Relay node registration

Abstract

Various aspects of the present disclosure relate to a network entity for wireless communication, comprising at least one memory and at least one processor. The processor is configured to cause the network entity to: receive a registration request for registering at least one application layer entity for a machine learning, ML, model operation, the ML model operation being operated among a plurality of artificial intelligence, AI / ML endpoints, the request comprising a requirement related to the at least one application layer entity for acting as an intermediate node for undertaking part of the ML model operation; authorize the at least one application entity for acting as the intermediate node; and store at least one parameter related to a capability of the at least one application layer entity to serve as the intermediate node for the ML model operation.

Description

RELAY NODE REGISTRATIONTECHNICAL FIELD

[0001] The present disclosure relates to wireless communications, and more specifically to supporting relay node registration in split model learning operations.BACKGROUND

[0002] A wireless communications system may include one or multiple network communication devices, such as base stations, which may support 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, asAttorney Docket No. PC934667WOused 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] Some implementations of the method and apparatuses described herein may further include a network entity for wireless communication, comprising at least one memory; and at least one processor coupled with the at least one memory. The at least one processor is configured to cause the network entity to receive a registration request for registering at least one application layer entity for a machine learning, ML, model operation, the ML model operation being operated among a plurality of artificial intelligence, AI / ML endpoints, the request comprising a requirement related to the at least one application layer entity for acting as an intermediate node for undertaking part of the ML model operation; authorize the at least one application layer entity for acting as the intermediate node; and store at least one parameter related to a capability of the at least one application layer entity to serve as the intermediate node for the ML model operation.

[0005] The split ML model operation may be a ML model lifecycle operation such as a ML model learning operation. Alternatively or additionally, the split ML model operation may comprise a split ML model operation such as a split ML model inference operation and / or a split ML model training operation. For example, the ML model operation may be a split ML model learning operation.

[0006] The application layer entity may comprise a device application.

[0007] The device application may comprise at least one of an AI / ML enablement,AIMLE, client and a vertical application layer, VAL, client.

[0008] The application layer entity may comprise a server application.

[0009] The server application may comprise at least one of a VAL server, an AIMLE server, an edge server, an edge platform service, a multi-access edge computing, MEC, application, or a combination thereof.Attorney Docket No. PC934667WO

[0010] The server application may comprise an AIMLE server and wherein the processor is further configured to cause the network entity to store the association of the AIMLE server with a given candidate role for the ML model operation.

[0011] The requirement may comprise an indication that the registration is applicable to a ML model operation, and / or in particular a split ML model operation.

[0012] The intermediate node may be at least one of: an amplify and forward relay node; a store and forward relay node; and an application layer relay node.

[0013] The intermediate node may be configured to process, on behalf of one of the AI / ML endpoints, at least one cut and / or layer of the ML model operation, which may for example be a split ML model operation..

[0014] The requirement may indicate information regarding a role and / or capability of the at least one application layer entity, the information comprising at least one of: a supported role for the application layer entity as intermediate node; one or more permissions and / or restrictions for the application layer entity acting as intermediate node; at least one policy regarding the utilization of the at least one application layer entity as intermediate node; and one or more criteria regarding mobility of the application layer entity.

[0015] Said one or more permissions and / or restrictions may comprise one or more of: a vendor compatibility permission and / or restriction; an access type permission and / or restriction; a radio access technology, RAT, permission and / or restriction; and a compute limitation permission and / or restriction.

[0016] Said at least one policy may comprise one or more VAL policies relating to the selection of a VAL UE as relay and / or intermediate node, said one or more VAL policies comprising at least one of: a compute policy; a load policy; an energy constraint policy; and an energy requirement policy.

[0017] The requirement may comprise an indication of a time validity for an availability of the at least one application layer entity.Attorney Docket No. PC934667WO

[0018] The requirement may comprise at least one of: a coverage indication for an availability of the at least one application layer entity, wherein the coverage indication indicates a geographical area of interest; a service area of interest; and a topological area of interest.

[0019] The requirement may comprise information indicating a topology and / or strategy of a split of the ML model.

[0020] The requirement may comprise a preference and / or indication of being candidate intermediate node considering one or more ML pipelines as part of the ML model operation, for example a split ML model operation.

[0021] The processor may be configured to cause the network entity to perform the storing the at least one parameter comprising: transmitting a storage request to a ML repository; and receiving a storage response from the ML repository.

[0022] The processor may be configured to cause the network entity to receive the registration request from a AIMLE client.

[0023] The AIMLE client may be implemented in a user equipment, UE.

[0024] Some implementations of the present a method performed by a network equipment in a telecommunications network. Such a method may comprise: receiving a registration request for registering at least one application layer entity for a machine learning, ML, model operation, the ML model operation being operated among a plurality of artificial intelligence, AI / ML endpoints, the request comprising a requirement related to the at least one application layer entity for acting as an intermediate node for undertaking part of the ML model operation; authorizing the at least one application layer entity for acting as the intermediate node; and storing at least one parameter related to a capability of the at least one application layer entity to serve as the intermediate node for the ML model operation.BRIEF DESCRIPTION OF THE DRAWINGSAttorney Docket No. PC934667WO

[0025] Figure 1 illustrates an example of a wireless communications system in accordance with aspects of the present disclosure.

[0026] Figure 2 illustrates a functional model according to an example.

[0027] Figure 3 depicts an example of ML model lifecycle enhancement.

[0028] Figures 4A and 4B illustrates implementation of split AI / ML.

[0029] Figure 5 illustrates examples of communication between Al apps on UEs and an Al server.

[0030] Figure 6 depicts an example of communication flow.

[0031] Figure 7 depicts an example of communication flow.

[0032] Figure 8 illustrates an example of a network equipment (NE) 800 in accordance with aspects of the present disclosure.

[0033] Figure 9 illustrate a flowcharts of method performed by a NE in accordance with aspects of the present disclosure.DETAILED DESCRIPTION

[0034] Split Learning is a type of machine learning (ML) operation which involves multiple artificial intelligence (AI) / ML endpoints taking part in an ML task or operation processing such as an inference operation. In certain scenarios in which Split Learning is performed within the context of a telecommunications network, the Split Learning operation may be supported by intermediate nodes, which may for example be relay nodes.

[0035] The present disclosure addresses the problem of how to enable a node at device or edge server side to register at an ML repository as a relay UE / edge node for supporting a split AI / ML operation. In particular, current systems do not consider the notion of a relay role when performing the registration.

[0036] This problem is addressed by providing a mechanism for registering a further entity (e.g. UE, edge function) to serve as relay for a split learning operation, andAttorney Docket No. PC934667WOparticularly a split AI / ML operation. Such a mechanism enhances the existing mechanisms for AI / ML Enablement (AIMLE) client and server registration to support also the registration of the capability to serve as intermediate / relay node. Effective split learning is thereby facilitated.

[0037] Thus, whereas in comparative systems the AIMLE client and AI / ML split operation node operation only refer to the source or destination AI / ML endpoint and not a further entity which supports relaying or performing some further splitting on behalf of the UE, the present disclosure provides such a further entity.

[0038] Aspects of the present disclosure are described in the context of a wireless communications system.

[0039] 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.

[0040] 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, anAttorney Docket No. PC934667WOeNodeB (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 signaling, transmit signaling) over a Uu interface.

[0041] 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.

[0042] 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.

[0043] 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, vehicle-to-everything (V2X) deployments, or cellular-V2X deployments, the communication link may be referred to as a sidelink. For example, a UE 104 may support wireless communication directly with another UE 104 over a PC5 interface.

[0044] 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 106Attorney Docket No. PC934667WOthrough 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).

[0045] 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 5G core (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.

[0046] 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). The PDU 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).

[0047] 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,Attorney Docket No. PC934667WOcarriers)) 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.

[0048] 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.

[0049] 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, each frame may have the same duration. In some implementations, each subframe of a frame may have the same duration.

[0050] 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 communicationsAttorney Docket No. PC934667WOsystem 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.

[0051] 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). In 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.

[0052] 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., atAttorney Docket No. PC934667WOleast 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.

[0053] Considering vertical-specific applications and edge applications as the major consumers of 3 GPP-provided data analytics and AI / ML support services, the AIMLE service plays 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; and on defining, at a SEAL layer, value-add support services for assisting AI / ML services provided by the VAL layer, while being complementary to AI / ML support solutions provided in other 3 GPP domains.

[0054] Figure 2 illustrates an example of an on-network functional model of AIML enablement. Figure 2 depicts a network comprising a UE 205 in communication with a 3GPP network system 210. The UE 205 comprises a vertical application layer (VAL) client 215 in a vertical application layer, and a AIMLE client 220 in a service enabler architecture layer (SEAL), communicating via AIML-C.

[0055] In the vertical application layer, the VAL client 215 communicates with the VAL server 225 over VAL-UU reference point. VAL-UU supports both unicast and multicast delivery modes.

[0056] The AIML enablement functional entities on the UE 205 and the server are grouped into AIMLE client(s) 220 and AIML enablement server(s) 230 respectively. The AIMLE client 220 communicates with the AIMLE server 230 over AIML-UU reference point. AIMLE server 230 is additionally in communication with an ML repository 235 via AIML-R.

[0057] The AIMLE server 230 is a newly defined SEAL server which includes a common set of services for comprehensive enablement of AIML functionality. In this example, the AIMLE server 230 defines the following group of capabilities:Support for application-layer ML model related aspects, including model retrieval, model training, model monitoring, model selection, model update and model storage / discovery.Attorney Docket No. PC934667WOAssistance in AI / ML task transfer and split AI / ML operations.Support horizontal federated learning (HFL) / vertical federated learning (VFL) operations, including FL member registration, FL grouping and FL-related events notification, VFL feature alignment, HFL training.Support for AIMLE client registration, discovery, participation and selection.

[0058] The AIMLE client 220 functional entity acts as the application client supporting AIMLE services.

[0059] The ML repository 235 is an entity that serves as: a registry for ML / FL members (application layer entities participating in an AI / ML operation) and as a repository for application layer ML model related information.

[0060] Figure 3 presents an example of ML model lifecycle enablement. One role of AIMLE is applicable to such ML model lifecycle enablement which provides assistance for use cases where an application service provider (ASP) / VAL layer aims to find and use other application entities to perform some ML operations (e.g. ML model inference) and AIMLE server as a mediator to accomplish that.

[0061] Figure 3 depicts AIMLE 305 in conjunction with VAL server(s) 310 (e.g. the VAL server(s) 225 of Figure 2). AIMLE 305 implements capabilities for ML model lifecycle enhancement 315, and VAL server(s) implements capabilities for ML model operational workflow 320.

[0062] By way of example, AIMLE can undertake:ML model related support capabilities such as model retrieval, discovery and storage (for example as described in procedures in clauses 8.2 and 8.11 of 3GPP TS 23.482).ML operation related support capabilities such as VFL / HFL and transfer learning (TL) enablement, Split AI / ML Operation support, Data management assistance, AI / ML task transfer, FL assistance in member grouping, registration and eventAttorney Docket No. PC934667WOnotification (for example as described in procedures in clauses 8.4, 8.6, 8.12, 8.14, 8.15-8.18 of 3GPP TS 23.482).AIMLE client related support capabilities, including AIMLE client registration, discovery, participation, monitoring, selection (for example as described in procedures in clauses 8.7-8.10, 8.13 of 3GPP TS 23.482).

[0063] An example of split AI / ML inference is depicted in Figure 4 A. An input image 405 such as an image of a tree is acquired by an end device 410. Partial inference is performed by the end device 410 and intermediate data is transmitted to network AI / ML endpoint 1 415. Network AI / ML endpoint 1 415 performs further partial inference and transmits further intermediate data to network AI / ML endpoint 2420. Network AI / ML endpoint 2420 performs a final partial inference and produces output 425, for example identifying the image as a “tree”.

[0064] Thus, the AI / ML model inference can be distributed into multiple parts according to the current task and environment (such as communications data rate, device resource, and server workload). For example, this allows the computation-intensive, energy-intensive parts to be offloaded to network endpoints 415, 420 (cloud or edge server), whilst leaving the privacy-sensitive and delay-sensitive parts at the end device 410. The device 410 can thus execute partial model inference and sends the intermediate data to a network endpoint 415, 420 where the remaining model inference is executed, and the inference results fed back to the device.

[0065] For AI / ML split operation, the enabler layer can provide support to negotiate splitting of the AI / ML operation and to discover required nodes to perform computationintensive, energy-intensive parts of AI / ML operations.

[0066] Further use cases have been identified for Split AI / ML operations, for:Split AI / ML operation between AI / ML endpoints for Al inference by leveraging direct device connection; andLocal AI / ML model split on factory robots.Attorney Docket No. PC934667WO

[0067] A relay UE may be utilized to support meeting the performance requirements in case of the source UE being in bad channel conditions or lack of resources. This relaying for the factory robot use cases could be due to the joint local processing at the robot side to minimize the end to end latency for the Al inference process.

[0068] Figure 4B depicts an example utilizing a relay UE. In this example, a first UE communicates with a second, relay, UE via a sidelink. The relay UE is connected to the first UE via a sidelink, and connected to the network via Uu. The first UE may perform calculation of e.g. layers 1 to 4, and the relay UE may perform calculation of e.g. layers 5 to 15 and transmit intermediate data via the network to the application server to calculate e.g. layers 16 to 24. In this example, the data rate on Uu need not be increased while the original UE’s computation load is offloaded.

[0069] As part of 3 GPP SA6 (AIMLE), split AI / ML operations have been introduced in 3GPP TS 23.482. The framework therein is for single AIMLE client-to-network interaction and does not consider multiple AIMLE clients collaborating in a split AI / ML scenario. If multiple AIMLE clients were to collaboratively execute split AI / ML operations within the same pipeline involving an Edge Server, the operation continuity may be disrupted if one client moves out of the service area during execution. Therefore, examples of the present disclosure are intended to improve the service continuity and optimization for split AIML operations involving multiple AIML clients.

[0070] In addition to the above multi-client scenario, emerging AI / ML applications require collaborative inference across multiple computing nodes (UE(s), edge(s), and cloud(s)) rather than a single operation. For example, in an autonomous driving or V2X scenario, a VAL UE (e.g. a connected vehicle) may only execute part of a model locally and rely on a roadside edge server or even a cloud server to complete the remaining inference. This is due to the vehicle’s limited computing resources and the need for low- latency results to ensure safe and efficient driving decisions.

[0071] In Release- 19, basic support for split ML model inference between an AIMLE client and an AIMLE server was introduced in 3GPP TS 23.482). However, those solutions assumed a relatively static allocation of inference tasks and did not fully explore more dynamic or collaborative inference involving multiple nodes. Therefore, examples of theAttorney Docket No. PC934667WOpresent disclosure are intended to enhance the AIML application enablement architecture and functions to support cloud-edge-device collaborative inference considering the conditions changes on these computing nodes (e.g. due to variations in edge server load).

[0072] Some use cases are promising for the Split AI / ML scenario involving multiple UEs, for example:Enhanced Smart City Surveillance with Mobility: Surveillance cameras, both stationary and mobile, capture video footage and process raw data on the cameras, sending subsequent layers to an edge server for immediate processing. AIML split operations enable real-time improved surveillance and threat detection for public safety through a shared pipeline that allows collaborative data processing. The pipeline is re-adjusted based on computing resource powers for the surveillance cameras. Pipeline re-adjustment is also performed as the mobile surveillance cameras change service areas. This results in efficient resource utilization, energy saving, and larger surveillance.Collaborative Autonomous Driving: A group of autonomous vehicles collaborate by sharing and processing data together. An example is vehicles utilizing shared Local Dynamic Maps (LDM) which are collaboratively updated and used by each vehicle locally. The raw data of each vehicle is processed locally and is sent for subsequent layers in the edge server for immediate processing. The shared AIML split operation enables real-time collaboration to optimize driving conditions (e.g., efficient routes) and reduces computational burden in the vehicles. Pipeline readjustment is performed based on the computing resource power of the participants. Pipeline-readjustment is also performed as the vehicles change service area during mobility. This results in a larger collaboration coverage.

[0073] Some further use cases are discussed with reference to Ligure 5, which depicts Al apps on UEs 505, 510 in communication with each other and with Al server 515. Ligure 5 depicts use cases UC1-UC4.UC1 : Pipeline among UEs in side-link for uncrewed aerial vehicle (UAV)-UAVC local inference, V2X see through driving (among cars).Attorney Docket No. PC934667WOUC2: Pipeline among UEs via the network. The use case can be for UAV-UAVC local inference, V2X see through driving (among cars). The network is used for V2N2V for NLoS scenarios or for keeping sensitive data at UE side, and to perform part of the processing.UC3: Multiple pipelines for parallel / federated splitting. One pipeline per UE, and binding of pipelines per service / task at the Al server / AIMLE.UC4: Pipeline UE to UE to NW (using relay): One or more pipelines, disco very / create and update moving part of pipeline (UE1 or UE2).

[0074] In a further use case, which is applicable for the relay / intermediate node scenario, the Relay / intermediate node is an edge node (edge application server (EAS), multi-access edge computing (MEC) app, Edge platform SaaS / PaaS), which is used closer to the UE to support some of the processing (some cut layers) to offload the processing from the UE side, while keeping some layers (e.g. corresponding to sensitive data) at the UE side.

[0075] In addition to the use cases described in SAI, 3GPP SA4 has also defined split AIML operation for media services in 3 GPP TR 26.927. In this document, some considerations for the split Al operation are described.

[0076] An AI / ML model may be splitable, meaning that it may be theoretically represented by several sequential sub-models separated by split points. In TR 26.927 the following assumptions are made:Each sub-model describes a unique part of the inference process.The combination of the inference of all sub-models is equivalent to the inference of the entire AI / ML model.Several split points, identifying the frontier between AI / ML sub-models, may be identified within an AI / ML model.Those split points are predefined and may be selected or re-selected dynamically to adapt to the changing conditions.Attorney Docket No. PC934667WO

[0077] In SA4, the assumptions were mainly considering a UE and NW endpoint, however a further extension to these options, could be to allow a further entity as relay or intermediate node to support processing part of the model.

[0078] The present disclosure aims to address several considerations relating to split AI / ML. The use cases supported in the AIML enablement layer, involving multiple AIMLE clients, edge AIMLE servers and central AIMLE servers requiring concurrent usage of the same split AI / ML operation pipeline, are defined. Aspects of the disclosure provides ways for AIML enablement layer to support split AI / ML operations involving multiple AIMLE clients, edge AIMLE servers and central AIMLE servers. Aspects of the disclosure provide for the AIMLE enablement layer to support service continuity during split AIML operation involving multiple AIMLE clients. Aspects of the disclosure provide for supporting dynamic inference partitioning and coordinating across AIMLE clients, edge AIMLE servers and central AIMLE servers in AIMLE layer.

[0079] As stated, one consideration is the use of multiple UEs (via their corresponding AIMLE clients), and this can be for example for adding these UEs as relay UEs or intermediate nodes which perform part of the processing between the UE and the Server side. In this scenario, one problem to be solved is how to enable a node at device or edge server side to register as a relay UE / edge node for supporting a split AI / ML operation.

[0080] The present disclosure provides a mechanism for registering a further entity (UE, edge function) to serve as relay for a split learning operation, and particularly a split AI / ML operation.

[0081] Figure 6 depicts a communication flow between an AIMLE client 220, AIMLE server 230 and ML repository 235, according to an example of registration of an entity as relay. It will be understood that this is an example, and that in other examples steps may be removed, or added, or performed by different entities.

[0082] In this example, the registration of the entity acting as relay is performed by extending the AIMLE client 220 registration procedure as described in TS 23.482. Initially, the procedure for AIMLE client 220 registration is meant to accommodate scenarios where the AIMLE client 220 registers to be part of an AIML operation (training, inference etc.).Attorney Docket No. PC934667WOWith this extension the AIMLE client 220 is registering to be able to act as Relay UE for the Split Learning operation.

[0083] Prior to the performance of the method of Figure 6, two pre-conditions are to be met. Firstly, the AIMLE client 220 has been pre-configured or has discovered the address (e.g., URI) of the AIMLE server 230. Secondly, the AIMLE client 220 has been preconfigured with an AIMLE client profile.

[0084] At 1 , the AIMLE client 220 sends an AIMLE client registration request to the AIMLE server 230. The registration request may include information as described in table 1 below.Table 1: AIMLE client registration requestTable 2: AIMLE client profileAttorney Docket No. PC934667WOAttorney Docket No. PC934667WO

[0085] The AIMLE client 220 indicates in the registration request its AI / ML capabilities such as supported ML model types and supported AI / ML operations, supported AIMLE client task capabilities with compute and task performance capabilities to assist with performing AIMLE client discovery and AIMLE client selection.

[0086] Lor the case in which the service relates to an AI / ML split learning operation (or split AI / ML training or inference service), the AIMLE client 220 may request to register as relay / intermediate node for the service ID. Such indication can be part of the AIMLE client profile, or as separate IE in the AIMLE client registration request.

[0087] The AIMLE client 220 may also indicate the policies / criteria in which the VAL UE 205 can act as candidate relay / intermediate node. This includes the time and area of validity, a list of supported VAL services / servers or vendors which are part of the Split AIML operation, compute and energy requirements and constraints for the utilization of the VAL UE 205 (e.g. only use as relay if battery is higher than 50% or in low load scenarios or in good channel conditions (e.g. cell center)). This may also include criteria for the UE mobility or relative mobility, i.e., UEs that move towards the same direction with a speed that allows connectivity could be considered as candidate relay nodes.

[0088] It should be noted that the use of an AIMLE client as an intermediate node in the Split AIML pipeline can be in certain examples a new AIMLE service (or new “supported service”).

[0089] At 2, the AIMLE server 230 validates the registration request and performs an authentication and authorization check to determine if the AIMLE client 220 is permitted toAttorney Docket No. PC934667WOregister to the AIMLE server 230 and participate in AI / ML operations as relay / intermediate node.

[0090] At 3, upon successful authorization, the AIMLE server 230 saves the context of the AIMLE client registration in the ML repository 235. The ML repository 235 also stores the association of the AIMLE server 230 with a certain candidate role for a split learning operation. The storage at the repository maps the AIMLE client ID with an ML task / service type (or list of services which support split learning), a role indication and a role type (relay, intermediate processing node).

[0091] At 4, the AIMLE server 230 returns an AIMLE client registration response to the AIMLE client 220 with the status of the request.

[0092] Eigure 7 depicts a communication flow between a VAL server 225 and an AIMLE server 230, according to a further example of registration of an entity as relay. It will be understood that this is an example, and that in other examples steps may be removed, or added, or performed by different entities.

[0093] In this example, the registration of the entity acting as relay is performed by extending the Split Operation node registration procedure as described in TS 23.482.

[0094] Prior to performance of the communication flow of Figure 7, two pre-conditions are to be met:1. The VAL server has received information (e.g. URI, IP address) related to the AIMLE Server; and2. The VAL server has received security credentials authorizing it to communicate with the AIMLE server.

[0095] At 1, the VAL server 225 sends a split operation node register request to the AIMLE server 230 to indicate the split operation capabilities of the VAL server 225 (or from another entity connected with the VAL server 225 indirectly, such as a VAL client 215 or an EAS). The request may include information defined in table 3 below, including the node information related to the use as relay or intermediate node.Table 3: split operation node register requestAttorney Docket No. PC934667WO

[0096] At 2, upon receiving the request from the VAL server 225, the AIMLE server 230 validates if the requestor is authorized for the request, the AIMLE server 230 checks if the node to be registered is a VAL UE 205 is capable and available to act as relay / intermediate node given also its radio and computational / energy conditions, and stores the registration information (including the role of the registered node (VAL server 225 or VAL UE 205) with respect to the split model operation).

[0097] At 3, the AIMLE server 230 checks if the node to be registered is capable and available to act as relay / intermediate node given also its radio and computational / energyAttorney Docket No. PC934667WOconditions. Such checking may include querying from the network or ML repository or AIMLE client side the status of the VAL UE.

[0098] At 3, the AIMLE server 230 sends a split operation node register response to the requestor. This message may comprise information as set out in table 4.Table 4: split operation node register response

[0099] Figure 8 illustrates an example of a NE 800 in accordance with aspects of the present disclosure. The NE 800 may include a processor 802, a memory 804, a controller 806, and a transceiver 808. The processor 802, the memory 804, the controller 806, or the transceiver 808, 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.

[0100] The processor 802, the memory 804, the controller 806, or the transceiver 808, 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.Attorney Docket No. PC934667WO

[0101] The processor 802 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 802 may be configured to operate the memory 804. In some other implementations, the memory 804 may be integrated into the processor 802. The processor 802 may be configured to execute computer-readable instructions stored in the memory 804 to cause the NE 800 to perform various functions of the present disclosure.

[0102] The memory 804 may include volatile or non-volatile memory. The memory 804 may store computer-readable, computer-executable code including instructions when executed by the processor 802 cause the NE 800 to perform various functions described herein. The code may be stored in a non-transitory computer-readable medium such the memory 804 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.

[0103] In some implementations, the processor 802 and the memory 804 coupled with the processor 802 may be configured to cause the NE 800 to perform one or more of the functions described herein (e.g., executing, by the processor 802, instructions stored in the memory 804). For example, the processor 802 may support wireless communication at the NE 800 in accordance with examples as disclosed herein. For example, the processor 802 may be configured to cause the NE 800 to receive a registration request for registering at least one application layer entity for a ML model operation, the ML model operation being operated among a plurality AI / ML endpoints, the request comprising a requirement related to the at least one application layer entity for acting as an intermediate node for undertaking part of the ML model operation; authorize the at least one application layer entity for acting as the intermediate node; and store at least one parameter related to a capability of the at least one application layer entity to serve as the intermediate node for the ML model operation.

[0104] The controller 806 may manage input and output signals for the NE 800. The controller 806 may also manage peripherals not integrated into the NE 800. In someAttorney Docket No. PC934667WOimplementations, the controller 806 may utilize an operating system such as iOS®, ANDROID®, WINDOWS®, or other operating systems. In some implementations, the controller 806 may be implemented as part of the processor 802.

[0105] In some implementations, the NE 800 may include at least one transceiver 808. In some other implementations, the NE 800 may have more than one transceiver 808. The transceiver 808 may represent a wireless transceiver. The transceiver 808 may include one or more receiver chains 810, one or more transmitter chains 812, or a combination thereof.

[0106] A receiver chain 810 may be configured to receive signals (e.g., control information, data, packets) over a wireless medium. For example, the receiver chain 810 may include one or more antennas for receive the signal over the air or wireless medium. The receiver chain 810 may include at least one amplifier (e.g., a low-noise amplifier (LN A)) configured to amplify the received signal. The receiver chain 810 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 810 may include at least one decoder for decoding the processing the demodulated signal to receive the transmitted data.

[0107] A transmitter chain 812 may be configured to generate and transmit signals (e.g., control information, data, packets). The transmitter chain 812 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 812 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 812 may also include one or more antennas for transmitting the amplified signal into the air or wireless medium.

[0108] Figure 9 illustrates a flowchart of a method in accordance with aspects of the present disclosure. The operations of the method may be implemented by a NE as described herein, for example the NE of Figure 8. In some implementations, the NE may execute a setAttorney Docket No. PC934667WOof instructions to control the function elements of the NE to perform the described functions.

[0109] At 902, the method comprises receiving a registration request for registering at least one application layer entity for a ML model operation, the ML model operation being operated among a plurality of AI / ML endpoints, the request comprising a requirement related to the at least one application layer entity for acting as an intermediate node for undertaking part of the ML model operation.

[0110] At 904 the method comprises authorizing the at least one application layer entity for acting as the intermediate node.

[0111] At 906 the method comprises storing at least one parameter related to a capability of the at least one application layer entity to serve as the intermediate node for the ML model operation.

[0112] 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.

[0113] 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.Attorney Docket No. PC934667WO

Claims

27CLAIMSWhat is claimed is:

1. A 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 network entity to: receive a registration request for registering at least one application layer entity for a machine learning, ML, model operation, the ML model operation being operated among a plurality of artificial intelligence, AI / ML endpoints, the request comprising a requirement related to the at least one application layer entity for acting as an intermediate node for undertaking part of the ML model operation; authorize the at least one application entity for acting as the intermediate node; and store at least one parameter related to a capability of the at least one application layer entity to serve as the intermediate node for the ML model operation.

2. The network entity of claim 1, wherein the ML model operation comprises a split ML model operation, said split ML model operation optionally comprising at least one of a split ML model inference operation and / or a split ML model training operation.

3. The network entity of claim 1 or claim 2, wherein the application layer entity comprises a device application and / or a server application.

4. The network entity of claim 3, wherein the device application comprises at least one of an AI / ML enablement, AIMLE, client and a vertical application layer, VAL, client5. The network entity of claim 3, wherein the server application comprises at least one of a VAL server, an AIMLE server, an edge server, an edge platform service, a multiaccess edge computing, MEC, application, or a combination thereof.Attorney Docket No. PC934667WO6. The network entity of any preceding claim, wherein the server application comprises an AIMLE server and wherein the processor is further configured to cause the network entity to store the association of the AIMLE server with a given candidate role for the ML model operation.

7. The network entity of any preceding claim, wherein the requirement comprises an indication that the registration is applicable to a ML model operation.

8. The network entity of any preceding claim, wherein the intermediate node is at least one of: an amplify and forward relay node; a store and forward relay node; and an application layer relay node.

9. The network entity of any preceding claim, wherein the intermediate node is configured to process, on behalf of one of the AI / ML endpoints, at least one cut and / or layer of the ML model operation.

10. The network entity of any preceding claim, wherein the requirement indicates information regarding a role and / or capability of the at least one application layer entity, the information comprising at least one of: a supported role for the application layer entity as intermediate node; one or more permissions and / or restrictions for the application layer entity acting as intermediate node; at least one policy regarding the utilization of the at least one application layer entity as intermediate node; and one or more criteria regarding mobility of the application layer entity.

11. The network entity of claim 10, wherein said one or more permissions and / or restrictions comprises one or more of:Attorney Docket No. PC934667WOa vendor compatibility permission and / or restriction; an access type permission and / or restriction; a radio access technology, RAT, permission and / or restriction; and a compute limitation permission and / or restriction.

12. The network entity of claim 10 or claim 11, wherein said at least one policy comprises one or more VAL policies relating to the selection of a VAL UE as relay and / or intermediate node, said one or more VAL policies comprising at least one of: a compute policy; a load policy; an energy constraint policy; and an energy requirement policy.

13. The network entity of any preceding claim, wherein the requirement comprises an indication of a time validity for an availability of the at least one application layer entity.

14. The network entity of any preceding claim, wherein the requirement comprises at least one of: a coverage indication for an availability of the at least one application layer entity, wherein the coverage indication indicates a geographical area of interest; a service area of interest; and a topological area of interest.

15. The network entity of any preceding claim, wherein the requirement comprises information indicating a topology and / or strategy of a split of the ML model.

16. The network entity of any preceding claim, wherein the requirement comprises a preference and / or indication of being candidate intermediate node considering one or more ML pipelines as part of the ML model operation.Attorney Docket No. PC934667WO17. The network entity of any preceding claim, wherein the processor is configured to cause the network entity to perform the storing the at least one parameter comprising: transmitting a storage request to a ML repository; and receiving a storage response from the ML repository.

18. The network entity of any preceding claim, wherein the processor is configured to cause the network entity to receive the registration request from a AIMLE client.

19. The network entity of any preceding claim, wherein the AIMLE client is implemented in a user equipment, UE.

20. A method, performed by a network equipment in a telecommunications network, comprising: receiving a registration request for registering at least one application layer entity for a machine learning, ML, model operation, the ML model operation being operated among a plurality of artificial intelligence, AI / ML endpoints, the request comprising a requirement related to the at least one application layer entity for acting as an intermediate node for undertaking part of the ML model operation; authorizing the at least one application layer entity for acting as the intermediate node; and storing at least one parameter related to a capability of the at least one application layer entity to serve as the intermediate node for the ML model operation.Attorney Docket No. PC934667WO

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