Analytics services in a wireless communication system

Abstract

Various aspects of the present disclosure relate to a first network entity for wireless communication. The first network entity may be configured to, capable of, or operable to receive a first request to support an analytics service associated with a UE, is served by a first wireless communication network; determine a second network entity to support the analytics service whilst the UE is being served by the second wireless communication network; and transmit, to the second network entity, a second request to support the analytics service.

Description

ANALYTICS SERVICES IN A WIRELESS COMMUNICATION SYSTEMTECHNICAL FIELD

[0001] The present disclosure relates generally to wireless communication, including analytics services.BACKGROUND

[0002] A wireless communications system may include one or multiple network communication devices, which may be otherwise knowns as network equipment (NE) supporting wireless communications for one or multiple user communication devices, which may be otherwise known as user equipment (UE), or other suitable terminology. The wireless communications system may support wireless communications with one or multiple user communication devices by utilizing resources of the wireless communication system (e.g., time resources (e.g., symbols, slots, subframes, frames, or the like) or frequency resources (e.g., subcarriers, carriers, or the like). Additionally, the wireless communications system may support wireless communications across various radio access technologies including third generation (3G) radio access technology, fourth generation (4G) radio access technology, fifth generation (5G) radio access technology, among other suitable radio access technologies beyond 5G (e.g., sixth generation (6G)).SUMMARY

[0003] An article “a” before an element is unrestricted and understood to refer to “at least one” of those elements or “one or more” of those elements. The terms “a,” “at least one,” “one or more,” and “at least one of one or more” may be interchangeable. As used herein, including in the claims, “or” as used in a list of items (e.g., a list of items prefaced by a phrase such as “at least one of’ or “one or more of’ or “one or both of’) indicates an inclusive list such that, for example, a list of at least one of A, B, or C means A or B or C or AB or AC or BC or ABC (i.e., A and B and C). Also, as used herein, the phrase “based on” shall not be construed as a reference to a closed set of conditions. For example, an example step that is described as “based on condition A” may be based on both a condition A and a condition B without departing from the scope of the present disclosure. In other words, asDocket No. SMM920250066-GR-NPused herein, the phrase “based on” shall be construed in the same manner as the phrase “based at least in part on. Further, as used herein, including in the claims, a “set” may include one or more elements.

[0004] The following abbreviations are herewith defined, at least some of which are referred to within the following description: 3 GPP - Third Generation Partnership Project; 5GC - 5G Core Network; 5GS - 5G System; ADAEC - Application Data Analytics Enablement Client; AD AES - Application Data Analytics Enablement Server; A-ADRF - Analytics Data Repository Function; AF - Application Function; Al - Artificial Intelligence; AIMLE - AI / ML Enablement; AIMLEC - AI / ML Enablement Client; AIMLES - AI / ML Enablement Server; API - Application Programming Interface; ASP - Application Service Provider; CSP - Cloud Service Provider; ECSP - Edge Computing Service Provider; ECS - Edge Configuration Server; ECS-ER - Edge Configuration Server Edge Relay; EDN - Edge Data Network (DN); EEC - Edge Enabling Client; EEL - Edge Enabler Layer; eV2X - Evolved Vehicle-to-everything; FL - Federated Learning; H-ECS - Hardware-based Edge Computing Server; HFL - Horizontal Federated Learning; HPLMN- Home PLMN; HR - Home Routed; LBO - Local Breakout; LMC - Local Management Client; LMF - Location Management Function; LMS - Location Management Service; ML- Machine Learning; MNO - Mobile Network Operator; NF - Network Function; NEF - Network Exposure Function; NPN - Non-Public Network; NSCE - Network Slice Capability Enablement; NWDAF - Network Data Analytics Function; 0AM - Operations, Administration and Maintenance; PaaS - Platform as a Service; PLMN - Public Land Mobile Network; S-ADAES - Source Application Data Analytics Enablement Server; S- AIMLES - Source AI / ML Enablement Server; SEAL - Service Enabler Architecture Layer; SNPN - Standalone NPN; T-ADAES - Target Application Data Analytics Enablement Server; T-AIMLES - Target AI / ML Enablement Server; TL - Transfer Learning; UDM - Unified Data Management; V2X - Vehicle-to-everything; VAL - Vertical Application Layer; VAS - Vertical Application layer Server; V-ECS - Virtualized Edge Computing Server; V-EES - Virtualized Edge Enabling Server; VFL - Vertical Federated Learning; VPLMN - Visiting PLMN.Docket No. SMM920250066-GR-NP

[0005] A first network entity for wireless communication is described. The first network entity may be configured to, capable of, or operable to perform one or more operations as described herein. For example, the first network entity may include at least one memory, and at least one processor coupled with the at least one memory and configured to cause the first network entity to: receive a first request to support an analytics service associated with a UE served by a first wireless communication network; determine a second network entity to support the analytics service whilst the UE is being served by the second wireless communication network; and transmit, to the second network entity, a second request to support the analytics service.

[0006] A method performed or performable by the first network entity is described herein. The method may comprise receiving a first request to support an analytics service associated with a UE served by a first wireless communication network; determining a second network entity to support the analytics service whilst the UE is being served by the second wireless communication network; and transmitting, to the second network entity, a second request to support the analytics service.

[0007] A processor for wireless communication is described. The processor may be configured to, capable of, or operable to perform one or more operations as described herein. For example, the processor may comprise at least one controller coupled with at least one memory and configured to cause the processor to: receive a first request to support an analytics service associated with a UE served by a first wireless communication network; determine a second network entity to support the analytics service whilst the UE is being served by the second wireless communication network; and transmit, to the second network entity, a second request to support the analytics service.

[0008] A UE for wireless communication is described. The UE may be configured to, capable of, or operable to perform one or more operations as described herein. For example, the UE may include at least one memory, and at least one processor coupled with the at least one memory and configured to cause the UE to: determine to support a first network entity with an analytics service, wherein the UE is served by a first wireless communication network; determine that the UE is to be served by a second wireless communication network; and determine a second network entity for supporting theDocket No. SMM920250066-GR-NPanalytics service whilst the UE is being served by the second wireless communication network.

[0009] A method performed or performable by the UE is described herein. The method may comprise determining to support a first network entity with an analytics service, wherein the UE is served by a first wireless communication network; determining that the UE is to be served by a second wireless communication network; and determining a second network entity for supporting the analytics service whilst the UE is being served by the second wireless communication network.

[0010] A processor for wireless communication is described. The processor may be configured to, capable of, or operable to perform one or more operations as described herein. For example, the processor may comprise at least one controller coupled with at least one memory and configured to cause the processor to: determine to support a first network entity with an analytics service, wherein the UE is served by a first wireless communication network; determine that the UE is to be served by a second wireless communication network; and determine a second network entity for supporting the analytics service whilst the UE is being served by the second wireless communication network.BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 illustrates an example of a wireless communications system in accordance with aspects of the present disclosure.

[0012] Figure 2 illustrates an example of an on-network AIMLE functional model in accordance with aspects of the present disclosure.

[0013] Figure 3 illustrates an example of ML model lifecycle enablement in accordance with aspects of the present disclosure.

[0014] Figure 4 illustrates an example of relationships involved in an edge computing service in accordance with aspects of the present disclosure.Docket No. SMM920250066-GR-NP

[0015] Figure 5 illustrates an example of a UE roaming between wireless communication networks with different AIMLE servers in accordance with aspects of the present disclosure.

[0016] Figure 6 illustrates an example architecture for supporting AIML-enabled ADAE analytics in accordance with aspects of the present disclosure.

[0017] Figure 7 illustrates an example of a UE roaming between wireless communication networks with different SEAL servers in accordance with aspects of the present disclosure.

[0018] Figure 8 illustrates an example of a process flow for providing an analytics service in accordance with aspects of the present disclosure.

[0019] Figure 9 illustrates an example of a process flow for providing an analytics service in accordance with aspects of the present disclosure.

[0020] Figure 10 illustrates an example of a UE 1000 in accordance with aspects of the present disclosure.

[0021] Figure 11 illustrates an example of a processor 1100 in accordance with aspects of the present disclosure.

[0022] Figure 12 illustrates an example of an NE 1200 in accordance with aspects of the present disclosure.

[0023] Figure 13 illustrates a flowchart of a method 1300 performed by a NE in accordance with aspects of the present disclosure.DETAILED DESCRIPTION

[0024] A wireless communication system (which may be referred to as a wireless communication network herein), may comprise one or more UE and NE. In a roaming scenario, a UE may move from coverage of a first PLMN to coverage of a second PLMN. Whilst roaming, the UE, the first PLMN and the second PLMN may be configured to maintain connectivity between the UE and the NE.Docket No. SMM920250066-GR-NP

[0025] The wireless communication system may be configured to support an analytics service (e.g., a ML-enabled analytics service). Such scenarios may involve one or more AD AES and / or AIMLES which support the wireless communication system (and the UE) with an analytics service. However, roaming from the first PLMN to the second PLMN whilst supporting the analytics service tends to affect the performance (e.g., continuity) of the analytics service.

[0026] Examples described herein generally relate to a first AD AES and / or AIMLES arranged to support the analytics service whilst the UE is roaming. In some examples described herein, the first AD AES and / or AIMLES is arranged to determine a second AD AES and / or AIMLES for supporting the analytics service whilst the UE is being served by the second PLMN. This tends to reduce the impact of UE roaming on the performance of the analytics service.

[0027] Aspects of the present disclosure are described in the context of a wireless communications system.

[0028] Figure 1 illustrates an example of a wireless communications system 100 in accordance with aspects of the present disclosure. The wireless communications system 100 may include one or more NE 102, one or more UE 104, and a core network (CN) 106. The wireless communications system 100 may support various radio access technologies. In some implementations, the wireless communications system 100 may be a 4G network, such as an LTE network or an LTE- Advanced (LTE-A) network. In some other implementations, the wireless communications system 100 may be a NR network, such as a 5G network, a 5G- Advanced (5G-A) network, or a 5G ultrawideband (5G-UWB) network. In other implementations, the wireless communications system 100 may be a combination of a 4G network and a 5G network, or other suitable radio access technology including Institute of Electrical and Electronics Engineers (IEEE) 802.11 (Wi-Fi), IEEE 802.16 (WiMAX), IEEE 802.20. The wireless communications system 100 may support radio access technologies beyond 5G, for example, 6G. Additionally, the wireless communications system 100 may support technologies, such as time division multiple access (TDMA), frequency division multiple access (FDMA), or code division multiple access (CDMA), etc.Docket No. SMM920250066-GR-NP

[0029] The one or more NE 102 may be dispersed throughout a geographic region to form the wireless communications system 100. One or more of the NE 102 described herein may be or include or may be referred to as a network node, a base station, a network element, a network function, a network entity, a radio access network (RAN), a NodeB, an eNodeB (eNB), a next-generation NodeB (gNB), or other suitable terminology. An NE 102 and a UE 104 may communicate via a communication link, which may be a wireless or wired connection. For example, an NE 102 and a UE 104 may perform wireless communication (e.g., receive signalling, transmit signalling) over a Uu interface.

[0030] An NE 102 may provide a geographic coverage area for which the NE 102 may support services for one or more UEs 104 within the geographic coverage area. For example, an NE 102 and a UE 104 may support wireless communication of signals related to services (e.g., voice, video, packet data, messaging, broadcast, etc.) according to one or multiple radio access technologies. In some implementations, an NE 102 may be moveable, for example, a satellite associated with a non-terrestrial network (NTN). In some implementations, different geographic coverage areas associated with the same or different radio access technologies may overlap, but the different geographic coverage areas may be associated with different NE 102.

[0031] The one or more UE 104 may be dispersed throughout a geographic region of the wireless communications system 100. A UE 104 may include or may be referred to as a remote unit, a mobile device, a wireless device, a remote device, a subscriber device, a transmitter device, a receiver device, or some other suitable terminology. In some implementations, the UE 104 may be referred to as a unit, a station, a terminal, or a client, among other examples. Additionally, or alternatively, the UE 104 may be referred to as an Internet-of-Things (loT) device, an Internet-of-Everything (loE) device, or machine-type communication (MTC) device, among other examples.

[0032] A UE 104 may be able to support wireless communication directly with otherUEs 104 over a communication link. For example, a UE 104 may support wireless communication directly with another UE 104 over a device-to-device (D2D) communication link. In some implementations, such as vehicle-to-vehicle (V2V) deployments, V2X deployments, or cellular-V2X deployments, the communication linkDocket No. SMM920250066-GR-NPmay be referred to as a sidelink. For example, a UE 104 may support wireless communication directly with another UE 104 over a PC5 interface.

[0033] An NE 102 may support communications with the CN 106, or with another NE 102, or both. For example, an NE 102 may interface with other NE 102 or the CN 106 through one or more backhaul links (e.g., SI, N2, N2, or network interface). In some implementations, the NE 102 may communicate with each other directly. In some other implementations, the NE 102 may communicate with each other or indirectly (e.g., via the CN 106. In some implementations, one or more NE 102 may include subcomponents, such as an access network entity, which may be an example of an access node controller (ANC). An ANC may communicate with the one or more UEs 104 through one or more other access network transmission entities, which may be referred to as a radio heads, smart radio heads, or transmission-reception points (TRPs).

[0034] The CN 106 may support user authentication, access authorization, tracking, connectivity, and other access, routing, or mobility functions. The CN 106 may be an evolved packet core (EPC), or a 5GC, which may include a control plane entity that manages access and mobility (e.g., a mobility management entity (MME), an access and mobility management functions (AMF)) and a user plane entity that routes packets or interconnects to external networks (e.g., a serving gateway (S-GW), a Packet Data Network (PDN) gateway (P-GW), or a user plane function (UPF)). In some implementations, the control plane entity may manage non-access stratum (NAS) functions, such as mobility, authentication, and bearer management (e.g., data bearers, signal bearers, etc.) for the one or more UEs 104 served by the one or more NE 102 associated with the CN 106.

[0035] The CN 106 may communicate with a packet data network over one or more backhaul links (e.g., via an SI, N2, N2, or another network interface). The packet data network may include an application server. In some implementations, one or more UEs 104 may communicate with the application server. A UE 104 may establish a session (e.g., a protocol data unit (PDU) session, or the like) with the CN 106 via an NE 102. The CN 106 may route traffic (e.g., control information, data, and the like) between the UE 104 and the application server using the established session (e.g., the established PDU session). TheDocket No. SMM920250066-GR-NPPDU session may be an example of a logical connection between the UE 104 and the CN 106 (e.g., one or more network functions of the CN 106).

[0036] In the wireless communications system 100, the NEs 102 and the UEs 104 may use resources of the wireless communications system 100 (e.g., time resources (e.g., symbols, slots, subframes, frames, or the like) or frequency resources (e.g., subcarriers, carriers)) to perform various operations (e.g., wireless communications). In some implementations, the NEs 102 and the UEs 104 may support different resource structures. For example, the NEs 102 and the UEs 104 may support different frame structures. In some implementations, such as in 4G, the NEs 102 and the UEs 104 may support a single frame structure. In some other implementations, such as in 5 G and among other suitable radio access technologies, the NEs 102 and the UEs 104 may support various frame structures (i.e., multiple frame structures). The NEs 102 and the UEs 104 may support various frame structures based on one or more numerologies.

[0037] One or more numerologies may be supported in the wireless communications system 100, and a numerology may include a subcarrier spacing and a cyclic prefix. A first numerology (e.g., / r=0) may be associated with a first subcarrier spacing (e.g., 15 kHz) and a normal cyclic prefix. In some implementations, the first numerology (e.g., / r=0) associated with the first subcarrier spacing (e.g., 15 kHz) may utilize one slot per subframe. A second numerology (e.g., / r=l) may be associated with a second subcarrier spacing (e.g., 30 kHz) and a normal cyclic prefix. A third numerology (e.g., / r=2) may be associated with a third subcarrier spacing (e.g., 60 kHz) and a normal cyclic prefix or an extended cyclic prefix. A fourth numerology (e.g., / r=3) may be associated with a fourth subcarrier spacing (e.g., 120 kHz) and a normal cyclic prefix. A fifth numerology (e.g., / r=4) may be associated with a fifth subcarrier spacing (e.g., 240 kHz) and a normal cyclic prefix.

[0038] A time interval of a resource (e.g., a communication resource) may be organized according to frames (also referred to as radio frames). Each frame may have a duration, for example, a 10 millisecond (ms) duration. In some implementations, each frame may include multiple subframes. For example, each frame may include 10 subframes, and each subframe may have a duration, for example, a 1 ms duration. In some implementations,Docket No. SMM920250066-GR-NPeach frame may have the same duration. In some implementations, each subframe of a frame may have the same duration.

[0039] Additionally or alternatively, a time interval of a resource (e.g., a communication resource) may be organized according to slots. For example, a subframe may include a number (e.g., quantity) of slots. The number of slots in each subframe may also depend on the one or more numerologies supported in the wireless communications system 100. For instance, the first, second, third, fourth, and fifth numerologies (i.e., / r=0, jU=l , / r=2, jU=3, / r=4) associated with respective subcarrier spacings of 15 kHz, 30 kHz, 60 kHz, 120 kHz, and 240 kHz may utilize a single slot per subframe, two slots per subframe, four slots per subframe, eight slots per subframe, and 16 slots per subframe, respectively. Each slot may include a number (e.g., quantity) of symbols (e.g., OFDM symbols). In some implementations, the number (e.g., quantity) of slots for a subframe may depend on a numerology. For a normal cyclic prefix, a slot may include 14 symbols. For an extended cyclic prefix (e.g., applicable for 60 kHz subcarrier spacing), a slot may include 12 symbols. The relationship between the number of symbols per slot, the number of slots per subframe, and the number of slots per frame for a normal cyclic prefix and an extended cyclic prefix may depend on a numerology. It should be understood that reference to a first numerology (e.g., / r=0) associated with a first subcarrier spacing (e.g., 15 kHz) may be used interchangeably between subframes and slots.

[0040] In the wireless communications system 100, an electromagnetic (EM) spectrum may be split, based on frequency or wavelength, into various classes, frequency bands, frequency channels, etc. By way of example, the wireless communications system 100 may support one or multiple operating frequency bands, such as frequency range designations FR1 (410 MHz - 7.125 GHz), FR2 (24.25 GHz - 52.6 GHz), FR3 (7.125 GHz - 24.25 GHz), FR4 (52.6 GHz - 114.25 GHz), FR4a or FR4-1 (52.6 GHz - 71 GHz), and FR5 (114.25 GHz - 300 GHz). In some implementations, the NEs 102 and the UEs 104 may perform wireless communications over one or more of the operating frequency bands. In some implementations, FR1 may be used by the NEs 102 and the UEs 104, among other equipment or devices for cellular communications traffic (e.g., control information, data).Docket No. SMM920250066-GR-NPIn some implementations, FR2 may be used by the NEs 102 and the UEs 104, among other equipment or devices for short-range, high data rate capabilities.

[0041] FR1 may be associated with one or multiple numerologies (e.g., at least three numerologies). For example, FR1 may be associated with a first numerology (e.g., / r=0), which includes 15 kHz subcarrier spacing; a second numerology (e.g., / r=l), which includes 30 kHz subcarrier spacing; and a third numerology (e.g., / r=2), which includes 60 kHz subcarrier spacing. FR2 may be associated with one or multiple numerologies (e.g., at least 2 numerologies). For example, FR2 may be associated with a third numerology (e.g., / r=2), which includes 60 kHz subcarrier spacing; and a fourth numerology (e.g., / r=3), which includes 120 kHz subcarrier spacing.

[0042] The wireless communication system 100 may comprise multiple PLMN operators. Each of the multiple PLMN operators may operate different NEs 102. In roaming, the UEs 104 may move out of coverage of a first NE 102 operated by a first PLMN operator and into coverage of a second NE 102 operated by a second PLMN operator. If the wireless communication system 100 is configured to support an analytics service, roaming of the UEs 104 tends to impact on the performance of the analytics service. Examples described herein generally relate to a first NE 102 in the first PLMN arranged to determine the second NE 102 in the second PLMN that is suitable for supporting the analytics service whilst the UEs 104 roaming.

[0043] Figure 2 is a diagram 200 illustrating an example of an on-network AIMLE functional model in accordance with aspects of the present disclosure. The diagram 200 comprises a UE 205, a 3GPP Network System 240, a VAL server(s) 216, ML Repository 241 and an AIMLE server 236. The UE 205 comprises a VAL 210 and a SEAL 220. The VAL 210 comprises a VAL Client(s) 212. The SEAL 220 comprises an AIML-C 230 and an AIMLE client 232. The VAL server(s) 216 connects to the VAL Client(s) 212 via a VAL-UU 214 reference point. The AIMLE client 232 connects to the AIMLE server 236 via an AIML-UU 234 reference point. The 3 GPP Network System 240 connects to the AIMLE server 236 via Network interfaces 235. The AIMLE server 236 connects to the VAL server(s) 216 via an AIML-S 231 reference point. The AIMLE server 236 connects toDocket No. SMM920250066-GR-NPthe ML Repository 241 via AIML-R 233 reference point. The AIMLE server 236 comprises an AIML-E 238 reference point.

[0044] Vertical-specific applications and edge applications tend to be the major consumers of 3 GPP-provided data analytics and AI / ML support services. The AIMLE service plays a role on the exposure of AI / ML services from different 3 GPP domains to the vertical / ASP in a unified manner on top of 3GPP core network and 0AM. The AIMLE service also plays a role on defining, at a SEAL layer, value-add support services for assisting AI / ML services provided by the VAL 210, while being complementary to AI / ML support solutions provided in other 3 GPP domains.

[0045] Figure 2 illustrates the on-network functional model of AIML enablement. In the VAL 210, the VAL client 212 communicates with the VAL server 216 over VAL-UU reference point 214. The VAL-UU reference point 214 supports both unicast and multicast delivery modes. The AIML enablement functional entities on the UE 205 and the server are grouped into AIMLE client(s) 232 and AIMLE server(s) 236 respectively.

[0046] The AIMLE server 236 may be a SEAL server which includes of a common set of services for comprehensive enablement of AIML functionality. The AIMLE server 236 may comprise at least one of the following group of capabilities: support for applicationlayer ML model related aspects (e.g., model retrieval, model training, model monitoring, model selection, model update and / or model storage / discovery); assistance in AI / ML task transfer and split AI / ML operations; support HFL / VFL operations (e.g., FL member registration, FL grouping and FL-related events notification, VFL feature alignment, and / or HFL training); and support for AIMLE client registration, discovery, participation and / or selection.

[0047] The AIMLE client 232 functional entity acts as the application client supporting AIMLE services. The ML repository 241 is an entity that serves as: a registry for ML / FL members (e.g., application layer entities participating in an AI / ML operation) and as a repository for application layer ML model related information.

[0048] AIMLE is applicable to ML model lifecycle enablement which provides assistance for use cases where an ASP / VAL layer 210 aims to find and use otherDocket No. SMM920250066-GR-NPapplication entities to perform some ML operations (e.g., ML model inference) and the AIMLE server 236 acts as a mediator to accomplish that.

[0049] Figure 3 is a diagram 300 illustrating an example of ML model lifecycle enablement in accordance with aspects of the present disclosure. The ML model lifecycle enablement diagram 300 comprises a VAL server(s) 316 for ML model operational workflow and an AIMLE 330 for ML model lifecycle enablement. The VAL Server(s) 316 comprises data management, model training, model evaluation, model deployment and model inference. The AIMLE 330 comprises ML model related support which comprises model retrieval, model discovery and model storage. The AIMLE 330 further comprises ML operation related support which comprises VFL / HFL enablement, TL enablement, split AI / ML operation, Data management support and FL member support e.g., grouping, register and events. The AIMLE 330 further comprises AIMLE client support which comprises AIMLE register, discovery, participate and monitoring.

[0050] The ML model lifecycle enablement diagram 300 is an exemplary grouping of AIMLE capabilities with respect to enablement. The AIMLE capabilities are described in Annex C.4 of TS 23.482 VI 9.1.0.

[0051] AIMLE may undertake at least one of: ML model related support capabilities (e.g., model retrieval, discovery and / or storage; for example, as described in procedures in clauses 8.2 and 8.11 of TS 23.482 V19.1.0); ML operation related support capabilities (e.g., VFL / HFL and TL enablement, Split AI / ML Operation support, Data management assistance, AI / ML task transfer, FL assistance in member grouping, registration and / or event notification; for example, as described in procedures in clauses 8.4, 8.6, 8.12, 8.14, 8.15-8.18 of TS 23.482 V19.1.0); and AIMLE client related support capabilities (e.g., AIMLE client registration, discovery, participation, monitoring, selection; for example, as described in procedures in clauses 8.7-8.10, 8.13 of TS 23.482 V19.1.0).

[0052] Figure 4 is a diagram 400 illustrating an example of relationships involved in an edge computing service in accordance with aspects of the present disclosure.

[0053] The diagram 400 comprises an ASP 401 which has an ASP service agreement(s) 411 with an End User 402. The ASP 401 also has an ECSP service agreement(s) 412 withDocket No. SMM920250066-GR-NPECSP A 403 and ECSP B 404. ECSP A 403 and ECSP B 404 have a Federation agreement 415. ECSP A 403 and ECSP B 404 also have an Edge service authorization(s) 413 with the End User 402. ECSP A 403 and ECSP B 404 also have a PLMN operator service agreement(s) 416 with the PLMN operator X (e.g., HPLMN) 406 and PLMN operator Y (e.g., VPLMN) 407. The PLMN operator X 406 and PLMN operator Y 407 have a service agreement 417. The PLMN operator X 406 and PLMN operator Y 407 have a PLMN subscription arrangement 414 with the End User 402.

[0054] There may be different business relationships that exist with respect to federation. In TS 23.558 V19.5.0 Annex B.2, the relationship of edge computing service providers 403 / 404, PLMN operators 406 / 407, ASPs 401 and end users 402 is illustrated in Figure 4, taking federation and roaming into account.

[0055] The end user 402 is the consumer of the applications provided by the ASP 401. The end user 402: may have the ASP service agreement 411 with a single or multiple application service providers. The end user 402 may have a PLMN subscription arrangement 414 with a PLMN operator (e.g., HPLMN 406), and the UE used by the end user may register on the HPLMN 406 and network of its roaming partners; or an SNPN subscription arrangement with a SNPN operator (subscribed SNPN), and the UE used by the end user 402 may register on the subscribed SNPN and a serving SNPN. The end user 402 may have authorization to access edge services of a single or multiple ECSPs (e.g., ECSP A 403 and / or ECSP B 404).

[0056] The ASP 401 consumes the edge services (e.g., infrastructure, platform) provided by the ECSP (e.g., ECSP A 403 and / or ECSP B 404). The ASP 401 may have the ECSP service agreement 412 with a single or multiple ECSPs (e.g., ECSP A 403 and / or ECSP B 404).

[0057] The PLMN operator (e.g., PLMN operator X 406 and / or PLMN operator Y 407) provides connectivity between the end user 402 and the edge services provided by the ECSP (e.g., ECSP A 403 and / or ECSP B 404). The PLMN operator may have the PLMN operator service agreement 416 with a single or multiple ECSPs 403 / 404. The PLMN operator may have a service agreement for roaming including agreements for Edge Computing services, and / or federation with a single or multiple PLMN operators 406 / 407.Docket No. SMM920250066-GR-NP

[0058] The ECSP 403 / 404 provides the edge services. The ECSP 403 / 404 may have the PLMN operator service agreement 416 with a single or multiple PLMN operators 406 / 407 which provide edge computing support. The ECSP 403 / 404 have federation partnership to share edge services with a single or multiple ECSPs 403 / 404. The ECSP 403 / 404 and the PLMN operator 406 / 407 may be part of the same organization.

[0059] Figure 5 is a diagram 500 illustrating an example of a UE roaming between wireless communication networks with different AIMLE servers in accordance with aspects of the present disclosure. The diagram 500 includes UE 505 being served by a first 3GPP Network (e.g., PLMN1, HPLMN) 540. The UE 505 comprises an AIMLE client 532 that connects to a first AIMLE server 536 via the first 3GPP Network 540. The UE 505 roams from the first 3 GPP Network 540 to a second 3 GPP Network (e.g., PLMN2, VPLMN) 542. The AIMLE client 532 then connects to a second AIMLE server 538. The first AIMLE server 536 and second AIMLE server 538 are connected to VAL servers 516. The first AIMLE server 536 connects to a first ML repository 541. The second AIMLE server 538 connects to a second ML repository 543.

[0060] In diagram 500, the UE 505 is roaming or expected to roam from PLMN1 540 to PLMN2 542 during an ongoing application session (e.g., AIML service, AIMLE service, ML process, FL process, ML / FL process)

[0061] AIMLE server 1 536 is mapped to PLMN1 540. Roaming may lead to an update of the serving AIMLE server for the UE. AIMLE client 532 may change AIMLE server while roaming to the target area covered by a different PLMN (e.g., PLMN2 542).

[0062] A change of the AIMLE server may comprise a change of the ML / FL server role for an ML / FL process. The second AIMLE Server 538 may undertake the ML / FL server role / functionality for the ongoing ML / FL process. A change of AIMLE server may not comprise a change to the ML / FL server role; e.g., the ML / FL server role remains the same in the first AIMLE server 536. This may not require a context transfer for the role / capability change, and only the serving AIMLE server changes.

[0063] The ML repository 541 / 543 may store ML models and ML / FL member information. If the AIMLE server changes (e.g., due to roaming), the ML repositoryDocket No. SMM920250066-GR-NP541 / 543 may also change based on the deployment (e.g., from the first ML repository 541 to the second ML repository 543). There are two scenarios which may require interactions among ML repositories:

[0064] Each AIMLE server 536 / 538 is deployed at the edge / cloud by the edge / cloud provider, and the ML repository is a single entity which is expected to store roaming information (e.g., multi-MNO info), and there is no update required based on the switching of the AIMLE server for the ongoing AI / ML service.

[0065] Each AIMLE server 536 / 538 is deployed by each MNO, and the ML repository is different per PLMN. The AIMLE server 536 / 538 may discover and subscribe to the target ML repository (e.g., the second ML repository 543) to receive information on the ML model and FL / ML members for the ongoing ML / FL process. This may involve migration and an app context transfer between ML repositories 541 / 543 or via the AIMLE servers 536 / 538.

[0066] Figure 6 is a diagram 600 illustrating an example architecture for supporting AIML-enabled ADAE analytics in accordance with aspects of the present disclosure. The diagram comprises a VAL 610 and SEAL 620. The VAL 610 comprises VAL server(s) 616 connected to an AD AES 622 in SEAL 620 via ADAE-S 623. The AD AES 622 connects to AIMLE server 636 via AIML-X 637. The AIMLE server 636 connects to an ML Repository 641 via an AIML-R 633 reference point. ADAE-S 623 may be an architecture and AIML-X 637 may be a conceptual framework.

[0067] 3GPP SA6 in Rel-18 specified an application data analytics enablement functionality (e.g., AD AES 622, TS 23.436 V19.3.0). AD AES 622 is a SEAL functionality for providing end-to-end performance analytics (e.g., VAL server 616 performance). There are multiple analytics services defined in Rel-18 including: application performance analytics; slice-specific application performance analytics; UE-to-UE application performance analytics; location accuracy analytics Service; API analytics; slice usage pattern analytics; edge load analytics; edge computing preparation analytics; support for server-to-server performance analytics; support for collision detection analytics; support for location-related; UE group analytics; support for VAL performance analytics for tetheredDocket No. SMM920250066-GR-NPUEs; support for Application Layer AI / ML Member Capability Analytics; support DN Energy Efficiency analytics; and support ML Model Performance Degradation Detection.

[0068] With respect to ML-enabled analytics, diagram 600 illustrates the architecture representation including AIMLE (as specified in 3 GPP TS 23.482 VI 9.1.0) for supporting ML-enabled analytics in AD AES 622. In this representation, the AIML support capabilities serve AD AES 622 to enhance its analytics services. Based on the VAL request to provide ML-enabled analytics, AD AES 622 may consume AIMLE services (e.g., for ML model training for a given analytics ID) to derive application layer data analytics.

[0069] For the interaction between AIMLE server 636 and AD AES 622, AIML-X is introduced to support consuming AIMLE services for deriving ADAE analytics (e.g., VAL server 616 performance analytics).

[0070] The ML repository 641 is specified in 3 GPP TS 23.482 VI 9.1.0 as a repository for the ML model and registry for the ML-related information (such as ML / FL members). This repository may be utilized by AD AES 622 via AIMLE server 636 (via AIML-R 633) for fetching ML-related information (e.g., trained ML model, ML / FL members) which is used for a given ADAE analytics event.

[0071] Figure 7 is a diagram 700 illustrating an example of a UE roaming between wireless communication networks with different SEAL servers in accordance with aspects of the present disclosure. Diagram 700 may illustrate an example of a scenario ofAD AE+ AIMLE in roaming. Diagram 700 may illustrate a deployment of nodes in such an AD AE+ AIMLE roaming scenario.

[0072] The diagram 700 includes a first 3GPP Network (e.g., PLMN1, HPLMN) 740 providing first network coverage 746 and a second 3GPP Network (e.g., PLMN2, VPLMN) 742 providing second network coverage 748. The diagram 700 also includes a UE 705 which is initially being served by the first 3 GPP Network 740 via first network coverage 746.

[0073] The UE 705 comprises an ADAE / AIMLE client 732 that connects to a first SEAL server (e.g., comprising a first ADAE and a first AIMLE) 736 via the first 3GPP Network 740. The UE 705 roams from the first network coverage 746 of the first 3GPPDocket No. SMM920250066-GR-NPNetwork 740 to the second network coverage 748 of the second 3 GPP Network 742. The AD AE / AIMLE client 732 then connects to a second SEAL server (e.g., comprising a second ADAE and a second AIMLE) 738. The first SEAL server 736 and second SEAL server 738 are connected to VAL servers 716.

[0074] In 3GPP SA6, as part of EDGEAPP (see TS 23.558 V19.5.0) there is support for roaming (e.g., cross-ECS impacts). To support UEs that are roaming, the EEL uses ECSs provided in HPLMN and VPLMN. The EEC in the UE obtains EEL services from V-ECS and V-EES. EDGE-10 reference point is used between the H-ECS and the V-ECS. Two roaming models are supported for edge enabling applications: Local breakout (LBO) roaming architecture; and Home routed (HR) roaming architecture.

[0075] In both architecture options, the EDN is located in the V-PLMN and is accessed via an LBO or HR-SBO session. More details on the two options are provided in TS 23.558 VI 9.5.0 clause 6.2a. In both cases, there is no change of EES while roaming to the VPLMN. In addition, in SA6 as part of NSCE, there is support for service continuity while UE roaming. This functionality is provided to the VAL 610 to make predictive slice modification where NSCE service provider provides its services when connected to two PLMNs and has SLA with them. The NSCE server checks with 5GS (e.g., 0AM, 5GC) whether the serving slice is available and can offer the same performance at the target PLMN and make slice modification decision if needed.

[0076] Predictive slice modification in Inter-PLMN based slice service continuity procedures are specified in clause 9.13 of TS 23.435 V19.3.0. In this feature, the NSCE server initially receives an expected / predicted UE location / mobility change request outside a PLMN1 slice service area for one or more UEs within the VAL application session (e.g., such session can be a V2X session). Then, the NSCE server checks with 5GS (e.g., 0AM, 5GC) whether the serving slice is available and can offer the same performance at the target PLMN. The NSCE server evaluates the need for a slice modification (e.g., a slice lifecycle related trigger change). Based on this decision / recommendation, it provides the action to the 0AM of PLMN2 proactively, before UE mobility happens.

[0077] In Rel-20, in TR 23.700-83 V0.1.0, there is a key issue (#3) to study how AIMLE services can support mobility, including roaming scenarios where UEs accessDocket No. SMM920250066-GR-NPAI / ML services in visited networks, and cross-PLMN environments where AI / ML operations involve entities from different operators. This relates to both the continuity of AI / ML services and the federation across MNOs and CSPs / ECSPs.

[0078] In particular, as ML model inference increasingly moves toward the network edge to meet ultra-low latency and bandwidth efficiency demands (e.g., in real-time video analytic, industrial automation), maintaining inference continuity across mobility events becomes critical. When the VAL UEs move across network boundaries or service areas, edge AIMLE instances may need to be reselected, dynamically discovered, or seamlessly replaced to ensure uninterrupted AI / ML inference execution and delivery.

[0079] The current AIMLE specifications and ongoing studies focus on static deployments and assume the VAL UE remains within its home network. However, real- world deployments may require: 1. VAL UEs to maintain access to AIMLE services when roaming into visited PLMNs or NPNs; 2. Cross-border AI / ML service collaboration (e.g., in eV2X or multiplayer gaming scenarios); and / or 3. Edge inference or Al model access to continue seamlessly even as the UE moves between coverage areas.

[0080] There is an ongoing requirement for developments in the following aspects: 1. identify the use cases for supporting multiple PLMN / NPN, and across MNOs and CSPs / ECSPs scenarios; 2. whether and how AIMLE services can support VAL UEs that roam into visited PLMNs or NPNs, e.g., registration, discovery, and access of AIMLE in the visited domain, as well as go back to home domain; 3. whether and how to ensure service continuity and switching for inference tasks or Al service sessions during UE mobility across MNOs and CSPs / ECSPs; 4. whether and how UEs or VAL functions may discover the most suitable AIMLE instance in edge-specific scenarios while UEs move across network boundaries or service areas, considering latency, capability, and service policy.

[0081] Examples described herein generally relate to ADAE and AIML services, and in particular the ML-enabled analytics at AD AES with the support of AIMLE functionality. Examples described herein generally relate to supporting the ML-enabled analytics service continuity while one or more UEs involved in the analytics / AIML service are roaming to different PLMN. The ADAES / AIMLE server may be expected to be deployed only for aDocket No. SMM920250066-GR-NPgiven area covered by the first PLMN (e.g., at MNO or in trusted MNO domain as trusted AF).

[0082] Examples described herein generally relate to a mechanism for supporting the ML-enabled analytics capability in scenarios where the ML model training / inference happens at the UE (which may comprise a AIMLE client and / or ADAE client) for an analytics task (e.g., analytics service) which is handled or coordinated by the AD AES, while the UE is expected to move to an area served by another PLMN /

[0083] Some examples described herein may relate to server-enabled service continuity for VAL session analytics (e.g., see process flow 800 described below). Some examples described herein may relate to client-enabled service continuity for VAL session analytics (e.g., see process flow 900 described below).

[0084] Figure 8 illustrates an example of a process flow 800 for providing an analytics service in accordance with aspects of the present disclosure. Process flow 800 may be an example of server-enabled service continuity for VAL session analytics. Process flow 800 may illustrate an example of AD AES support for VAL session performance analytics in roaming scenarios.

[0085] The process flow 800 may implement or be implemented by aspects of the wireless communication system 100. For example, the process flow 800 may include a VAL UE 805 (comprising a VAL Client 812 and an ADAEC / AIMLEC 832), a S-ADAES + S-AIMLES (e.g., H-PLMN) 836, a Consumer (e.g., VAL server, AF) 816, a T-ADAES + T-AIMLES (e.g., V-PLMN) 838 and ML repository (or multiple ML repositories) 841, which may be one or more examples of devices described herein with reference to Figure 1.

[0086] The process flow 800 may be referred to as a procedure, including one or more operations performed by one or more of the VAL UE 805, a S-ADAES + S-AIMLES 836, a Consumer 816, a T-ADAES + T-AIMLES 838 and ML repository 841. In the example of Figure 8, the process flow 800 may include client enabled continuity support for ML model workflow operation.

[0087] In the following description of the process flow 800, the operations or signalling performed between one or more of the VAL UE 805, a S-ADAES + S-AIMLES 836, aDocket No. SMM920250066-GR-NPConsumer 816, a T-ADAES + T-AIMLES 838 and ML repository 841 may be performed or signalled (e.g., transmitted, received) in a different order than the example order shown, or the operations or signalling performed by one or more of the VAL UE 805, a S-ADAES + S-AIMLES 836, a Consumer 816, a T-ADAES + T-AIMLES 838 and ML repository 841 may be performed or signalled (e.g., transmitted, received) in different orders or at different times. Some operations or signalling may also be omitted from the process flow 800. Additionally, although some operations or signalling may be shown to occur at different times, these operations or signalling may occur at the same time or in overlapping time periods

[0088] The producer of the ML model inference is the AIMLEC 832 and the consumer is the ADAE server and ADAEC 832 for providing ADAE analytics. The ADAE analytics may comprise VAL session performance analytics (e.g., as described in clause 8.2.3 of TS 23.436 V19.3.0). The ML model training and inference may be consumed by the AIMLE layer, and the scenario involves roaming the VAL UE 805 while performing the local analytics / inference.

[0089] Process flow 800 illustrates the procedure where the VAL session performance analytics are performed based on data collected from the ongoing VAL sessions for the scenario when the VAL UE 805 is expected to move to a different service area operated by a different PLMN (e.g., VPLMN).

[0090] The AIMLE / DAE may be co-located or co-deployed service modules. TheADAE may provide the analytics functionality and the ML model inference. The AIMLE may provide the ML model training functionality. Alternatively, the ADAE may provide the analytics functionality. The AIMLE may provide the ML model training and inference functionality. The ADAE may be ML enabled by consuming AIMLE services for training and inference.

[0091] The ADAEC / AIMLEC 832 is connected to S-ADAES / S-AIMLES 836. The data producers (e.g., A-ADRF, VAL Client 812) may be pre-configured with data producer profiles for the data they can provide. The AD AES 836 / 838 and ADAEC 832 may have discovered available data producers and their data producer profiles per PLMN.Docket No. SMM920250066-GR-NP

[0092] Process flow 800 starts at step 871. The consumer 816 of the AD AES analytics service sends (e.g., transmits, outputs) a VAL performance analytics subscription request to S-ADAES 836 and provides at least one of the analytics event ID e.g., "VAL session performance analytics", the target VAL UE ID, VAL server IDA / AL application ID, the time validity and area of the request, the required confidence level, and exposure level for providing UE analytics. If the consumer 816 is the VAL server, the VAL server may provide, to ADAEC 832, application data related to the UE expected route / trajectory and VAL application traffic schedule / expected session time. The consumer 816 may also provide the allowed MNO information (e.g., PLMN ID) in case the analytics service is expected to operate in case of UE roaming.

[0093] Step 872. The S-ADAES 836 sends (e.g., transmits, outputs) a subscription response as an acknowledgment (ACK) to the consumer 816.

[0094] Step 873. The S-ADAES 836 selects (e.g., determines) the corresponding ADAEC 832 of the VAL UE 805 for which the local analytics are to be performed. Additionally, the S-ADAES 836 determines (e.g., selects) the entity (or entities) to be considered for ML model training for the analytics event ID. The S-ADAES 836 requests the selected S-AIMLES 836 to provide ML model training and / or inference for the analytics event ID. The S-ADAES 836 may also provide the requirements received in step 871, e.g., type of training, whether it is online or offline training, time to provide trained models.

[0095] The following steps may be similar to steps 4-6 of procedure in TS 23.436 V19.3.0 clause 8.2.3 for the data collection start by the VAL UE 805 (using ADAEC / AIMLEC 832). The following steps may relate to starting the ongoing VAL session and deriving ML-enabled analytics / data from the VAL UE 805 to the S-ADAES + S-AIMLES 836; for example, as discussed in TS 23.436 V19.3.0 clause 8.2.3 and TR 23.700-82 VI 9.1.0 clause 8.4.1.

[0096] Step 874. The VAL Client 812, ADAEC / AIMLEC 832, ML repository 841 and S-ADAES + S-AIMLES 836 perform subscription and data collection for the ML model training / inference task. This may be referred to as a runtime analytics phase (e.g., local- global analytics).Docket No. SMM920250066-GR-NP

[0097] Step 875. The VAL UE 805 detects (e.g., determines, predicts) a trigger event. The trigger event indicates that the VAL UE 805 is expected to move to an area out of coverage of PLMN 1 (e.g., out of coverage of the HPLMN. This trigger event may be detected by the VAL client 812 and passed via ADAE-C or AIMLE-C to the ADAEC / AIMLEC 832.

[0098] Step 875a. The VAL UE 805 (e.g., AIMLEC / ADAE client 832) sends (e.g., transmits, outputs) an indication of the trigger event to the S-ADAES + S-AIMLES 836. The indication of the trigger event may comprise a UE mobility report. The UE mobility report may comprise an indication that the UE is approaching a new service area. The UE mobility report may comprise an indication that the UE is approaching a second PLMN (e.g., the VPLMN, cross-PLMN related to mobility).

[0099] Step 875b. Alternatively, the VAL server 816 monitors UE mobility or analytics (e.g., from core network, NWDAF analytics on UE mobility, or location information from SEAL LMS or LMF via NEF) and informs the S-ADAES + S-AIMLES 836 about the detection of a mobility event e.g., that of mobility of the VAL UE 805 to an area covered by a different PLMN e.g., the second PLMN, VPLMN, cross-PLMN related to mobility. An LMF report on mobility may not include an indication of the cross-PLMN related to mobility. Cross-PLMN related mobility may be identified at the S-ADAES + S-AIMLES 836 using information from the VAL UE 805 or based on pre-configuration at the AIMLE / ADAE provider.

[0100] Step 876. S-AIMLES or S-ADAES 836 identifies (e.g., determines, checks with the CN, ECS-ER or the VAL UE 805) the V-PLMN information and the time when the VAL UE 805 is expected to reach V-PLMN. The S-AIMLES or S-ADAES 836 may identify the V-PLMN information based on the area of mobility.

[0101] PLMN specific detection and fetching of the VPLMN information may comprise at least one of: ADAEC / AIMLEC 832 indicating a serving PLMN ID to the S- ADAES + S-AIMLE 836; S-AIMLES 836 requesting a notification (e.g., from other AIMLE servers) of when a UE or group of UEs registers to the "other AIMLE servers"; and the S-AIMLES 836 checking (e.g., determining) with the UDM the serving PLMN ID for a UE.Docket No. SMM920250066-GR-NP

[0102] Step 877. The S-AIMLES 836 fetches (e.g., requests, retrieves, receives), from ML repository 841, context information on available AIMLE / ADAE servers in the target V-PLMN. If the ML repository 841 is deployed by PLMN / MNO, there may be interaction among ML repositories of the same provider (e.g., based on the service agreements or synchronization via the ADAE / AIMLE servers).

[0103] Step 878. The S-ADAES + S-AIMLES selects (e.g., determines) the T-ADAES + T-AIMLES 838 from a list of AIMLE / ADAE servers, based on the selection criteria e.g., based on the context information fetched in step 877.

[0104] The context information may comprise at least one of: capabilities (e.g., computational, processing) and coverage (e.g., geographical or topological area covered by T-ADAES + T-AIMLES 838); load and energy status of the T-ADAES + T-AIMLES 838 based on monitoring or querying possible overload or high energy consumption (this may be provided by the target SEAL service provider, e.g., edge platform provider); expected delay / latency for accessing the T-ADAES + T-AIMLES 838 (e.g., in case of relaying ML inference task); vendor compatibility (e.g., when the selection of a T-ADAES + T-AIMLES 838 is based on which vendor provides the server (this may be either the same vendor only, or a list of selected vendors based on the service agreements among MNOs / SEAL providers); cost for task migration (e.g., the computational effort and / or processing required, or time required or energy required for offloading a task to the T-ADAES + T- AIMLES 838) (such cost may be based on the distance among servers (e.g. edge to regional cloud) or based on the task to be offloaded) (this may be also a parameter to identify whether to use Option 1 or Option 2 as discussed below in step 879); and a rate factor (e.g., ranking factor, weighting factor) of T-ADAES + T-AIMLES 838 based on fulfilment of previous tasks and statistics. The rate factor may be stored and updated at the ML repository 841 (or shared among repositories) after fulfilling each task based on consumer feedback or based on internal evaluation at the AIMLE / ADAE (e.g., based on success or failure of tasks under similar conditions).

[0105] The expected delay / latency for accessing the T-ADAES + T-AIMLES 838 may be estimated based on probes. The expected delay / latency for accessing the T- ADAES + T-AIMLES 838 may be estimated based on relative location of platform hostingDocket No. SMM920250066-GR-NPthe T-ADAES + T-AIMLES 838. The expected delay / latency for accessing the T-ADAES + T-AIMLES 838 may be estimated based on the interface / API statistics on delay or failures based on high delay.

[0106] Step 879. S-ADAES + S-AIMLES 836 requests (e.g., negotiates with, confirms) the T-ADAES + T-AIMLES 838 to undertake the training-l- analytics task for the one or more UEs of interest, which may be at least one of: migrating the capability (e.g., app, service, and / or PaaS) for continuing the ML enabled analytics aggregation and exposure (referred to as Option 1); relaying the analytics output to the consumer 816 via VPLMN (referred to as Option 2); and relaying the model training output to S-ADAES 836 to allow S-ADAES 836 continue the analytics task at the HPLMN.

[0107] Step 880. S-ADAES + S-AIMLES 836 requests (e.g., informs) ADAEC / AIMLEC 832 to change S-ADAES + S-AIMLES 836 and passes (e.g., sends, transmits) the T-ADAES + T-AIMLES 838 server information to ADAEC / AIMLEC 832. The S-ADAES + S-AIMLES 836 may also send (e.g., transmit, pass) information relating to limitations in the target V-PLMN (lower priority, capacity limitations for ML operation). The ADAEC / AIMLEC 832 confirms (e.g., acknowledges) the change and prepares to connect to the T-ADAES + T-AIMLES 838 via the S-ADAES + S-AIMLES 836. The ADAEC / AIMLEC 832 then connects to the T-ADAES + T-AIMLES 838.

[0108] Step 881. The T-ADAES + T-AIMLES 838 sends (e.g., transmits, outputs) a notification (e.g., indication) to the S-ADAES + S-AIMLES 836indicating roaming of the UE(s) of interest (see step 881a). The S-ADAES + S-AIMLES 836 may forward (e.g., send, transmit, output) the notification to the VAL server 816 (as expected consumer of the ML inference output).

[0109] Step 882. The T-ADAES + T-AIMLES 838 may provide the ML inference output to the VAL server 816 via the S-ADAES + S-AIMLES 836, if the task involves relaying the ML inference output over the VPLMN. Alternatively, the T-ADAES + T- AIMLES 838 sends (e.g., transmits, outputs) the ML inference output to VAL server 816; if the T-ADAES + T-AIMLES 838 is the new ML inference server.Docket No. SMM920250066-GR-NP

[0110] Figure 9 illustrates an example of a process flow 900 for providing an analytics service in accordance with aspects of the present disclosure. Process flow 900 may be an example of client-enabled service continuity for VAL session analytics. Process flow 900 may illustrate an example of client-enabled support for VAL session performance analytics in roaming scenarios.

[0111] The process flow 900 may implement or be implemented by aspects of the wireless communication system 100. For example, the process flow 900 may include a VAL UE 905 (comprising a VAL Client 912 and an ADAEC / AIMLEC 932), a S-ADAES + S-AIMLES (e.g., H-PLMN) 936, a Consumer (e.g., VAL server, AF) 916, a T-ADAES + T-AIMLES (e.g., V-PLMN) 938 and ML repository (or multiple ML repositories) 941, which may be one or more examples of devices described herein with reference to Figure 1.

[0112] The process flow 900 may be referred to as a procedure, including one or more operations performed by one or more of the VAL UE 905, a S-ADAES + S-AIMLES 936, a Consumer 916, a T-ADAES + T-AIMLES 938 and ML repository 941. In the example of Figure 9, the process flow 900 may include client enabled continuity support for ML model workflow operation.

[0113] In the following description of the process flow 900, the operations or signalling performed between one or more of the VAL UE 905, a S-ADAES + S-AIMLES 936, a Consumer 916, a T-ADAES + T-AIMLES 938 and ML repository 941 may be performed or signalled (e.g., transmitted, received) in a different order than the example order shown, or the operations or signalling performed by one or more of the VAL UE 905, a S-ADAES + S-AIMLES 936, a Consumer 916, a T-ADAES + T-AIMLES 938 and ML repository 941 may be performed or signalled (e.g., transmitted, received) in different orders or at different times. Some operations or signalling may also be omitted from the process flow 900. Additionally, although some operations or signalling may be shown to occur at different times, these operations or signalling may occur at the same time or in overlapping time periods

[0114] In process flow 900, the producer of the ML model inference is the AIMLEC932 and the consumer is the ADAE server and ADAEC 832 for providing ADAE analytics. The ADAE analytics may comprise VAL session performance analytics (e.g., as describedDocket No. SMM920250066-GR-NPin clause 8.2.3 of TS 23.436 V19.3.0). The ML model training and inference may be consumed by the AIMLE layer, and the scenario involves roaming the VAL UE 905 while performing the local analytics / inference. Process flow 900 supports the UE-triggered early detection of mobility to a different PLMN and the local UE selection of analytics / AIMLE server at the VPLMN.

[0115] Process flow 900 starts with steps 971-977 which correspond to (e.g., are identical to) steps 871-877 of process flow 800.

[0116] Step 978. The S-ADAES + S-AIMLES 936 sends (e.g., transmits, outputs), to the ADAEC / AIMLEC 932, a notification (e.g., an alert, a command, a request, a message) for selecting a new AIMLE / D AE server. The notification may comprise a list of available AIMLE / D AE servers in the VPLMN and / or some context information (e.g., rating, ranking, weighting, vendor information, capability information, energy status, and / or load status).

[0117] Step 979. The ADAEC / AIMLEC 932 selects (e.g., determines) the T-ADAES + T-AIMLES 938 from the list of AIMLE / ADAE servers, based on a selection criteria. The selection criteria may comprise the context information (fetched from the ML repository 841 in step 977).

[0118] The context information may comprise at least one of: capabilities (e.g., computational, processing) and coverage (e.g., geographical or topological area covered by T-ADAES + T-AIMLES 938); load and energy status of the T-ADAES + T-AIMLES 938 based on monitoring or querying possible overload or high energy consumption (this may be provided by the target SEAL service provider, e.g., edge platform provider); expected delay / latency for accessing the T-ADAES + T-AIMLES 938 (e.g., in case of relaying ML inference task); vendor compatibility (e.g., when the selection of a T-ADAES + T-AIMLES 938 is based on which vendor provides the server (this may be either the same vendor only, or a list of selected vendors based on the service agreements among MNOs / SEAL providers); cost for task migration (e.g., the computational effort and / or processing required, or time required or energy required for offloading a task to the T-ADAES + T- AIMLES 938) (such cost may be based on the distance among servers (e.g. edge to regional cloud) or based on the task to be offloaded) (this may be also a parameter to identifyDocket No. SMM920250066-GR-NPwhether to use Option 1 or Option 2 as discussed below in step 981); and a rate factor (e.g., ranking factor, weighting factor) of T-ADAES + T-AIMLES 938 based on fulfilment of previous tasks and statistics. The rate factor may be stored and updated at the ML repository 941 (or shared among repositories) after fulfilling each task based on consumer feedback or based on internal evaluation at the AIMLE / ADAE (e.g., based on success or failure of tasks under similar conditions).

[0119] Pre-defined policies from the S-ADAES + S-AIMLES 936 or VAL server 916 may be provided in steps 971 to 978 to dictate the S-ADAES + S-AIMLES 936 selection criteria (e.g., policies on minimum rating, allowed vendors, preferred AIMLE servers).

[0120] Step 980. ADAEC / AIMLEC 932 sends (e.g., transmits, outputs) to the S- ADAES + S-AIMLES 936 the selected T-ADAES + T-AIMLES 938 and the expected time and area of roaming.

[0121] Step 981. The S-ADAES + S-AIMLES 936 requests (e.g., negotiates with, confirms) the T-ADAES + T-AIMLES 938 to undertake the training-l- analytics task for the one or more UEs of interest, which may be at least one of: migrating the capability (e.g., app, service, and / or PaaS) for continuing the ML enabled analytics aggregation and exposure (referred to as Option 1); relaying the analytics output to the consumer 816 via VPLMN (referred to as Option 2); and relaying the model training output to S-ADAES 936 to allow S-ADAES 936 continue the analytics task at the HPLMN.

[0122] Step 982. The S-ADAES + S-AIMLES 936 may also send to the T-ADAES + T-AIMLES 938 the configuration information for the analytics task in case of migrating the capability. This may include the analytics KPIs (e.g., accuracy, analytics type, and / or vendor compatibility).

[0123] Step 983. The ADAEC / AIMLEC 932 connects to T-ADAES + T-AIMLES 938.

[0124] Step 984. The T-ADAES + T-AIMLES 938 sends (e.g., transmits, outputs), toS-ADAES + S-AIMLES 936, a notification to indicating roaming of the UE(s) of interest (see step 984a). The S-ADAES + S-AIMLES 936 may forward (e.g., send, transmit, output) the notification indicating roaming of the UE(s) of interest to the VAL server 916 (as expected consumer of the ML inference output).Docket No. SMM920250066-GR-NP

[0125] Step 985. The T- AD AES + T-AIMLES 938 may provide the ML inference output to the VAL server 916 via the S-ADAES + S-AIMLES 936, if the task involves relaying the ML inference output over the VPLMN. Alternatively, the T- D AES + T- AIMLES 938 sends (e.g., transmits, outputs) the ML inference output to VAL server 916, if the T- D AES + T-AIMLES 938 is the new ML inference server.

[0126] Figure 10 illustrates an example of a UE 1000 in accordance with aspects of the present disclosure. The UE 1000 may include a processor 1002, a memory 1004, a controller 1006, and a transceiver 1008. The processor 1002, the memory 1004, the controller 1006, or the transceiver 1008, or various combinations thereof or various components thereof may be examples of means for performing various aspects of the present disclosure as described herein. These components may be coupled (e.g., operatively, communicatively, functionally, electronically, electrically) via one or more interfaces.

[0127] The processor 1002, the memory 1004, the controller 1006, or the transceiver 1008, or various combinations or components thereof may be implemented in hardware (e.g., circuitry). The hardware may include a processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), or other programmable logic device, or any combination thereof configured as or otherwise supporting a means for performing the functions described in the present disclosure.

[0128] The processor 1002 may include an intelligent hardware device (e.g., a general- purpose processor, a DSP, a CPU, an ASIC, an FPGA, or any combination thereof). In some implementations, the processor 1002 may be configured to operate the memory 1004. In some other implementations, the memory 1004 may be integrated into the processor 1002. The processor 1002 may be configured to execute computer-readable instructions stored in the memory 1004 to cause the UE 1000 to perform various functions of the present disclosure.

[0129] The memory 1004 may include volatile or non-volatile memory. The memory 1004 may store computer-readable, computer-executable code including instructions when executed by the processor 1002 cause the UE 1000 to perform various functions described herein. The code may be stored in a non-transitory computer-readable medium such theDocket No. SMM920250066-GR-NPmemory 1004 or another type of memory. Computer-readable media includes both non- transitory computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A non-transitory storage medium may be any available medium that may be accessed by a general-purpose or special-purpose computer.

[0130] In some implementations, the processor 1002 and the memory 1004 coupled with the processor 1002 may be configured to cause the UE 1000 to perform one or more of the functions described herein (e.g., executing, by the processor 1002, instructions stored in the memory 1004). For example, the processor 1002 may support wireless communication at the UE 1000 in accordance with examples as disclosed herein. The UE 1000 may be configured to support a means for determining to support a first network entity with an analytics service, wherein the UE is served by a first wireless communication network; determining that the UE is to be served by a second wireless communication network; and determining a second network entity for supporting the analytics service whilst the UE is being served by the second wireless communication network.

[0131] The controller 1006 may manage input and output signals for the UE 1000. The controller 1006 may also manage peripherals not integrated into the UE 1000. In some implementations, the controller 1006 may utilize an operating system such as iOS®, ANDROID®, WINDOWS®, or other operating systems. In some implementations, the controller 1006 may be implemented as part of the processor 1002.

[0132] In some implementations, the UE 1000 may include at least one transceiver 1008. In some other implementations, the UE 1000 may have more than one transceiver 1008. The transceiver 1008 may represent a wireless transceiver. The transceiver 1008 may include one or more receiver chains 1010, one or more transmitter chains 1012, or a combination thereof.

[0133] A receiver chain 1010 may be configured to receive signals (e.g., control information, data, packets) over a wireless medium. For example, the receiver chain 1010 may include one or more antennas for receive the signal over the air or wireless medium. The receiver chain 1010 may include at least one amplifier (e.g., a low-noise amplifier (LNA)) configured to amplify the received signal. The receiver chain 1010 may include atDocket No. SMM920250066-GR-NPleast one demodulator configured to demodulate the receive signal and obtain the transmitted data by reversing the modulation technique applied during transmission of the signal. The receiver chain 1010 may include at least one decoder for decoding the processing the demodulated signal to receive the transmitted data.

[0134] A transmitter chain 1012 may be configured to generate and transmit signals (e.g., control information, data, packets). The transmitter chain 1012 may include at least one modulator for modulating data onto a carrier signal, preparing the signal for transmission over a wireless medium. The at least one modulator may be configured to support one or more techniques such as amplitude modulation (AM), frequency modulation (FM), or digital modulation schemes like phase-shift keying (PSK) or quadrature amplitude modulation (QAM). The transmitter chain 1012 may also include at least one power amplifier configured to amplify the modulated signal to an appropriate power level suitable for transmission over the wireless medium. The transmitter chain 1012 may also include one or more antennas for transmitting the amplified signal into the air or wireless medium.

[0135] Figure 11 illustrates an example of a processor 1100 in accordance with aspects of the present disclosure. The processor 1100 may be an example of a processor configured to perform various operations in accordance with examples as described herein. The processor 1100 may include a controller 1102 configured to perform various operations in accordance with examples as described herein. The processor 1100 may optionally include at least one memory 1104, which may be, for example, an L1 / L2 / L3 cache. Additionally, or alternatively, the processor 1100 may optionally include one or more arithmetic-logic units (ALUs) 1106. One or more of these components may be in electronic communication or otherwise coupled (e.g., operatively, communicatively, functionally, electronically, electrically) via one or more interfaces (e.g., buses).

[0136] The processor 1100 may be a processor chipset and include a protocol stack (e.g., a software stack) executed by the processor chipset to perform various operations (e.g., receiving, obtaining, retrieving, transmitting, outputting, forwarding, storing, determining, identifying, accessing, writing, reading) in accordance with examples as described herein. The processor chipset may include one or more cores, one or more caches (e.g., memory local to or included in the processor chipset (e.g., the processor 1100) orDocket No. SMM920250066-GR-NPother memory (e.g., random access memory (RAM), read-only memory (ROM), dynamic RAM (DRAM), synchronous dynamic RAM (SDRAM), static RAM (SRAM), ferroelectric RAM (FeRAM), magnetic RAM (MRAM), resistive RAM (RRAM), flash memory, phase change memory (PCM), and others).

[0137] The controller 1102 may be configured to manage and coordinate various operations (e.g., signalling, receiving, obtaining, retrieving, transmitting, outputting, forwarding, storing, determining, identifying, accessing, writing, reading) of the processor 1100 to cause the processor 1100 to support various operations in accordance with examples as described herein. For example, the controller 1102 may operate as a control unit of the processor 1100, generating control signals that manage the operation of various components of the processor 1100. These control signals include enabling or disabling functional units, selecting data paths, initiating memory access, and coordinating timing of operations.

[0138] The controller 1102 may be configured to fetch (e.g., obtain, retrieve, receive) instructions from the memory 1104 and determine subsequent instruction(s) to be executed to cause the processor 1100 to support various operations in accordance with examples as described herein. The controller 1102 may be configured to track memory address of instructions associated with the memory 1104. The controller 1102 may be configured to decode instructions to determine the operation to be performed and the operands involved. For example, the controller 1102 may be configured to interpret the instruction and determine control signals to be output to other components of the processor 1100 to cause the processor 1100 to support various operations in accordance with examples as described herein. Additionally, or alternatively, the controller 1102 may be configured to manage flow of data within the processor 1100. The controller 1102 may be configured to control transfer of data between registers, arithmetic logic units (ALUs), and other functional units of the processor 1100.

[0139] The memory 1104 may include one or more caches (e.g., memory local to or included in the processor 1100 or other memory, such RAM, ROM, DRAM, SDRAM, SRAM, MRAM, flash memory, etc. In some implementations, the memory 1104 may reside within or on a processor chipset (e.g., local to the processor 1100). In some otherDocket No. SMM920250066-GR-NPimplementations, the memory 1104 may reside external to the processor chipset (e.g., remote to the processor 1100).

[0140] The memory 1104 may store computer-readable, computer-executable code including instructions that, when executed by the processor 1100, cause the processor 1100 to perform various functions described herein. The code may be stored in a non-transitory computer-readable medium such as system memory or another type of memory. The controller 1102 and / or the processor 1100 may be configured to execute computer-readable instructions stored in the memory 1104 to cause the processor 1100 to perform various functions. For example, the processor 1100 and / or the controller 1102 may be coupled with or to the memory 1104, the processor 1100, the controller 1102, and the memory 1104 may be configured to perform various functions described herein. In some examples, the processor 1100 may include multiple processors and the memory 1104 may include multiple memories. One or more of the multiple processors may be coupled with one or more of the multiple memories, which may, individually or collectively, be configured to perform various functions herein.

[0141] The one or more ALUs 1106 may be configured to support various operations in accordance with examples as described herein. In some implementations, the one or more ALUs 1106 may reside within or on a processor chipset (e.g., the processor 1100). In some other implementations, the one or more ALUs 1106 may reside external to the processor chipset (e.g., the processor 1100). One or more ALUs 1106 may perform one or more computations such as addition, subtraction, multiplication, and division on data. For example, one or more ALUs 1106 may receive input operands and an operation code, which determines an operation to be executed. One or more ALUs 1106 be configured with a variety of logical and arithmetic circuits, including adders, subtractors, shifters, and logic gates, to process and manipulate the data according to the operation. Additionally, or alternatively, the one or more ALUs 1106 may support logical operations such as AND, OR, exclusive-OR (XOR), not-OR (NOR), and not-AND (NAND), enabling the one or more ALUs 1106 to handle conditional operations, comparisons, and bitwise operations.

[0142] The processor 1100 may support wireless communication in accordance with examples as disclosed herein. The processor 1100 may be configured to support a meansDocket No. SMM920250066-GR-NPfor determining to support a first network entity with an analytics service, wherein the UE is served by a first wireless communication network; determining that the UE is to be served by a second wireless communication network; and determining a second network entity for supporting the analytics service whilst the UE is being served by the second wireless communication network. Alternatively, the processor 1100 may be configured to or operable to support a means for receiving a first request to support an analytics service associated with a UE served by a first wireless communication network; determining a second network entity to support the analytics service whilst the UE is being served by the second wireless communication network; and transmitting, to the second network entity, a second request to support the analytics service.

[0143] Figure 12 illustrates an example of a NE 1200 in accordance with aspects of the present disclosure. The NE 1200 may include a processor 1202, a memory 1204, a controller 1206, and a transceiver 1208. The processor 1202, the memory 1204, the controller 1206, or the transceiver 1208, or various combinations thereof or various components thereof may be examples of means for performing various aspects of the present disclosure as described herein. These components may be coupled (e.g., operatively, communicatively, functionally, electronically, electrically) via one or more interfaces.

[0144] The processor 1202, the memory 1204, the controller 1206, or the transceiver 1208, or various combinations or components thereof may be implemented in hardware (e.g., circuitry). The hardware may include a processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), or other programmable logic device, or any combination thereof configured as or otherwise supporting a means for performing the functions described in the present disclosure.

[0145] The processor 1202 may include an intelligent hardware device (e.g., a general- purpose processor, a DSP, a CPU, an ASIC, an FPGA, or any combination thereof). In some implementations, the processor 1202 may be configured to operate the memory 1204. In some other implementations, the memory 1204 may be integrated into the processor 1202. The processor 1202 may be configured to execute computer-readable instructionsDocket No. SMM920250066-GR-NPstored in the memory 1204 to cause the NE 1200 to perform various functions of the present disclosure.

[0146] The memory 1204 may include volatile or non-volatile memory. The memory 1204 may store computer-readable, computer-executable code including instructions when executed by the processor 1202 cause the NE 1200 to perform various functions described herein. The code may be stored in a non-transitory computer-readable medium such the memory 1204 or another type of memory. Computer-readable media includes both non- transitory computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A non-transitory storage medium may be any available medium that may be accessed by a general-purpose or special-purpose computer.

[0147] In some implementations, the processor 1202 and the memory 1204 coupled with the processor 1202 may be configured to cause the NE 1200 to perform one or more of the functions described herein (e.g., executing, by the processor 1202, instructions stored in the memory 1204). For example, the processor 1202 may support wireless communication at the NE 1200 in accordance with examples as disclosed herein. The NE 1200 may be configured to support a means for receiving a first request to support an analytics service associated with a UE served by a first wireless communication network; determining a second network entity to support the analytics service whilst the UE is being served by the second wireless communication network; and transmitting, to the second network entity, a second request to support the analytics service.

[0148] The controller 1206 may manage input and output signals for the NE 1200. The controller 1206 may also manage peripherals not integrated into the NE 1200. In some implementations, the controller 1206 may utilize an operating system such as iOS®, ANDROID®, WINDOWS®, or other operating systems. In some implementations, the controller 1206 may be implemented as part of the processor 1202.

[0149] In some implementations, the NE 1200 may include at least one transceiver 1208. In some other implementations, the NE 1200 may have more than one transceiver 1208. The transceiver 1208 may represent a wireless transceiver. The transceiver 1208 mayDocket No. SMM920250066-GR-NPinclude one or more receiver chains 1210, one or more transmitter chains 1212, or a combination thereof.

[0150] A receiver chain 1210 may be configured to receive signals (e.g., control information, data, packets) over a wireless medium. For example, the receiver chain 1210 may include one or more antennas for receive the signal over the air or wireless medium. The receiver chain 1210 may include at least one amplifier (e.g., a low-noise amplifier (LN A)) configured to amplify the received signal. The receiver chain 1210 may include at least one demodulator configured to demodulate the receive signal and obtain the transmitted data by reversing the modulation technique applied during transmission of the signal. The receiver chain 1210 may include at least one decoder for decoding the processing the demodulated signal to receive the transmitted data.

[0151] A transmitter chain 1212 may be configured to generate and transmit signals (e.g., control information, data, packets). The transmitter chain 1212 may include at least one modulator for modulating data onto a carrier signal, preparing the signal for transmission over a wireless medium. The at least one modulator may be configured to support one or more techniques such as amplitude modulation (AM), frequency modulation (FM), or digital modulation schemes like phase-shift keying (PSK) or quadrature amplitude modulation (QAM). The transmitter chain 1212 may also include at least one power amplifier configured to amplify the modulated signal to an appropriate power level suitable for transmission over the wireless medium. The transmitter chain 1212 may also include one or more antennas for transmitting the amplified signal into the air or wireless medium.

[0152] Figure 13 illustrates a flowchart of a method 1300 in accordance with aspects of the present disclosure. The operations of the method 1300 may be implemented by a NE as described herein. In some implementations, the NE may execute a set of instructions to control the function elements of the NE to perform the described functions.

[0153] At 1302, the method 1300 may include receiving a first request to support an analytics service associated with a UE served by a first wireless communication network. The operations of 1302 may be performed in accordance with examples as described herein. In some implementations, aspects of the operations of 1302 may be performed by a NE as described with reference to Figure 12.Docket No. SMM920250066-GR-NP

[0154] At 1304, the method 1300 may include determining a second network entity to support the analytics service whilst the UE is being served by the second wireless communication network. The operations of 1304 may be performed in accordance with examples as described herein. In some implementations, aspects of the operations of 1304 may be performed by a NE as described with reference to Figure 12.

[0155] At 1306, the method 1300 may include transmitting, to the second network entity, a second request to support the analytics service. The operations of 1306 may be performed in accordance with examples as described herein. In some implementations, aspects of the operations of 1306 may be performed a NE as described with reference to Figure 12.

[0156] It should be noted that the method 1300 described herein describes a possible implementation, and that the operations and the steps may be rearranged or otherwise modified and that other implementations are possible.

[0157] There is provided a first network entity for wireless communication, comprising: at least one memory; and at least one processor coupled with the at least one memory and configured to cause the first network entity to: receive a first request to support an analytics service associated with a UE served by a first wireless communication network; determine a second network entity to support the analytics service whilst the UE is being served by the second wireless communication network; and transmit, to the second network entity, a second request to support the analytics service. Such a first network entity tends to improve continuity of the analytics service whilst the UE roams from the first wireless communication network to the second wireless communication network.

[0158] To receive the first request to support an analytics service associated with the UE served by a first wireless communication network, the at least one processor may be configured to cause the first network entity to receive the first request to support an analytics service associated with the UE served by a first wireless communication network from an analytics consumer. The first request to support the analytics service associated with the UE may comprise a request to support the analytics service for the UE. The first request to support the analytics service associated with the UE may comprise a request to support the analytics service involving the UE. The analytics service being associated withDocket No. SMM920250066-GR-NPthe UE may comprise the UE supporting the analytics service. The analytics service being associated with the UE may comprise the UE providing data for the analytics service. The analytics service being associated with the UE may comprise the UE performing at least part of the analytics service. The second network entity may be suitable for supporting the analytics service whilst the UE is being served by the second wireless communication network.

[0159] The first network entity may comprise a first analytics server. The second network entity may comprise a second analytics server. The first network entity may comprise a first AD AES. The second network entity may comprise a second AD AES. The first network entity may comprise a first AIMLE server. The second network entity may comprise a second AIMLE server. The first network entity may comprise a firstAD AE / AIMLE server. The second network entity may comprise a second ADAE / AIMLE server. The first network entity may comprise a source network entity. The second network entity may comprise a target network entity. The analytics consumer may comprise a VAL server. The analytics consumer may comprise an AF.

[0160] The analytics service may comprise an analytics event. The analytics service may comprise an analytics event ID. The analytics service may comprise a ML enabled analytics. The analytics service may comprise a ML enabled analytics aggregation. The analytics service may comprise a ML enabled analytics exposure.

[0161] The UE may comprise at least one of: a VAL UE, a VAL client, an ADAEC, an AIMLEC, and an ADAEC / AIMLEC. The first request to support the analytics service for the UE may comprise a first request to support the analytics service for the VAL client of the UE. The method may further comprise discovering at least one of: the ADAEC of the UE, the AIMLEC of the UE, the ADAEC / AIMLEC of the UE, and an AIMLE server. The AIMLE server may be suitable for supporting the analytics service.

[0162] Discovering the at least one of: the ADAEC of the UE, the AIMLEC of the UE, the ADAEC / AIMLEC of the UE, and an AIMLE server may comprise transmitting, to an ML repository, a request for information relating to the at least one of: the ADAEC of the UE, the AIMLEC of the UE, the ADAEC / AIMLEC of the UE, and an AIMLE server. The method may further comprise selecting the at least one of: the ADAEC of the UE, theDocket No. SMM920250066-GR-NPAIMLEC of the UE, the AD AEC / AIMLEC of the UE, and an AIMLE server. The method may further comprise transmitting, to the ML repository, a request for information relating to the analytics service.

[0163] The first wireless communication network may comprise a PLMN. The second wireless communication network may comprise a PLMN. The first wireless communication network may comprise a HPLMN. The second wireless communication network may comprise a VPLMN. The first wireless communication network may comprise a NPN. The second wireless communication network may comprise a NPN. The first network entity may be connected to the first wireless communication network. The second network entity may be connected to the second wireless communication network.

[0164] The first request to support the analytics service for the UE may comprise at least one of a: VAL UE ID, VAL server ID, VAL application ID, time validity, area of interest, a required confidence level, an exposure level for the analytics service.. The VAL UE ID may comprise an identifier for the UE. The VAL UE ID may comprise an identifier for the VAL client of the UE. The VAL UE may be suitable to support the analytics service.

[0165] The first request to support an analytics service for the UE may comprise information relating to one or more MNO for supporting the analytics service for the UE. The request may comprise allowed MNO info per UE for supporting serving continuing in case of UE roaming.

[0166] The analytics service may be associated with an AIML functionality provided by a first AIMLE server. The analytics service may comprise an AIML functionality provided by a first AIMLE server. An analytics service provider may consume the AIML functionality by a third network entity. The AIML functionality may be embedded in the first network entity. The at least one processor may be further configured to cause the first network entity to transmit, to the first AIMLE server, a request for the AIML functionality for the UE to support the analytics service. AIML functionality (or AI / ML functionality) may comprise undertaking or assisting the operation and / or preparation of the AIML functionality. The AIML functionality may comprise at least one of: an AIMLE service, an Al service, a ML model training operation, ML model inference operation, and an AI / MLDocket No. SMM920250066-GR-NPtask. The first AIMLE server may be collocated with the first network entity. The AIML functionality may comprise at least one of: a model operation; a model retrieval, a model training, a model monitoring, a model selection, a model update; a model storage; a model discovery; a ML operation, an ML training operation, a ML model operation; a ML model inference, and an ML model inference operation. The AIML functionality may support the analytics service. Migration of the analytics service to the second wireless communication network may comprise a change of the AIML functionality as part of the analytics service.

[0167] The at least one processor may be further configured to cause the first network entity to: determine that the UE is to be served by the second wireless communication network. To determine that the UE is to be served by the second wireless communication network, the at least one processor may be further configured to cause the first network entity to: receive an indication that the UE is expected to be served by the second wireless communication network.

[0168] The indication that the UE is expected to be served by the second wireless communication network may be received from the UE. The indication that the UE is expected to be served by the second wireless communication network may be received from the analytics consumer. The indication that the UE is expected to be served by the second wireless communication network may comprise a trigger event. The trigger event may indicate that the UE is expected to move to an area out of coverage of the first wireless communication network. The indication that the UE is expected to be served by the second wireless communication network may comprise an indication of a detection of a mobility event of the UE to an area covered by a different wireless communication network. The indication that the UE is expected to be served by the second wireless communication network may comprise an indication of a detection of a mobility event of the UE to an area covered by the second wireless communication network.

[0169] To determine the second network entity for supporting the analytics service whilst the UE is being served by the second wireless communication network, the at least one processor may be further configured to cause the first network entity to: transmit, to an ML repository, a request for context information for one or more available analytics server(s) in the second wireless communication network. The context information mayDocket No. SMM920250066-GR-NPcomprise at least one of: a capability; a coverage; a load status; an energy status; an expected delay for access; an expected latency for access; vendor compatibility; cost for task migration; and a weighting factor.

[0170] The at least one processor may be further configured to cause the first network entity to: select, based on the context information, the second network entity from the one or more available analytics server(s) in the second wireless communication network.

[0171] The second request to support the analytics service for the UE may comprise a second request to support an AIML functionality for the UE to support the analytics service. The second request to support the analytics service for the UE may comprise a second request to undertake the analytics service for the UE. The second request to support the analytics service for the UE may comprise at least one of: migrating a capability for continuing the analytics service for the UE, relaying analytics output to the analytics consumer via the second wireless communication network, and relaying the AIML functionality output to the first network entity.

[0172] The at least one processor may be further configured to cause the first network entity to transmit, to the UE, a request to serve the second network entity for the analytics service. The request to serve the second network entity for the analytics service may comprise information relating to the second network entity. The request to serve the second network entity for the analytics service may comprise information relating to limitations in the second wireless communication network. The limitations in the second wireless communication network may comprise at least one of: a priority of the AIML functionality, and a capacity limitation for the AIML functionality.

[0173] The at least one processor may be further configured to cause the first network entity to: receive, from the second network entity, a notification of roaming of the UE to the second wireless communication network. The at least one processor may be further configured to cause the first network entity to: transmit, to the analytics consumer, an indication of the notification of roaming of the UE to the second wireless communication network.Docket No. SMM920250066-GR-NP

[0174] The at least one processor may be further configured to cause the first network entity to: relay an output from the second network entity to the analytics consumer. Relaying the output from the second network entity to the analytics consumer may comprise relaying an output of the analytics service from the second network entity to the analytics consumer. Relaying the output from the second network entity to the analytics consumer may comprise relaying an output of an AIML functionality from the second network entity to the analytics consumer.

[0175] To determine the second network entity for supporting the analytics service whilst the UE is being served by the second wireless communication network, the at least one processor may be further configured to cause the first network entity to: transmit, to the UE, a request to select an analytics server from one or more available analytics server(s) in the second wireless communication network based on the context information for the one or more available analytics server(s) in the second wireless communication network.

[0176] The request to select the analytics server from the one or more available analytics server(s) in the second wireless communication network may comprise a list of the one or more available analytics server(s) in the second wireless communication network. The request to select the analytics server from the one or more available analytics server(s) in the second wireless communication network may comprise the context information for the one or more available analytics server(s) in the second wireless communication network.

[0177] To determine the second network entity for supporting the analytics service whilst the UE is being served by the second wireless communication network, the at least one processor may be further configured to cause the first network entity to: receive, from the UE, an indication that the second network entity has been selected. The indication that the second network entity has been selected may comprise at least one of: an expected time, and an area of roaming.

[0178] The at least one processor may be further configured to cause the first network entity to: transmit, to the second network entity, configuration information for the analytics service. The configuration information for the analytics service may comprise a KPI. TheDocket No. SMM920250066-GR-NPKPI may relate to at least one of: an accuracy, an analytics type, and a vendor compatibility.

[0179] There is further provided a method performed or performable by a first network entity, the method comprising: receiving a first request to support an analytics service associated with a UE, wherein the UE is served by a first wireless communication network; determining a second network entity to support the analytics service whilst the UE is being served by the second wireless communication network; and transmitting, to the second network entity, a second request to support the analytics service. Such a method performed or performable by the first network entity tends to improve continuity of the analytics service whilst the UE roams from the first wireless communication network to the second wireless communication network.

[0180] The analytics service may be associated with an AIML functionality provided by a first AIMLE server. The method may further comprise transmitting, to the first AIMLE server, a request for the AIML functionality for the UE to support the analytics service. The method may further comprise determining that the UE is to be served by the second wireless communication network. Determining that the UE is to be served by the second wireless communication network may comprise receiving an indication that the UE is expected to be served by the second wireless communication network.

[0181] Determining the second network entity for supporting the analytics service whilst the UE is being served by the second wireless communication network may comprise transmitting, to an ML repository, a request for context information for one or more available analytics server(s) in the second wireless communication network. The context information may comprise at least one of: a capability; a coverage; a load status; an energy status; an expected delay for access; an expected latency for access; vendor compatibility; cost for task migration; and a weighting factor.

[0182] The method may further comprise selecting, based on the context information, the second network entity from the one or more available analytics server(s) in the second wireless communication network. The method may further comprise receiving, from the second network entity, a notification of roaming of the UE to the second wirelessDocket No. SMM920250066-GR-NPcommunication network. The method may further comprise relaying an output from the second network entity to the analytics consumer.

[0183] Determining the second network entity for supporting the analytics service whilst the UE is being served by the second wireless communication network may comprise transmitting, to the UE, a request to select an analytics server from one or more available analytics server(s) in the second wireless communication network based on the context information for the one or more available analytics server(s) in the second wireless communication network.

[0184] Determining the second network entity for supporting the analytics service whilst the UE is being served by the second wireless communication network may comprise receiving, from the UE, an indication that the second network entity has been selected. The method may further comprise transmitting, to the second network entity, configuration information for the analytics service.

[0185] There is further provided performed or performable by a user equipment, UE, the method comprising: determining to support a first network entity with an analytics service, wherein the UE is served by a first wireless communication network; determining that the UE is to be served by a second wireless communication network; and determining a second network entity for supporting the analytics service whilst the UE is being served by the second wireless communication network. Such a method performed or performable by the UE tends to improve continuity of the analytics service whilst the UE roams from the first wireless communication network to the second wireless communication network.

[0186] The method may further comprise transmitting, to the first network entity, an indication that the UE is to be served by the second wireless communication network. The method may further comprise receiving, from the first network entity, a request to select an analytics server from one or more available analytics server(s) in the second wireless communication network based on the context information for the one or more available analytics server(s) in the second wireless communication network. Determining the second network entity for supporting the analytics service whilst the UE is being served by the second wireless communication network may comprise selecting the second network entity from the one or more available analytics server(s) in the second wireless communicationDocket No. SMM920250066-GR-NPnetwork based on the context information for the one or more available analytics server(s) in the second wireless communication network. The method may further comprise transmitting, to the first network entity, an indication that the second network entity has been selected. The method may further comprise connecting to the second network entity.

[0187] Examples described herein may relate to supporting ML-enabled analytics service continuity while one or more UEs involved in the analytics / AIML service are roaming to a different PLMN (e.g., from a first PLMN to a second PLMN). The ADAES / AIMLE server may be expected to be deployed only for a given area covered by the first PLMN (e.g., at MNO or in a trusted MNO domain as trusted AF).

[0188] Some examples described herein relate to a mechanism for supporting the ML- enabled analytics capability in scenarios where the ML model training / inference happens at the UE ( which may comprise an AIMLE client and / or ADAE client) for an analytics task which is handled or coordinated by the AD AES, while the UE is expected to move to an area served by another PLMN (e.g., the second PLMN). Previous solutions do not consider the roaming aspects, and assume that the analytics service terminates (VAL session terminates) when the UE is roaming. Some examples described herein relate to a server-enabled service continuity for VAL session analytics. Some examples described herein relate to a client-enabled service continuity for VAL session analytics

[0189] There is further provided a method for enabling an analytics functionality involving one or more UEs, wherein the one or more UEs are in the process of roaming from a fist network to a second network, the method comprising: obtaining a request for providing, and providing an analytics functionality, wherein the requirement indicates the functionality operation in a plurality of service areas, wherein the plurality of service areas are partly covered by the first and second network; detecting an expected / predicted mobility of the one or more UEs to an area covered by the second network; determining the adaption of at least one parameter of the analytics functionality based on the UE mobility and the analytics request, while the analytics functionality is operating; and executing the adaption of the at least one parameters of the analytics functionality while the analytics functionality is operating.Docket No. SMM920250066-GR-NP

[0190] The process of roaming may comprise an expected or predicted mobility of the one or more UEs to a second network whilst having an ongoing VAL session. The analytics functionality may be an ML enabled ADAE functionality. The functionality may comprise an ML model training and / or inference functionality.

[0191] The one or more UE may be served by an ML server entity at a first network. The ML server may be an AIMLE server connected to the first network. The one or more UE may be served by an analytics server entity at a first network. The analytics server may be an ADAE server connected to the first network.

[0192] The first and / or the second network may be a PLMN or NPN. The request may comprise a subscription for receiving monitoring related to UE mobility.

[0193] Detecting the event may comprise identifying an expected or predicted mobility to a geographical or topological area, where this area is covered by the second network. Adaption of at least one parameter may involve: adapting the analytics anchor / server, adapting the endpoint for collecting analytics from the at least one UE, adapting the communication means / API for providing the analytics functionality, and / or adapting a performance / KPI / accuracy of the analytics functionality.

[0194] Adaption of at least one parameter may involve: adapting the AIML training and / or inference functionality for the analytics functionality, adapting the endpoint of consuming AIML training and / or inference functionality for the analytics functionality, adapting the communication means / API for providing the AIML training / inference functionality, and / or adapting a performance / KPI / accuracy of the AIML training / inference functionality.

[0195] Executing may comprise sending to the UE, sending to the consumer, and / or sending to the target analytics functionality. Executing may comprise signalling among AIMLE servers, AIMLE to the analytics server.

[0196] It should be noted that the method described herein describes a possible implementation, and that the operations and the steps may be rearranged or otherwise modified and that other implementations are possible.Docket No. SMM920250066-GR-NP

[0197] The description herein is provided to enable a person having ordinary skill in the art to make or use the disclosure. Various modifications to the disclosure will be apparent to a person having ordinary skill in the art, and the generic principles defined herein may be applied to other variations without departing from the scope of the disclosure. Thus, the disclosure is not limited to the examples and designs described herein but is to be accorded the broadest scope consistent with the principles and novel features disclosed herein.Docket No. SMM920250066-GR-NP

Claims

CLAIMSWhat is claimed is:

1. A first network entity for wireless communication, comprising: at least one memory; and at least one processor coupled with the at least one memory and configured to cause the first network entity to: receive a first request to support an analytics service associated with a user equipment, UE, served by a first wireless communication network; determine a second network entity to support the analytics service whilst the UE is being served by the second wireless communication network; and transmit, to the second network entity, a second request to support the analytics service.

2. The first network entity of claim 1, wherein the analytics service is associated with an artificial intelligence machine learning, AIML, functionality provided by a first artificial intelligence machine learning enablement, AIMLE, server.

3. The first network entity of claim 1 or claim 2, wherein the at least one processor is further configured to cause the first network entity to: determine that the UE is to be served by the second wireless communication network.

4. The first network entity of claim 3, wherein to determine that the UE is to be served by the second wireless communication network, the at least one processor is further configured to cause the first network entity to: receive an indication that the UE is expected to be served by the second wireless communication network.

5. The first network entity of any one of claims 1 to 4, wherein to determine the second network entity for supporting the analytics service whilst the UE is being served byDocket No. SMM920250066-GR-NPthe second wireless communication network, the at least one processor is further configured to cause the first network entity to: transmit, to an machine learning, ML repository, a request for context information for one or more available analytics server(s) in the second wireless communication network.

6. The first network entity of claim 5, wherein the context information comprises at least one of: a capability; a coverage; a load status; an energy status; an expected delay for access; an expected latency for access; vendor compatibility; cost for task migration; and a weighting factor.

7. The first network entity of claim 5 or claim 6, wherein the at least one processor is further configured to cause the first network entity to: select, based on context information, the second network entity from the one or more available analytics server(s) in the second wireless communication network.

8. The first network entity of any one of claims 1 to 7, wherein the at least one processor is further configured to cause the first network entity to: receive, from the second network entity, a notification of roaming of the UE to the second wireless communication network.

9. The first network entity of any one of claims 1 to 8, wherein the at least one processor is further configured to cause the first network entity to: relay an output from the second network entity to an analytics consumer.Docket No. SMM920250066-GR-NP10. The first network entity of any one of claims 1 to 9, wherein to determine the second network entity for supporting the analytics service whilst the UE is being served by the second wireless communication network, the at least one processor is further configured to cause the first network entity to: transmit, to the UE, a request to select an analytics server from one or more available analytics server(s) in the second wireless communication network based on context information for the one or more available analytics server(s) in the second wireless communication network.

11. The first network entity of any one of claims 1 to 10, wherein to determine the second network entity for supporting the analytics service whilst the UE is being served by the second wireless communication network, the at least one processor is further configured to cause the first network entity to: receive, from the UE, an indication that the second network entity has been selected.

12. The first network entity of any one of claims 1 to 11, wherein the at least one processor is further configured to cause the first network entity to: transmit, to the second network entity, configuration information for the analytics service.

13. A method performed or performable by a first network entity, the method comprising: receiving a first request to support an analytics service associated with a user equipment, UE, served by a first wireless communication network; determining a second network entity to support the analytics service whilst the UE is being served by the second wireless communication network; and transmitting, to the second network entity, a second request to support the analytics service.Docket No. SMM920250066-GR-NP14. The method of claim 13, wherein the analytics service is associated with an artificial intelligence machine learning, AIML, functionality provided by a first artificial intelligence machine learning enablement, AIMLE, server.

15. The method of claim 13 or claim 14, further comprising: determining that the UE is to be served by the second wireless communication network.Docket No. SMM920250066-GR-NP

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