, which may be those described with reference to FIGS. 2 and 3. In an optional first step of the method, the WD 22 receives input data provided by the host computer 24 (Block SI 16). In an optional substep of the first step, the WD 22 executes the client application 92, which provides the user data in reaction to the received input data provided by the host computer 24 (Block SI 18). Additionally or alternatively, in an optional second step, the WD 22 provides user data (Block S120). In an optional substep of the second step, the WD provides the user data by executing a client application, such as, for example, client application 92 (Block S122). In providing the user data, the executed client application 92 may further consider user input received from the user. Regardless of the specific manner in which the user data was provided, the WD 22 may initiate, in an optional third substep, transmission of the user data to the host computer 24 (Block SI 24). In a fourth step of the method, the host computer 24 receives the user data transmitted from the WD 22, in accordance with the teachings of the embodiments described throughout this disclosure (Block S126).

FIG. 7 is a flowchart illustrating an example method implemented in a communication system, such as, for example, the communication system of FIG. 2, in accordance with one embodiment. The communication system may include a host computer 24, a network node 16 and a WD 22, which may be those described with reference to FIGS. 2 and 3. In an optional first step of the method, in accordance with the teachings of the embodiments described throughout this disclosure, the network node 16 receives user data from the WD 22 (Block S128). In an optional second step, the network node 16 initiates transmission of the received user data to the host computer 24 (Block SI 30). In a third step, the host computer 24 receives the user data carried in the transmission initiated by the network node 16 (Block SI 32).

FIG. 8 is a flowchart of an example process in a network node 16 for enhancement wireless device (WD) latency performance analytics to assist application layer artificial intelligence (Al)/ machine learning (ML) operations. One or more blocks described herein may be performed by one or more elements of network node 16 such as by one or more of processing circuitry 68 (including the AI/ML unit 32), processor 70, radio interface 62 and/or communication interface 60. Network node 16 such as via processing circuitry 68 and/or processor 70 and/or radio interface 62 and/or communication interface 60 is configured to implement a federated learning process that includes multiple-input artificial intelligence/machine learning, AI/ML, the multiple inputs including transmission data information. (Block SI 34). The federated learning process includes WD latency performance analytics configured to produce multiple outputs, the multiple outputs including latency statistics (Block SI 36). In some embodiments, the transmission data information includes at least one of a round trip transmitted data volume, start and end times for data transmission and filter information. In some embodiments, the latency statistics include uplink and downlink latency statistics for target transmission. In some embodiments, the latency statistics include round tip latency statistics for target transmission. In some embodiments, the latency statistics include maximum latency for target transmission.

FIG. 9 is a flowchart of an example process in a core network node 36 configured to include a network data analytics function (NWDAF 34) configured according to principles disclosed herein. One or more blocks described herein may be performed by one or more elements of core network node 36 such as by one or more of processing circuitry 94 (including the NWDAF 34) and radio interface 96. Core network node 36 such as via processing circuitry 94 and the radio interface 96 is configured to collecting wireless device, WD 22, latency performance inputs from a network function, NF 32, the latency performance inputs including a plurality of transmitted data volume values and transmission time values (Block SI 38). The method includes determining a set of latency performance analytics to assist a federated learning process, the set of latency performance analytics being based at least in part on the latency performance inputs and including at least one of latency predictions and latency statistics related to uplink and downlink transmission latency between the WD 22 and an application function, AF (Block S140). The method also includes transmitting the set of latency performance analytics to the NF 32 (Block SI 42).

In some embodiments, the at least one of latency predictions and latency statistics include a time delay for completing a transmission of a target volume of data from a WD 22 to the NF 32 or from the NF 32 to the WD 22. In some embodiments, the at least one of latency predictions and latency statistics include an estimated number of retransmissions for completing the transmission of the target volume of data. In some embodiments, the at least one of latency predictions and latency statistics include at least one of an uplink packet delay, a downlink patent delay and a round trip packet delay. In some embodiments, the at least one of latency predictions and latency statistics include values that are averaged over an analytics target period. In some embodiments, the at least one of latency predictions and latency statistics include a validity period for the latency performance analytics. In some embodiments, the at least one of latency predictions and latency statistics include a maximum packet delay observed for communicating with the NF 32. In some embodiments, the at least one of latency predictions and latency statistics include a spatial validity parameter indicative of an area over which the latency performance analytics apply. In some embodiments, the at least one of latency predictions and latency statistics are grouped into latency classes according to latency performance ranges. In some embodiments, the at least one of latency predictions and latency statistics include an indication of a percentage of WDs 22 in each latency class. In some embodiments, the NF is implemented as the application function.

FIG. 10 is a flowchart of an example process in a network node 16 configured to include a network function (NF 32) configured according to principles disclosed herein. One or more blocks described herein may be performed by one or more elements of network node 16 such as by one or more of processing circuitry 68 (including the NWDAF 34), processor 70, radio interface 62 and/or communication interface 60. Network node 16 such as via processing circuitry 68 and/or processor 70 and/or radio interface 62 and/or communication interface 60 is configured to transmit to the NWDAF 34, latency performance inputs, the latency performance inputs including a plurality of transmitted data volume values and transmission time values (Block S144). The method includes receiving a set of latency performance analytics to assist a federated learning process, the set of latency performance analytics being based at least in part on the latency performance inputs and including at least one of latency predictions and latency statistics related to uplink and downlink transmission latency between the WD 22 and the NF 32 (Block S146). The method also includes hosting artificial intelligence/machine learning, AI/ML, -based services based at least in part on the federated learning process (Block S148).

According to this aspect, in some embodiments, the at least one of latency predictions and latency statistics include a time delay for completing a transmission of a target volume of data from a WD 22 to the NF 32 or from the NF 32 to the WD 22. In some embodiments, the at least one of latency predictions and latency statistics include an estimated number of retransmissions for completing the transmission of the target volume of data. In some embodiments, the at least one of latency predictions and latency statistics include at least one of an uplink packet delay, a downlink patent delay and a round trip packet delay. In some embodiments, the at least one of latency predictions and latency statistics include values that are averaged over an analytics target period. In some embodiments, the at least one of latency predictions and latency statistics include a validity period for the latency performance analytics. In some embodiments, the at least one of latency predictions and latency statistics include a maximum packet delay observed for communicating with the NF 32. In some embodiments, the at least one of latency predictions and latency statistics include a spatial validity parameter indicative of an area over which the latency performance analytics apply. In some embodiments, the at least one of latency predictions and latency statistics are grouped into latency classes according to latency performance ranges. In some embodiments, the at least one of latency predictions and latency statistics include a an indication of a percentage of WDs 22 in each latency class. In some embodiments, the NF is implemented as the application function.

Having described the general process flow of arrangements of the disclosure and having provided examples of hardware and software arrangements for implementing the processes and functions of the disclosure, the sections below provide details and examples of arrangements for enhancement wireless device (WD) latency performance analytics to assist application layer artificial intelligence (Al)/ machine learning (ML) operations. Inputs of WD Latency Performance Analytics for Federated Learning

Referring to Table 1 below, extending the existing inputs from a single value to a (possible) list of values, may include one or more of the following:

■ In order to represent the possibility of different data volumes for different AI/ML traffic in one Federated Learning process, changing the input “Transmitted data volume” from single value to a (possible) list of values, and further extending it into “Round trip Transmitted data volume”; and

■ In order to represent the possibility of different transmission time for the data volumes of different AI/ML traffics in one Federated Learning process, changing the input “Transmission time” from single value to a (possible) list of values.

Some embodiments may include adding new inputs for the WD Latency Performance Analytics. This may include one or more of the following:

■ If the round trip transmitted data volume is provided, the “Time relevant to the round trip” (e.g., time interval for the total time of the round trip for each data volume) may also be provided by AF;

■ In order to get the information about the start time and the end time for the transmission of data with the transmitted data volume(s) for the AI/ML traffics of a Federated Learning process, new input “Timestamps” may be added to identify the start time and the end time for the transmission of data with the transmitted data volume. This new input may be used for the analytics for a specific time interval, e.g., tomorrow 10:00- 10:30, etc.;

In order to get more detailed information about the AI/ML traffic to generate statistics and predictions on the latency performance at a specific time interval (e.g., use the service flow for the AI/ML traffic between a WD 22 and AF at 10:00-10:30), new inputs, “IP filter information”, “Locations of Application”, “Application Server Instance address” may be implemented.

Table 1: Service Data from 5GC NFs for WD latency performance analytics

Outputs of WD Latency Performance Analytics for Federated Learning

Referring to Tables 2 and 3 below, in order to get the analytic results of the UL/DL/round-trip delay for the WD 22 communicating with the application for complete transmission of a target data volume (e.g., NWDAF 34 may estimate the number of transmissions for a data volume), there may be one single transmission for the data volume, or multiple re-transmissions for the data volume to achieve QoS requirements. New outputs may be added for the WD Latency Performance Analytics, which may include one or more of the following: ■ Estimated number of transmissions (statistics/predictions);

■ Statistics/Predictions on UL latency for target transmission;

■ Statistics/Predictions on DL latency for target transmission;

■ Statistics/Predictions on Round trip latency for target transmission; and/or

■ Statistics/Predictions on Maximum latency for target transmission.

Table 2: WD Latency performance statistics

Table 3: WD latency performance predictions

Some embodiments may include one or more of the following:

Embodiment Al. A network node configured to communicate with a wireless device (WD), the network node configured to, and/or comprising a radio interface and/or comprising processing circuitry configured to: implement a federated learning process that includes multiple-input artificial intelligence/machine learning, AI/ML, the multiple inputs including transmission data information; and the federated learning process including WD latency performance analytics configured to produce multiple outputs, the multiple outputs including latency statistics. Embodiment A2. The network node of Embodiment Al, wherein the transmission data information includes at least one of a round trip transmitted data volume, start and end times for data transmission and filter information.

Embodiment A3. The network node of any of Embodiments Al and A2, wherein the latency statistics include uplink and downlink latency statistics for target transmission.

Embodiment A4. The network node of any of Embodiments Al -A3, wherein the latency statistics include round tip latency statistics for target transmission.

Embodiment A5. The network node of any of Embodiments A1-A4, wherein the latency statistics include maximum latency for target transmission.

Embodiment Bl. A method implemented in a network node configured to communicate with a wireless device, WD„ the method comprising: implementing a federated learning process that includes multiple-input artificial intelligence/machine learning, AI/ML, the multiple inputs including transmission data information; and the federated learning process including WD latency performance analytics configured to produce multiple outputs, the multiple outputs including latency statistics.

Embodiment B2. The method of Embodiment B 1 , wherein the transmission data information includes at least one of a round trip transmitted data volume, start and end times for data transmission and filter information.

Embodiment B3. The method of any of Embodiments Bl and B2, wherein the latency statistics include uplink and downlink latency statistics for target transmission.

Embodiment B4. The method of any of Embodiments B1-B3, wherein the latency statistics include round tip latency statistics for target transmission.

Embodiment B5. The method of any of Embodiments B1-B4, wherein the latency statistics include maximum latency for target transmission.

As will be appreciated by one of skill in the art, the concepts described herein may be embodied as a method, data processing system, computer program product and/or computer storage media storing an executable computer program. Accordingly, the concepts described herein may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects all generally referred to herein as a “circuit” or “module.” Any process, step, action and/or functionality described herein may be performed by, and/or associated to, a corresponding module, which may be implemented in software and/or firmware and/or hardware. Furthermore, the disclosure may take the form of a computer program product on a tangible computer usable storage medium having computer program code embodied in the medium that may be executed by a computer. Any suitable tangible computer readable medium may be utilized including hard disks, CD-ROMs, electonic storage devices, optical storage devices, or magnetic storage devices.

Some embodiments are described herein with reference to flowchart illustrations and/or block diagrams of methods, systems and computer program products. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, may be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer (to thereby create a special purpose computer), special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer readable memory or storage medium that may direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture including instruction means which implement the function/act specified in the flowchart and/or block diagram block or blocks.

The computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.

It is to be understood that the functions/acts noted in the blocks may occur out of the order noted in the operational illustrations. For example, two blocks shown in succession may in fact be executed substantially concurrently or the blocks may sometimes be executed in the reverse order, depending upon the functionality/acts involved. Although some of the diagrams include arrows on communication paths to show a primary direction of communication, it is to be understood that communication may occur in the opposite direction to the depicted arrows.

Computer program code for carrying out operations of the concepts described herein may be written in an object oriented programming language such as Python, Java® or C++. However, the computer program code for carrying out operations of the disclosure may also be written in conventional procedural programming languages, such as the "C" programming language. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer. In the latter scenario, the remote computer may be connected to the user's computer through a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider).

Many different embodiments have been disclosed herein, in connection with the above description and the drawings. It will be understood that it would be unduly repetitious and obfuscating to literally describe and illustrate every combination and subcombination of these embodiments. Accordingly, all embodiments may be combined in any way and/or combination, and the present specification, including the drawings, shall be construed to constitute a complete written description of all combinations and subcombinations of the embodiments described herein, and of the manner and process of making and using them, and shall support claims to any such combination or subcombination.

Abbreviations that may be used in the preceding description include:

AF Application Function

AMF Access and Mobility Management Function

Al Artificial Intelligence

FL Federated Learning

FQDN Fully Qualified Domain Name

GMLC Gateway Mobile Location Centre

ML Machine Learning

NEF Network Exposure Function

NF Network Function

NWDAF Network Data Analytics Function

GAM Operations, Administration and Maintenance

SMF Session Management Function UPF User Plane Function

5GC 5G Core Network

It will be appreciated by persons skilled in the art that the embodiments described herein are not limited to what has been particularly shown and described herein above. In addition, unless mention was made above to the contrary, it should be noted that all of the accompanying drawings are not to scale. A variety of modifications and variations are possible in light of the above teachings without departing from the scope of the following claims.

Claims

What is claimed is:

1. A method in a core network node (36) configured to include a network data analytics function, NWDAF (34), the method comprising: collecting (S138) wireless device, WD (22), latency performance inputs from a network function, NF (32), the latency performance inputs including a plurality of transmitted data volume values and transmission time values; determining (SI 40) a set of latency performance analytics to assist a federated learning process, the set of latency performance analytics being based at least in part on the latency performance inputs and including at least one of latency predictions and latency statistics related to uplink and downlink transmission latency between the WD (22) and an application function, AF; and transmitting (SI 42) the set of latency performance analytics to the NF (32).

2. The method of Claim 1, wherein the at least one of latency predictions and latency statistics include a time delay for completing a transmission of a target volume of data from a WD (22) to the NF (32) or from the NF (32) to the WD (22).

3. The method of Claim 2, wherein the at least one of latency predictions and latency statistics include an estimated number of retransmissions for completing the transmission of the target volume of data.

4. The method of any of Claims 1 and 2, wherein the at least one of latency predictions and latency statistics include at least one of an uplink packet delay, a downlink patent delay and a round trip packet delay.

5. The method of any of Claims 1-4, wherein the at least one of latency predictions and latency statistics include values that are averaged over an analytics target period.

6. The method of any of Claims 1-5, wherein the at least one of latency predictions and latency statistics include a validity period for the latency performance analytics.

7. The method of any of Claims 1-6, wherein the at least one of latency predictions and latency statistics include a maximum packet delay observed for communicating with the NF (32).

8. The method of any of Claims 1-7, wherein the at least one of latency predictions and latency statistics include a spatial validity parameter indicative of an area over which the latency performance analytics apply.

9. The method of any of Claims 1-8, wherein the at least one of latency predictions and latency statistics are grouped into latency classes according to latency performance ranges.

10. The method of Claim 9, wherein the at least one of latency predictions and latency statistics include an indication of a percentage of WDs (22) in each latency class.

11. The method of Claims 1-10, wherein the NF is implemented as the application function.

12. A core network node (36) configured to include a network data analytics function, NWDAF (34), the core network node (36) configured to: collect wireless device, WD (22), latency performance inputs from a network function, NF (32), the latency performance inputs including a plurality of transmitted data volume values and transmission time values; determine a set of latency performance analytics to assist a federated learning process, the set of latency performance analytics being based at least in part on the latency performance inputs and including at least one of latency predictions and latency statistics related to uplink and downlink transmission latency between the WD (22) and an application function, AF; and transmit the set of latency performance analytics to the NF (32).

13. The core network node (36) of Claim 12, wherein the at least one of latency predictions and latency statistics include a time delay for completing a transmission of a target volume of data from a WD (22) to the NF (32) or from the NF (32) to the WD (22).

14. The core network node (36) of Claim 13, wherein the at least one of latency predictions and latency statistics include an estimated number of retransmissions for completing the transmission of the target volume of data.

15. The core network node (36) of any of Claims 12 and 13, wherein the at least one of latency predictions and latency statistics include at least one of an uplink packet delay, a downlink patent delay and a round trip packet delay.

16. The core network node (36) of any of Claims 12-15, wherein the at least one of latency predictions and latency statistics include values that are averaged over an analytics target period.

17. The core network node (36) of any of Claims 12-16, wherein the at least one of latency predictions and latency statistics include a validity period for the latency performance analytics.

18. The core network node (36) of any of Claims 12-17, wherein the at least one of latency predictions and latency statistics include a maximum packet delay observed for communicating with the NF (32).

19. The core network node (36) of any of Claims 12-18, wherein the at least one of latency predictions and latency statistics include a spatial validity parameter indicative of an area over which the latency performance analytics apply.

20. The core network node (36) of any of Claims 12-19, wherein the at least one of latency predictions and latency statistics are grouped into latency classes according to latency performance ranges.

21. The core network node (36) of Claim 20, wherein the at least one of latency predictions and latency statistics include a an indication of a percentage of WDs (22) in each latency class.

22. The core network node of Claims 12-21, wherein the NF is implemented as the application function.

23. A method in a network node configured to include a network function, NF (32), and configured to communicate with a network data analytics function, NWDAF (34), the method comprising: transmitting (S144) to the NWDAF (34), latency performance inputs, the latency performance inputs including a plurality of transmitted data volume values and transmission time values; receiving (SI 46) a set of latency performance analytics to assist a federated learning process, the set of latency performance analytics being based at least in part on the latency performance inputs and including at least one of latency predictions and latency statistics related to uplink and downlink transmission latency between a wireless device, WD (22), and an application function, AF; and hosting (S148) artificial intelligence/machine learning, AFML, -based services based at least in part on the federated learning process.

24. The method of Claim 23, wherein the at least one of latency predictions and latency statistics include a time delay for completing a transmission of a target volume of data from a WD (22) to the NF (32) or from the NF (32) to the WD (22).

25. The method of Claim 24, wherein the at least one of latency predictions and latency statistics include an estimated number of retransmissions for completing the transmission of the target volume of data.

26. The method of any of Claims 23-25, wherein the at least one of latency predictions and latency statistics include at least one of an uplink packet delay, a downlink patent delay and a round trip packet delay.

27. The method of any of Claims 23-26, wherein the at least one of latency predictions and latency statistics include values that are averaged over an analytics target period.

28. The method of any of Claims 23-27, wherein the at least one of latency predictions and latency statistics include a validity period for the latency performance analytics.

29. The method of any of Claims 23-28, wherein the at least one of latency predictions and latency statistics include a maximum packet delay observed for communicating with the NF (32).

30. The method of any of Claims 23-29, wherein the at least one of latency predictions and latency statistics include a spatial validity parameter indicative of an area over which the latency performance analytics apply.

31. The method of any of Claims 23-30, wherein the at least one of latency predictions and latency statistics are grouped into latency classes according to latency performance ranges.

32. The method of Claim 31, wherein the at least one of latency predictions and latency statistics include a an indication of a percentage of WDs (22) in each latency class.

33. The method of Claims 23-32, wherein the NF is implemented as the application function.

34. A network node (16) configured to include a network function, NF (32), and configured to communicate with a network data analytics function, NWDAF (34), the network node (16) configured to: transmit to the NWDAF (34), latency performance inputs, the latency performance inputs including a plurality of transmitted data volume values and transmission time values; receive a set of latency performance analytics to assist a federated learning process, the set of latency performance analytics being based at least in part on the latency performance inputs and including at least one of latency predictions and latency statistics related to uplink and downlink transmission latency between a wireless device, WD (22), and an application function, AF; and host artificial intelligence/machine learning, AI/ML, -based services based at least in part on the federated learning process.

35. The network node (16) of Claim 34, wherein the at least one of latency predictions and latency statistics include a time delay for completing a transmission of a target volume of data from a WD (22) to the NF (32) or from the NF (32) to the WD (22).

36. The network node (16) of Claim 35, wherein the at least one of latency predictions and latency statistics include an estimated number of retransmissions for completing the transmission of the target volume of data.

37. The network node (16) of any of Claims 35-36, wherein the at least one of latency predictions and latency statistics include at least one of an uplink packet delay, a downlink patent delay and a round trip packet delay.

38. The network node (16) of any of Claims 34-37, wherein the at least one of latency predictions and latency statistics include values that are averaged over an analytics target period.

39. The network node (16) of any of Claims 34-38, wherein the at least one of latency predictions and latency statistics include a validity period for the latency performance analytics.

40. The network node (16) of any of Claims 34-39, wherein the at least one of latency predictions and latency statistics include a maximum packet delay observed for communicating with the NF (32).

41. The network node (16) of any of Claims 34-40, wherein the at least one of latency predictions and latency statistics include a spatial validity parameter indicative of an area over which the latency performance analytics apply.

42. The network node (16) of any of Claims 34-41, wherein the at least one of latency predictions and latency statistics are grouped into latency classes according to latency performance ranges.

43. The network node (16) of Claim 42, wherein the at least one of latency predictions and latency statistics include a an indication of a percentage of WDs (22) in each latency class.

44. Then network node (16) of Claims 34-43, wherein the NF is implemented as the application function.