User equipment energy analytics 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 (802), from a second network entity, a first message indicating an analytics request associated with an energy capability of at least one UE; determine (804), in response to receiving the analytics request and based on an energy data, analytics associated with the energy capability of the at least one UE; and transmit (806), to the second network entity, a second message indicating the analytics associated with the energy capability of the at least one UE.

Description

USER EQUIPMENT ENERGY ANALYTICS IN A WIRELESS COMMUNICATION SYSTEMTECHNICAL FIELD

[0001] The present disclosure relates generally to wireless communication, including the user equipment (UE) energy analytics in a wireless communication system.BACKGROUND

[0002] A wireless communications system may include one or multiple network communication devices, which may be otherwise known as network equipment (NE) supporting wireless communications for one or multiple user communication devices, which may be otherwise known as 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 aDocket No. SMM920250103-GR-NPcondition B without departing from the scope of the present disclosure. In other words, as used 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] 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, from a second network entity, a first message indicating an analytics request associated with an energy capability of at least one UE; determine, in response to receiving the analytics request and based at least on an energy data, analytics associated with the energy capability of the at least one UE; and transmit, to the second network entity, a second message indicating the analytics associated with the energy capability of the at least one UE.

[0005] A second network entity for wireless communication is described. The second network entity may be configured to, capable of, or operable to perform one or more operations as described herein. For example, the second 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 second network entity to: transmit, to a first network entity, a first message indicating an analytics request associated with an energy capability of at least one UE; and receive, from the first network entity, a second message indicating an analytics associated with the energy capability of the at least one UE.

[0006] A method performed by a first network entity is described. The method may comprise: receiving, from a second network entity, a first message indicating an analytics request associated with an energy capability of at least one UE; determining, in response to receiving the analytics request and based on an energy data, analytics associated with the energy capability of the at least one UE; and transmitting, to the second network entity, a second message indicating the analytics associated with the energy capability of the at least one UE.Docket No. SMM920250103-GR-NP

[0007] A method performed by a second network entity is described. The method may comprise: transmitting, to a first network entity, a first message indicating an analytics request associated with an energy capability of at least one UE; and receiving, from the first network entity, a second message indicating the analytics associated with the energy capability of the at least one UE.BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 illustrates an example of a wireless communications system in accordance with aspects of the present disclosure.

[0009] Figure 2 illustrates an example of a process flow that provides application data analytics enablement service (AD AES) support for application layer AI / ML member capability analytics in accordance with aspects of the present disclosure.

[0010] Figure 3 illustrates a further example of a process flow that provides AD AES support for application layer AI / ML member capability analytics in accordance with aspects of the present disclosure.

[0011] Figure 4 illustrates an example of a process flow that provides AD AES support for DN energy analytics in accordance with aspects of the present disclosure.

[0012] Figure 5 illustrates an example of a UE 500 in accordance with aspects of the present disclosure.

[0013] Figure 6 illustrates an example of a processor 600 in accordance with aspects of the present disclosure.

[0014] Figure 7 illustrates an example of a NE 700 in accordance with aspects of the present disclosure.

[0015] Figure 8 illustrates a flowchart of a method 800 performed by a NE in accordance with aspects of the present disclosure.

[0016] Figure 9 illustrates a flowchart of a method 900 performed by a NE in accordance with aspects of the present disclosure.Docket No. SMM920250103-GR-NPDETAILED DESCRIPTION

[0017] A wireless communication system, including one or more UE and NE, will consume energy for a number of different tasks. The tasks performed by the one or more UE and NE may include wireless communication between UE and NE of the one or more UE and NE, and / or may include processing tasks performed by the one or more UE and NE. The amount of energy or energy budget that is available to the wireless communication system, or to the one or more UE and NE, may be finite and available for a given time period, for a given service, for a given session or for a given task. As the demand for wireless communication increases, energy efficiency and energy saving become increasingly important.

[0018] In the 5G system (5GS), energy efficiency and energy saving continue to be critical considerations, such considerations likely to extend into future generations of wireless communication systems (such as 6G). The increasing use of artificial intelligence (Al) / machine learning (ML) in wireless communication systems, by both NE and UE, is a growing concern for vertical service consumers, especially as AI / ML models become more complex and data intensive. Increasingly, service consumers are requiring the reporting of energy consumption for AI / ML model tasks, such as training and inference activities, to allow for energy expenditure to be calculated.

[0019] Analytics from the application layer in the 5GS, enabled through the AD AES, can be used to predict or forecast the energy consumption or efficiency for AI / ML model tasks. For AI / ML tasks using federated learning (FL) and / or for AI / ML model task transfers, it is important that a UE performing part or all of the AI / ML model task, is qualified for the given task. The qualification and selection of a UE for an AI / ML model task may consider a number of factors; however, the qualification and selection of a UE does not currently consider analytics pertaining to energy consumption and / or energy efficiency.

[0020] The present disclosure introduces a first network entity that can acquire energy data of a UE and determine energy related analytics, based on the energy data, in relation to whether that UE is suitable to act as an AI / ML member in an application layer AI / ML task. Accordingly, a UE can be selected if the AI / ML task is within the scope of its current orDocket No. SMM920250103-GR-NPpredicted energy budget. This tends to avoid assigning tasks to already energy depleted UEs, risking unexpected disconnections, task interruptions and / or device failure. Moreover, a UE can be selected in a manner that allows a wireless communication system to distribute workload efficiently across the system / network or in a manner that meets sustainability or quality of service requirements.

[0021] Aspects of the present disclosure are described in the context of a wireless communications system.

[0022] 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.

[0023] 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 orDocket No. SMM920250103-GR-NPwired connection. For example, an NE 102 and a UE 104 may perform wireless communication (e.g., receive signalling, transmit signalling) over a Uu interface.

[0024] 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.

[0025] 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.

[0026] A UE 104 may be able to support wireless communication directly with other UEs 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.

[0027] 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 otherDocket No. SMM920250103-GR-NPimplementations, 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).

[0028] 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.

[0029] 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).

[0030] 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.Docket No. SMM920250103-GR-NPFor 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 5G 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.

[0031] 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., / t=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., / t=0) associated with the first subcarrier spacing (e.g., 15 kHz) may utilize one slot per subframe. A second numerology (e.g., / / =1) may be associated with a second subcarrier spacing (e.g., 30 kHz) and a normal cyclic prefix. A third numerology (e.g., g=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., / t=3) may be associated with a fourth subcarrier spacing (e.g., 120 kHz) and a normal cyclic prefix. A fifth numerology (e.g., / t=4) may be associated with a fifth subcarrier spacing (e.g., 240 kHz) and a normal cyclic prefix.

[0032] 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 l 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.

[0033] 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., / t=0, / t=l, =2, jtz=3, =4) associated with respective subcarrier spacings of 15 kHz, 30 kHz, 60Docket No. SMM920250103-GR-NPkHz, 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., / t=0) associated with a first subcarrier spacing (e.g., 15 kHz) may be used interchangeably between subframes and slots.

[0034] 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.

[0035] 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., / t=0), which includes 15 kHz subcarrier spacing; a second numerology (e.g., / / =1), which includes 30 kHz subcarrier spacing; and a third numerology (e.g., g=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.,Docket No. SMM920250103-GR-NPjU=2), which includes 60 kHz subcarrier spacing; and a fourth numerology (e.g., / z=3), which includes 120 kHz subcarrier spacing.

[0036] The third -generation partnership project (3 GPP) service and system aspects (SA) working group 6 is the application enablement and critical communications applications group for vertical markets. The main objective of SA6 is to provide application layer architecture specifications for 3 GPP verticals, including architecture requirements and functional architecture for supporting the integration of verticals to 3GPP systems. With respect to application enablement, the main focus is on enablers for vertical applications (e.g., automotive) and service frameworks (e.g. Common application programming interface (API) Framework, Service Enabler Architecture Layer, Edge Application enablement).

[0037] The AD AES is described in the 3GPP Technical Report TR 23.700-36 titled “Study on Application Data Analytics Enablement Service”. The AD AES is a new enablement service (which can be part of the service enabler architecture layer (SEAL)). The 3GPP Technical Report TR 23.700-36 discusses new potential application data analytics services (statistics / predictions) to optimize the application service operation by notifying the application specific layer, and potentially the 5GS, for expected / predicted application service parameters changes considering both on-network and off-network deployments (e.g., related to application quality of service (QoS) parameters).

[0038] One newly defined SEAL service which was defined in 3GPP Rel-19 is the AIML Enablement (AIMLE) service as described in the 3GPP Specification TS 23.482 titled “Functional architecture and information flows for AIML Enablement Service”.

[0039] As part of the AIMLE service, an AIMLE server is introduced. The AIMLE server is a newly defined SEAL server which comprises of a common set of services for comprehensive enablement of AIML functionality. The AIMLE server defines a group of capabilities as will now be briefly introduced. A capability of the AIMLE server is support for application-layer ML model related aspects, including model retrieval, model training, model monitoring, model selection, model update and model storage / discovery. A further capability of the AIMLE server is assistance in AI / ML task transfer and split AI / ML operations. A further capability of the AIMLE server is support for horizontal federatedDocket No. SMM920250103-GR-NPlearning (HFL) / vertical federated learning (VFL) operations, including FL member registration, FL grouping and FL-related events notification, VFL feature alignment, and HFL training. A further capability of the AIMLE server is support for AIMLE client registration, discovery, participation and selection.

[0040] As part of the AIMLE service, an AIMLE client is introduced. The AIMLE client is a functional entity that acts as the application client supporting AIMLE services.

[0041] As part of the AIMLE service, an ML repository is introduced. The ML repository 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.

[0042] One of the AIMLE capabilities introduced is the support for AI / ML task transfer. This functionality covers the AIMLE support for ML task transfer, which is applicable to scenarios where an AI / ML member cannot finish the assigned AI / ML task during the performing process. In this scenario, the AIMLE server assists the source AI / ML member by supporting the transfer of the intermediate AI / ML information (e.g., the intermediate AI / ML operation status and results) to another AI / ML member (target AI / ML member) for further operations to complete the AI / ML task.

[0043] As used herein, an FL process or task or operation may comprise a task involving multiple entities (FL members or participants who can be FL server or clients or FL aggregators) who jointly perform the training and / or inference of an ML model.

[0044] Energy efficiency and energy saving are important considerations for wireless communication systems including 5G and 6G. Previous work by the 3GPP on energy efficiency and energy saving will now be briefly discussed.

[0045] The 3GPP SAI working group identified use cases and requirements for energy efficiency and energy saving in 3GPP Release-19 and 3GPP Release-20 (as discussed in the 3GPP Specification TS 22.261 titled “Service requirements for the 5G system”). Energy efficiency and energy saving tends to be a critical feature in 5G. Such use cases and requirements can have application layer impacts. For example, a use case identified by the SAI working group are ‘demanding services’, e.g., XR and AI / ML, which require moreDocket No. SMM920250103-GR-NPenergy consumption at the device side (i.e., at the UE) as well as the network side (i.e., at the NE).

[0046] The 3GPP SA2 working group prepared the 3GPP Release-19 study on “5GS Enhancement for Energy Efficiency and Energy Saving”. The SA2 working group focus on enhancements to the 5G System that includes a framework for data volume information collection, network energy consumption collection, calculation, and exposure. A new network function, the Energy Information Function (EIF), is introduced in the 3GPP Specification TS 23.501 titled “System architecture for the 5G System (5GS)” and in the 3GPP Specification TS 23.502 titled “Procedures for the 5G System (5GS)”), to enable a consumer (i.e., an application function (AF) / network exposure function (NEF) or 5G core network function (5GC NF)) to subscribe for energy related information of required granularities (UE, S-NSSAI, PDU Session and / or Service Data Flow).

[0047] The 3GPP SA5 working group started the work on the energy efficiency topics of 5G from 3GPP Release-16 and continues this work in 3GPP Release-19. As defined in the 3GPP Specifications TS 28.310 titled “Management and orchestration; Energy efficiency of 5G”, TS 28.552 titled “Management and orchestration; 5G performance measurements”, and TS 28.554 titled “Management and orchestration; 5G end to end Key Performance Indicators (KPI)”, SA5 focus on defining use cases, requirements, and solutions for the measurement of the energy efficiency of the next generation RAN (NG- RAN), 5G Core and network slicing and for the optimization of energy saving.

[0048] Energy reduction and energy efficiency related efforts may also use Management Data Analytics (MDA). The use of MDA as per clause 8.4.4 of the 3GPP Specification TS 28.104 titled “Management and orchestration; Management Data Analytics (MDA)” concentrates on: (i) identifying excessive energy consumption of a specific NF, (ii) introducing statistics related to an entity, e.g., a cell, being in an energy saving state per given time window, and (iii) providing recommendations related to NR cells and 5G core UPFs indicating network entities that shall enter an energy saving state, or that shall take over the traffic related to the entities that are suggested to enter an energy saving state.Docket No. SMM920250103-GR-NP

[0049] As AI / ML energy consumption is a significant concern for the service consumer, especially as AI / ML models become more complex and data intensive, an MnS Consumer may ask the MnS Producer to report the energy consumption of training each AI / ML model and / or for inference as per clause 5.1.8 and 5.4.2 in the 3GPP Technical Report TR 28.858 “Study on Artificial Intelligence / Machine Learning (AI / ML) management phase 2”, respectively. The MnS Producer may report the energy consumption: (i) based on nodal measurements, i.e., for physical NFs and virtual NFs, as per the 3GPP Specification TS 28.552 titled “Management and orchestration; 5G performance measurements” and TS 28.554 titled “Management and orchestration; 5G end to end Key Performance Indicators (KPI)” respectively and / or (ii) by providing the number of floating-point operations per AI / ML model used, to enable the consumer to calculate the expenditure of energy consumption.

[0050] In principle the AI / ML energy consumption aspects may be characterized by several factors. The factors may include the number of cycles. For instance, a low number of cycles generally contributes to reduced energy consumption. The factors may include dataset size. For instance, large datasets generally contribute to increased energy consumption due to intensive data processing. The factors may include the number of Floating-Point Operations per Second (FLOPS). For instance, the number of FLOPS may be complex with AI / ML models contributing to increased energy consumption. The factors may include deployment on high-end hardware platform. For instance, deployment on high-end hardware platforms can reduce energy consumption by offering higher energy efficiency through optimized performance.

[0051] Furthermore, in the 3 GPP SA6 working group Rel-20 there is a Study on “Application Enablement to support Energy Saving (SP-250871)”. This particular study has a number of objectives. An objective is to study potential requirements for application enablement layer (e.g. NSCE, etc) for supporting use cases related to energy savings (e.g. re-map the user / application to the slice, network slice diagnostics enhancement with energy saving information). An objective is to investigate enhancements of existing procedures to add support for exposure of energy consumption related information to applications. An objective is to study potential usage and enhancements of SEAL enablers such asDocket No. SMM920250103-GR-NPSEALDD, AIMLE, LM, ADAE, etc services, to further optimize energy savings. An objective is to study whether and how other working groups (i.e., SA2, SA5) exposure of energy efficiency and energy saving capabilities can be consumed by SA6 enablers (e.g. energy consumption information provided by the CN (e.g. EIF defined in the 3 GPP Specification TS 23.501 titled “System architecture for the 5G System (5GS)”) and measurement information exposed by Management system (e.g. introduced in the 3 GPP Specifications TS 28.552 titled “Management and orchestration; 5 G performance measurements” and TS 28.554 titled “Management and orchestration; 5G end to end Key Performance Indicators (KPI)”) to address the above enhancements.

[0052] The 3GPP SA6 working group specified in 3GPP Rel-18 an AD AES, (as per the 3GPP Specification TS 23.436 titled “Functional architecture and information flows for Application Data Analytics Enablement Service”). The AD AES is a SEAL functionality for providing end to end performance analytics (e.g. vertical application layer (VAL) server performance). There are multiple analytics services defined in the 3GPP Specification TS 23.436. These analytics services include: 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; and data network (DN) energy analytics.

[0053] One analytics service of AD AES, as introduced in the 3GPP Rel-19, is the“Support for DN energy Analytics”. This feature supports a logical functionality at the AD AES to provide analytics on the energy consumption / efficiency of an edge platform (including the edge enabler server (EES)s / edge application server (EAS)s). The DN energy analytics is performed per DN name (DNN) / DN access identifier (DNAI) and may be used to trigger the application server migration to different cloud. The analytics are based on network data analytics function (NWDAF) analytics and UPF / DN measurements on user plane load as well as edge / app side measurements on the energy consumption. The procedure for this feature will now be described.

[0054] In a first step, the VAL server sends a DN energy analytics request to AD AES to perform analytics on the DN Energy Consumption / Efficiency for one or more DN / edgeDocket No. SMM920250103-GR-NPDN (EDN), Event ID= “DN energy analytics”, for a given DN service area (or subarea) and a given time window.

[0055] In a further step, the AD AES authorizes the VAL request.

[0056] In a further step, the AD AES requests and receives from the EAS / VAL servers hosted at the serving and target DNs (within the VAL service area), expected application service load and traffic schedules for the ongoing or future sessions within the area. Such data include traffic schedule report for the VAL Server, and this step re-uses the steps 3 to 10 of clause 8.8.2.1 of 3GPP TS 23.436.

[0057] In a further step, the AD AES calculates the expected energy consumption or efficiency based on the received traffic and load data for the given DNN / DNAI based on the request.

[0058] In a further step, the AD AES obtains the corresponding trained ML model based on the procedure in the 3GPP Specification TS 23.482 titled “Functional architecture and information flows for AIML Enablement Service” clause 8.3.2 and performs analytics to derive the predicted energy consumption at the target area and time horizon. The analytics outputs can be the predicted energy consumption / efficiency for the given DNN / DNAI.

[0059] In a further step, the AD AES sends a DN energy analytics response with the energy consumption / efficiency analytics output data to the VAL server.

[0060] The VAL server may use the analytics as input to trigger pro-actively an application server migration to a different edge cloud or to a centralized cloud as a way of reducing the energy consumption for the edge (if consumption is expected to be very high (e.g. higher than a pre-configured threshold)). The VAL server may use the analytics as input to trigger pro-actively an application server offboarding and the instantiation of a new server at the target edge / centralized cloud to minimize energy consumption of the edge platform (taking into account the system wide energy efficiency).

[0061] Currently, energy analytics from the application layer focuses mainly on the DN / server energy consumption or efficiency prediction. However, for AI / ML, and in particular the requirement for a UE to undertake part of an AI / ML task, the energyDocket No. SMM920250103-GR-NPconsumption / efficiency analytics of the UE would help select the most appropriate UE for the application layer AEML task (e.g. training, inference). This is considered to be particularly relevant to FL scenarios, such as ML model training in FL. Currently, it is not defined how to acquire energy data from the UE side to enable the prediction of whether a UE is suitable to act as an AI / ML / FL member in an application layer ML task, ML model lifecycle operation or other similar workflow operation.

[0062] The disclosure herein addresses this problem by providing a mechanism at a first network entity (i.e., an analytics function such as AD AES) for providing energy analytics for one or more UEs (or constituents / segments of the one or more UEs). More specifically, a mechanism is provided for enhancing AIML member capability analytics (the AIML member optionally being a VAL UE or a AIMLE client of the VAL UE) for supporting the energy criteria and information related to energy consumption / efficiency metrics. These energy -related metrics may be used as inputs for determining whether the UE can support an AI / ML operation or task (e.g. FL training / inference by the UE). Furthermore, a new ADAE analytics service for UE energy analytics is proposed that is based on UE data on load, energy and / or performance.

[0063] The examples described herein refer to an apparatus that is in the AD AES. However, in certain implementations, a different or new application enabler may be used e.g., an enabler dedicated for energy analytics, or an AI / ML related enabler (such as AIMLE), or an AF.

[0064] Figure 2 illustrates an example of a process flow 200 in accordance with aspects of the present disclosure. The process flow 200 may provide AD AES support for application layer AI / ML member capability analytics. The process flow 200 may implement or be implemented by aspects of the wireless communication system 100. For example, the process flow 200 may include an application layer analytics data repository function (A-ADRF) 210, an UE 220, an AD AES 230 and a consumer 240. The UE 220 may comprise a VAL client 222 and a ADAE client (ADAEC) 224. The consumer 240 may comprise a VAL server or an AIMLE server. The A-ADRF 210, UE 220, AD AES 230 and consumer 240 may be one or more examples of devices described herein with reference to Figure 1.Docket No. SMM920250103-GR-NP

[0065] The process flow 200 may be referred to as a procedure, including one or more operations performed by one or more of the A-ADRF 210, an UE 220, an AD AES 230 and a consumer 240. In the example of Figure 200, the process flow 200 may include a subscribe-notify procedure for supporting application layer AI / ML member capability analytics, where the application layer AI / ML member capability analytics includes the energy factor as part of the capability. Such analytics are performed based on data collected from a data producer (e.g. the ADAEC 224) and the A-ADRF 210, where the data includes energy data from the UE side. More specifically, the introduction of the energy factor into the procedure in the 3GPP Specification TS 23.436 clause 8.16.2.1 will also be described.

[0066] In the following description of the process flow 200, the operations or signalling performed between one or more of the A-ADRF 210, an UE 220, an AD AES 230 and consumer 240 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 A-ADRF 210, an UE 220, an AD AES 230 and consumer 240 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 200.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.

[0067] In step 201, the consumer 240 of analytics (e.g. a VAL Server, or a AIMLE Server) sends an application layer AI / ML member capability analytics subscription request to the AD AES 230. The analytics subscription request may comprise one or more of the information elements described in Table 1. In particular, the analytics subscription request may comprise an energy capability requirement.Docket No. SMM920250103-GR-NPDocket No. SMM920250103-GR-NPTable 1

[0068] At step 202, upon receiving the event subscription request from the consumer 240, the AD AES 230 may check for the relevant authorization for the event subscription. If the authorization is successful, the AD AES 230 may store the request information. The AD AES 230 may send a service API event subscription response to the consumer 240 indicating successful subscription.

[0069] At step 203, the AD AES 230 may map the analytics event ID to a list of data collection event identifiers, and a list of data producer IDs. Such mapping may be preconfigured by an operations, administration and maintenance (0AM) function or may be determined by the AD AES 230 based on the analytics event type / vertical type and / or data producer profile. If the analytics request from the consumer 240 comprises an energy related requirement, the AD AES 230 may determine and map the analytics event to the energy data to acquire, and to the data producers which can be functionalities in the target UE(s) 220 and / or data stored in A-ADRF 210. Data sources in the UE 220 may comprise either a UE modem functionality for energy metering, or a VAL app client or an app client service, or an AIMLE or ADAEC 224. In certain examples, the AD AES 230 (or the A- ADRF 210) may keep an Energy Profile per Data Source (at the UE 220) or per UE 220 (i.e., per VAL UE). The Energy Profile may be defined as an identification of an entity (e.g., the UE 220, a data source, a functionality of an application) with respect to the energy consumption or efficiency of that entity whilst operating or performing a task or process. The Energy Profile may be implemented based on an operator preference, for example the Energy Profile may be identified as Tow’ or ‘high’ energy consuming application or UE, or as a rating or weight, or may enclose one or more characteristics relating to energy resourceDocket No. SMM920250103-GR-NPutilisation under a particular environment or network condition. For example a ‘low’ energy rating may be present for highly congested areas or in high mobility assumptions. The energy profile may keep statistics or energy ratings of the UE 220 or the data source in the UE 220 (e.g. Al app) which can be used as a metric to identify whether the UE 220 is an energy demanding source. Thereby it can be determined whether the UE is applicable or not to be considered as capable of undertaking an AI / ML task. In some examples, if the Energy Profile is used, this information may either be provided by the UE 220 (or the consumer 240 i.e., the VAL server) as part of the energy data or can be quantified at the AD AES 230. If the Energy profile is used, then the mapping of the analytics event ID to the energy data sources may be based on the energy profile of the data source and / or the UE 220.

[0070] At steps 204a and 204b, the AD AES 230 sends a data collection subscription request to the data producers (i.e., ADAEC 224) or a data collection request to the data producers (i.e., the A-ADRF 210) with the respective Data Collection Event ID and the requirement for data collection. Data collection at the UE(s) 220 reuses the mechanism defined in the 3GPP Specification TS 26.531 titled “Data Collection and Reporting; General Description and Architecture”. The data collection subscription request and / or the data collection request may comprise one or more information elements. More specifically, the one or more information elements may be configured to indicate an energy information, an energy criteria and / or an energy data. The data collection subscription request may comprise one or more of the information elements shown in Table 2.Docket No. SMM920250103-GR-NPDocket No. SMM920250103-GR-NPTable 2

[0071] At step 205, the data producers (i.e., ADAEC 224) send a data collection subscription response as a positive or negative acknowledgement to the AD AES 230.

[0072] At step 206, the AD AES 230 based on the data collection subscription, receives from the ADAEC 224, the data on the application layer AI / ML Member capability based on the data collection event ID. The data may be received in a data notification. The data may comprise energy information of the UE 220. The data notification may comprise one or more information elements as shown in Table 3.Docket No. SMM920250103-GR-NPTable 3

[0073] At step 207, the AD AES 230, based on data collection request, receives from the A-ADRF 210, data / analytics on the application layer AI / ML Member capability based on the data / analytics collection event ID. The input from the A-ADRF 210 may comprise statistics on the energy average consumption / efficiency per UE 220 or per UE 220 per app, or per app / UE 220 per PLMN or slice or RAT / access type. In certain examples, the energy profile per UE 220 or for the data sources of the UE 220 (VAL client 222, ADAEC 224, UE modem, SEAL client) may be fetched from the A-ADRF 210 by the AD AES 230. In certain examples, the A-ADRF 210 may also keep the number or proportion of alerts / flags related to high energy consumption for the UE 220 given historical data. For example, a UE#1 from time X to time Y in an area of interest may have been reported 5 times to be in low battery or high energy consumption e.g. when running an app X. The procedures for data collection for application layer AI / ML Member capability analytics may also take user consent into account.

[0074] At step 208, the AD AES 230 performs analytics relevant operations to generate the analytics based on the data / analytics received from the ADAEC 224. The analytics may also be based on the energy capability / status or energy statistic of the VAL UE 220, based on the received data from the VAL UE 220 and / or A-ADRF 210. In certain examples, the analytics consider also the energy profile of the UE 220, or the determined energy profile of the UE 220 (i.e., determined at the AD AES 230) based on the received energy information.Docket No. SMM920250103-GR-NP

[0075] At step 209, the AD AES 230 sends application layer AI / ML member capability analytics notifications to the consumer 240 with the required application layer AI / ML Member capability analytics. The application layer AI / ML member capability analytics notification may comprise energy information. More specifically, the application layer AI / ML member capability analytics notification may comprise one or more of the information elements provided in Table 4.Table 4

[0076] Figure 3 illustrates an example of a process flow 300 in accordance with aspects of the present disclosure. The process flow 300 may be further example of a process flow that provides AD AES support for application layer AI / ML member capability analytics in accordance with aspects of the present disclosure. The process flow 300 may implement orDocket No. SMM920250103-GR-NPbe implemented by aspects of the wireless communication system 100. For example, the process flow 300 may include an AD AES 330 and a consumer 340, which may be one or more examples of devices described herein with reference to Figure 1. The consumer 340 may comprise a VAL server or an AIMLE server.

[0077] The process flow 300 may be referred to as a procedure, including one or more operations performed by one or more of the AD AES 330 and consumer 340. In the example of Figure 3, the process flow 300 may include a request-response procedure for supporting application layer AI / ML member capability analytics, where the application layer AI / ML member capability analytics includes the energy factor as part of the capability.

[0078] In the following description of the process flow 300, the operations or signalling performed between one or more of the AD AES 330 and consumer 340 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 AD AES 330 and consumer 340 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 300. 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.

[0079] The AD AES 330 may already have the analytics data derived from steps 203- 208 in the procedure shown in Figure 2.

[0080] At step 301, the analytics consumer 340 (e.g. VAL Server, AIMLE Server) sends a get application layer AI / ML member capability analytics request message to the AD AES 330 in order to receive analytics data for application layer AI / ML Member capability. The request contains message may comprise energy attributes. More specifically, the request may comprise one or more information elements as shown in Table 5.Docket No. SMM920250103-GR-NPDocket No. SMM920250103-GR-NPTable 5

[0081] At step 302, upon receiving the request, the AD AES 330 authenticates and authorizes the analytics consumer 340.

[0082] At step 303, if the analytics consumer 340 is authorized, the AD AES may get the analytics by performing the steps 203 to 208 of Figure 2.

[0083] At step 304, the AD AES 330 sends a get application layer AI / ML member capability analytics response message to the consumer 340 including the analytics data (statistical and / or predictive) of the application layer AI / ML Member capability. The get analytics response message may comprise one or more of the information elements shown in Table 6.Docket No. SMM920250103-GR-NPTable 6

[0084] The disclosure herein further introduces a new analytics event for “UE energy analytics” as requested from a consumer. The consumer may comprise a VAL Server or AIMLE server to an AD AES. The analytics may be used for selecting an AI / ML member UE in a ML training or inference task, or in an FL process. In some examples, the analytics event may also be used for identifying whether the UE of interest (or which of one or more UE(s)) can be used in an AI / ML process. In some examples, the analytics event may be used for identifying high energy consuming apps or UEs, as requested by the vertical / ASP. Two APIs are proposed herein to support the new analytics event. These APIs are referred to as “UE energy data collection API” and “UE energy data analytics API”.Docket No. SMM920250103-GR-NP

[0085] The UE energy data collection API (i.e., UE to Server / NW) may facilitate an energy data request or subscription request from AD AES to ADAEC. The energy data may include: battery information for the UE (stats and measurements on the battery usage and current battery capacity); battery drain rate stats (calculated at the UE); per app energy consumption; per app energy efficiency; per UE energy consumption / efficiency; per UE per slice / PLMN / RAT energy consumption / efficiency; and / or energy profile per UE or app.

[0086] The UE energy data analytics API (i.e., NW / Server to Analytics Consumer) may facilitate an energy data analytics request or subscription request from a consumer (i.e., AIMLE server or VAL server or FL server / collaborator) to an AD AES. The energy data analytics may include: statistics or predictions of the energy capability or status (battery level or capacity) or profile of the UE(s) of interest, e.g. maximum / minimum energy efficiency or consumption, battery drain rate, energy profile of the UE, highest energy draining apps, energy profile of the AI / ML apps; and / or energy sustainability predictions for a given session and time / area of interest or route of the UE. For example, the analytics output may be whether the energy consumption is sustainable for an AI / ML task or session in a given time period and area of interest or UE route (if UE mobility is known or given by VAL server). So, the output can be in form of Yes / No, if this is requested in the analytics subscription request.

[0087] Figure 4 illustrates an example of a process flow 400 in accordance with aspects of the present disclosure. The process flow 400 may be for providing AD AES support for DN energy analytics in accordance with aspects of the present disclosure. The process flow 400 may implement or be implemented by aspects of the wireless communication system 100. For example, the process flow 400 may include VAL UE(s) 420, AD AES 430 and a consumer 440, which may be one or more examples of devices described herein with reference to Figure 1. The consumer 440 may comprise an AIMLE server or a VAL server, for instance.

[0088] The process flow 400 may be referred to as a procedure, including one or more operations performed by one or more of the VAL UE(s) 420, AD AES 430 and consumer 440.Docket No. SMM920250103-GR-NP

[0089] In the following description of the process flow 400, the operations or signalling performed between one or more of the VAL UE(s) 420, AD AES 430 and consumer 440 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(s) 420, AD AES 430 and consumer 440 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 400. 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.

[0090] At a first step 401, the consumer 440 (i.e., AIMLE or VAL server) sends an UE energy analytics request to the AD AES 430 to perform analytics on the VAL UE 420 Energy Consumption / Efficiency for either a UE app (VAL client, AIMLE client) or for the entire UE 420 (UE modem plus OS plus apps). The request may be to perform analytics for an event ID= “VAL UE energy analytics”, for a given service area (or subarea) and / or for a given time window. The request may include a VAL service ID or AIMLE service ID for which the analytics apply, as well as the consumer identifier and the analytics KPI.

[0091] In some examples, the request may include a predicted or expected route of the VAL UE 420, and the type of analytics requested. This type may comprise requesting statistics or predictions on the energy level of the UE or the UE apps of interest. The type may comprise requesting whether the energy consumption / efficiency is sustainable for a given VAL or AIMLE service, where this service can be an AI / ML service.

[0092] In some examples, this request may include an AI / ML task or operation or process (ML training or inference or FL task) for which the prediction / statistics apply.

[0093] In some examples, the request may include a request for recommending whether the VAL UE 420 is applicable or suitable for the AI / ML task based on the energy prediction or sustainability indicator.

[0094] In some examples, the analytics type is a prediction of the energy profile of the VAL UE 420 and / or the data sources in the VAL UE 420.

[0095] In a further step 402, the AD AES 430 authorizes the VAL request.Docket No. SMM920250103-GR-NP

[0096] In a further step 403, the AD AES 430 maps the UE energy analytics event ID to a list of data collection event identifiers, and a list of data producer IDs. Such mapping may be preconfigured by an 0 AM or may be determined by the AD AES 430 based on the analytics event type / vertical type and / or data producer profile. The AD AES 430 determines and maps the analytics event to the energy data to acquire and to the data producers which can be functionalities in the target VAL UE(s) 420 and / or data stored in an A-ADRF. The step 403 is shown as “acquire load, energy and / or performance data for VAL UE”.

[0097] In some examples, the AD AES 430 (or A-ADRF) keeps an Energy Profile per Data Source (at UE) or per VAL UE 420. This energy profile keeps statistics or energy rating of the VAL UE 420 or the data source in the UE 420 (e.g. Al app) which can be used as a metric to identify whether the UE 420 is an energy demanding source. Accordingly, it can be determined whether the UE 420 is applicable or not to be considered as capable of undertaking an AI / ML task.

[0098] In some examples, if the Energy Profile is used, this information may either be provided by the UE 420 (or the VAL server) as part of the energy data or can be quantified at the AD AES 430.

[0099] If the Energy profile is used, then the mapping of analytics event ID to energy data sources may be based on the energy profile of the data source and / or the UE 420.

[0100] The AD AES 430 may request and receive from the VAL UE(s) 420 of interest, the energy data in similar manner as in steps 203-207 of Figure 2, in this instance using the UE Energy Data Collection API.

[0101] In addition, or complementary, or alternatively to UE energy data collection, the AD AES 430 may also request and receive from an ADAEC of the UE 420, load measurements for the UE apps (VAL client, ADAEC) and performance measurements related to the UE to Server sessions (based on existing ADAE capabilities).

[0102] In step 404, the AD AES 430 calculates the expected UE energy consumption or efficiency based on the received UE energy / load / performance data from step 403, based on the request.Docket No. SMM920250103-GR-NP

[0103] In step 405, the AD AES 430 obtains the corresponding trained ML model based on procedure in the 3GPP Specification TS 23.482 titled “Functional architecture and information flows for AIML Enablement Service” clause 8.3.2 and performs analytics to derive the predicted UE energy consumption or efficiency or energy sustainability at the target area and time horizon. The analytics outputs can be the predicted energy consumption / efficiency for the given VAL UE 420 or UE app. In particular, the analytics output may comprise: statistics or predictions on the energy level of the UE or the UE apps of interest; identification on whether the energy consumption / efficiency is sustainable for a given VAL or AIMLE service, where this service can be an AI / ML service; a recommendation on whether the VAL UE 420 is applicable or suitable for the AI / ML task based on the energy prediction or sustainability indicator; and / or a prediction of the energy profile of the VAL UE 420 and / or the data sources in the VAL UE 420.

[0104] In step 406, the AD AES 430 sends the analytics output via the UE energy analytics API to the consumer 440 as an analytics response with the UE energy consumption / efficiency analytics output data (based on the type of analytics in the request).

[0105] The AD AES 430 and / or the consumer 440 (i.e., the AIMLE) may also store the energy data and / or analytics to a repository. The repository may comprise an A-ADRF or ML repository (if this applies to an ML task)

[0106] Based on step 406, the consumer 440 (AIMLE or VAL server) can use these analytics as input to trigger the pro-active selection or re-selection or removal of the VAL UE 420 from the list of UEs which are capable of undertaking an AI / ML process / task / operation, having taken consideration of the energy factor.

[0107] Figure 5 illustrates an example of a UE 500 in accordance with aspects of the present disclosure. The UE 500 may include a processor 502, a memory 504, a controller 506, and a transceiver 508. The processor 502, the memory 504, the controller 506, or the transceiver 508, 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.Docket No. SMM920250103-GR-NP

[0108] The processor 502, the memory 504, the controller 506, or the transceiver 508, 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.

[0109] The processor 502 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 502 may be configured to operate the memory 504. In some other implementations, the memory 504 may be integrated into the processor 502. The processor 502 may be configured to execute computer-readable instructions stored in the memory 504 to cause the UE 500 to perform various functions of the present disclosure.

[0110] The memory 504 may include volatile or non-volatile memory. The memory 504 may store computer-readable, computer-executable code including instructions when executed by the processor 502 cause the UE 500 to perform various functions described herein. The code may be stored in a non-transitory computer-readable medium such the memory 504 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.[oni] In some implementations, the processor 502 and the memory 504 coupled with the processor 502 may be configured to cause the UE 500 to perform one or more of the functions described herein (e.g., executing, by the processor 502, instructions stored in the memory 504). For example, the processor 502 may support wireless communication at the UE 500 in accordance with examples as disclosed herein. The UE 500 may be configured to support the arrangements described herein.

[0112] The controller 506 may manage input and output signals for the UE 500. The controller 506 may also manage peripherals not integrated into the UE 500. In some implementations, the controller 506 may utilize an operating system such as iOS®,Docket No. SMM920250103-GR-NPANDROID®, WINDOWS®, or other operating systems. In some implementations, the controller 506 may be implemented as part of the processor 502.

[0113] In some implementations, the UE 500 may include at least one transceiver 508. In some other implementations, the UE 500 may have more than one transceiver 508. The transceiver 508 may represent a wireless transceiver. The transceiver 508 may include one or more receiver chains 510, one or more transmitter chains 512, or a combination thereof.

[0114] A receiver chain 510 may be configured to receive signals (e.g., control information, data, packets) over a wireless medium. For example, the receiver chain 510 may include one or more antennas for receive the signal over the air or wireless medium. The receiver chain 510 may include at least one amplifier (e.g., a low-noise amplifier (LN A)) configured to amplify the received signal. The receiver chain 510 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 510 may include at least one decoder for decoding the processing the demodulated signal to receive the transmitted data.

[0115] A transmitter chain 512 may be configured to generate and transmit signals (e.g., control information, data, packets). The transmitter chain 512 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 512 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 512 may also include one or more antennas for transmitting the amplified signal into the air or wireless medium.

[0116] Figure 6 illustrates an example of a processor 600 in accordance with aspects of the present disclosure. The processor 600 may be an example of a processor configured to perform various operations in accordance with examples as described herein. The processor 600 may include a controller 602 configured to perform various operations in accordance with examples as described herein. The processor 600 may optionally include at least oneDocket No. SMM920250103-GR-NPmemory 604, which may be, for example, an L1 / L2 / L3 cache. Additionally, or alternatively, the processor 600 may optionally include one or more arithmetic-logic units (ALUs) 606. 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).

[0117] The processor 600 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 600) or other 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).

[0118] The controller 602 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 600 to cause the processor 600 to support various operations in accordance with examples as described herein. For example, the controller 602 may operate as a control unit of the processor 600, generating control signals that manage the operation of various components of the processor 600. These control signals include enabling or disabling functional units, selecting data paths, initiating memory access, and coordinating timing of operations.

[0119] The controller 602 may be configured to fetch (e.g., obtain, retrieve, receive) instructions from the memory 604 and determine subsequent instruction(s) to be executed to cause the processor 600 to support various operations in accordance with examples as described herein. The controller 602 may be configured to track memory address of instructions associated with the memory 604. The controller 602 may be configured to decode instructions to determine the operation to be performed and the operands involved. For example, the controller 602 may be configured to interpret the instruction andDocket No. SMM920250103-GR-NPdetermine control signals to be output to other components of the processor 600 to cause the processor 600 to support various operations in accordance with examples as described herein. Additionally, or alternatively, the controller 602 may be configured to manage flow of data within the processor 600. The controller 602 may be configured to control transfer of data between registers, arithmetic logic units (ALUs), and other functional units of the processor 600.

[0120] The memory 604 may include one or more caches (e.g., memory local to or included in the processor 600 or other memory, such RAM, ROM, DRAM, SDRAM, SRAM, MRAM, flash memory, etc. In some implementations, the memory 604 may reside within or on a processor chipset (e.g., local to the processor 600). In some other implementations, the memory 604 may reside external to the processor chipset (e.g., remote to the processor 600).

[0121] The memory 604 may store computer-readable, computer-executable code including instructions that, when executed by the processor 600, cause the processor 600 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 602 and / or the processor 600 may be configured to execute computer-readable instructions stored in the memory 604 to cause the processor 600 to perform various functions. For example, the processor 600 and / or the controller 602 may be coupled with or to the memory 604, the processor 600, the controller 602, and the memory 604 may be configured to perform various functions described herein. In some examples, the processor 600 may include multiple processors and the memory 604 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.

[0122] The one or more ALUs 606 may be configured to support various operations in accordance with examples as described herein. In some implementations, the one or more ALUs 606 may reside within or on a processor chipset (e.g., the processor 600). In some other implementations, the one or more ALUs 606 may reside external to the processor chipset (e.g., the processor 600). One or more ALUs 606 may perform one or moreDocket No. SMM920250103-GR-NPcomputations such as addition, subtraction, multiplication, and division on data. For example, one or more ALUs 606 may receive input operands and an operation code, which determines an operation to be executed. One or more ALUs 606 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 606 may support logical operations such as AND, OR, exclusive-OR (XOR), not-OR (NOR), and not- AND (NAND), enabling the one or more ALUs 606 to handle conditional operations, comparisons, and bitwise operations.

[0123] The processor 600 may support wireless communication in accordance with examples as disclosed herein. The processor 600 may be configured to or operable to support a means for a first network entity. The processor 600 may be configured to or operable to cause the first network entity to: receive, from a second network entity, a first message indicating an analytics request associated with an energy capability of at least one UE; determine, in response to receiving the analytics request and based at least on an energy data, analytics associated with the energy capability of the at least one UE; and transmit, to the second network entity, a second message indicating the analytics associated with the energy capability of the at least one UE.

[0124] Alternatively, the processor 600 may be configured to or operable to support a means for a second network entity. The processor 600 may be configured to or operable to cause the second network entity to: transmit, to a first network entity, a first message indicating an analytics request associated with an energy capability of at least one UE; and receive, from the first network entity, a second message indicating an analytics associated with the energy capability of the at least one UE.

[0125] Figure 7 illustrates an example of a NE 700 in accordance with aspects of the present disclosure. The NE 700 may include a processor 702, a memory 704, a controller 706, and a transceiver 708. The processor 702, the memory 704, the controller 706, or the transceiver 708, 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.Docket No. SMM920250103-GR-NP

[0126] The processor 702, the memory 704, the controller 706, or the transceiver 708, 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.

[0127] The processor 702 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 702 may be configured to operate the memory 704. In some other implementations, the memory 704 may be integrated into the processor 702. The processor 702 may be configured to execute computer-readable instructions stored in the memory 704 to cause the NE 700 to perform various functions of the present disclosure.

[0128] The memory 704 may include volatile or non-volatile memory. The memory 704 may store computer-readable, computer-executable code including instructions when executed by the processor 702 cause the NE 700 to perform various functions described herein. The code may be stored in a non-transitory computer-readable medium such the memory 704 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.

[0129] In some implementations, the processor 702 and the memory 704 coupled with the processor 702 may be configured to cause the NE 700 to perform one or more of the functions described herein (e.g., executing, by the processor 702, instructions stored in the memory 704). For example, the processor 702 may support wireless communication at the NE 700 in accordance with examples as disclosed herein. The NE 700 may comprise the first network entity described herein. The NE 700 may be configured to: receive, from a second network entity, a first message indicating an analytics request associated with an energy capability of at least one UE; determine, in response to receiving the analytics request and based at least on an energy data, analytics associated with the energy capabilityDocket No. SMM920250103-GR-NPof the at least one UE; and transmit, to the second network entity, a second message indicating the analytics associated with the energy capability of the at least one UE.

[0130] Alternatively, the NE 700 may comprise the second network entity described herein. The NE 700 may be configured to: transmit, to a first network entity, a first message indicating an analytics request associated with an energy capability of at least one UE; and receive, from the first network entity, a second message indicating an analytics associated with the energy capability of the at least one UE.

[0131] The controller 706 may manage input and output signals for the NE 700. The controller 706 may also manage peripherals not integrated into the NE 700. In some implementations, the controller 706 may utilize an operating system such as iOS®, ANDROID®, WINDOWS®, or other operating systems. In some implementations, the controller 706 may be implemented as part of the processor 702.

[0132] In some implementations, the NE 700 may include at least one transceiver 708. In some other implementations, the NE 700 may have more than one transceiver 708. The transceiver 708 may represent a wireless transceiver. The transceiver 708 may include one or more receiver chains 710, one or more transmitter chains 712, or a combination thereof.

[0133] A receiver chain 710 may be configured to receive signals (e.g., control information, data, packets) over a wireless medium. For example, the receiver chain 710 may include one or more antennas for receive the signal over the air or wireless medium. The receiver chain 710 may include at least one amplifier (e.g., a low-noise amplifier (LNA)) configured to amplify the received signal. The receiver chain 710 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 710 may include at least one decoder for decoding the processing the demodulated signal to receive the transmitted data.

[0134] A transmitter chain 712 may be configured to generate and transmit signals (e.g., control information, data, packets). The transmitter chain 712 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 orDocket No. SMM920250103-GR-NPmore 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 712 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 712 may also include one or more antennas for transmitting the amplified signal into the air or wireless medium.

[0135] Figure 8 illustrates a flowchart of a method 800 in accordance with aspects of the present disclosure. The operations of the method 800 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.

[0136] At 802, the method 800 may include receiving, from a second network entity, a first message indicating an analytics request associated with an energy capability of at least one UE. The operations of 802 may be performed in accordance with examples as described herein. In some implementations, aspects of the operations of 802 may be performed by a NE as described with reference to Figure 7.

[0137] At 804, the method 800 may include determining, in response to receiving the analytics request and based on an energy data, analytics associated with the energy capability of the at least one UE. The operations of 804 may be performed in accordance with examples as described herein. In some implementations, aspects of the operations of 804 may be performed by a NE as described with reference to Figure 7.

[0138] At 806, the method 800 may include transmitting, to the second network entity, a second message indicating the analytics associated with the energy capability of the at least one UE. The operations of 806 may be performed in accordance with examples as described herein. In some implementations, aspects of the operations of 806 may be performed a NE as described with reference to Figure 7.

[0139] It should be noted that the method 800 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. SMM920250103-GR-NP

[0140] Figure 9 illustrates a flowchart of a method 900 in accordance with aspects of the present disclosure. The operations of the method 900 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.

[0141] At 902, the method 900 may include transmitting, to a first network entity, a first message indicating an analytics request associated with an energy capability of at least one UE. The operations of 902 may be performed in accordance with examples as described herein. In some implementations, aspects of the operations of 902 may be performed by a NE as described with reference to Figure 7.

[0142] At 904, the method 900 may include receiving, from the first network entity, a second message indicating the analytics associated with the energy capability of the at least one UE. The operations of 904 may be performed in accordance with examples as described herein. In some implementations, aspects of the operations of 904 may be performed by a NE as described with reference to Figure 7.

[0143] It should be noted that the method 900 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.

[0144] 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. The first network entity may comprise at least one memory and at least one processor coupled with the at least one memory. The at least one processor may be configured to cause the first network entity to: receive, from a second network entity, a first message indicating an analytics request associated with an energy capability of at least one UE; determine, in response to receiving the analytics request and based at least on an energy data, analytics associated with the energy capability of the at least one UE; and transmit, to the second network entity, a second message indicating the analytics associated with the energy capability of the at least one UE.Docket No. SMM920250103-GR-NP

[0145] The first message may comprise an application layer AI / ML member capability analytics subscription request. The first message may alternatively comprise a UE energy analytics request or energy data analytics request.

[0146] The second message may comprise an application layer AI / ML member capability analytics notification. The second message may alternatively comprise an application layer AI / ML member capability analytics response message. The second message may be referred to herein more generally as an analytics response.

[0147] The analytics of the energy capability of the at least one UE may be predictive analytics. The analytics of the energy capability of the at least one UE may be statistical analytics. The analytics may comprise application layer AI / ML member capability analytics.

[0148] The energy capability may comprise at least one of a battery capacity and / or battery status; a battery level below or above a first pre-defined threshold; a battery drain rate for a plurality of different load conditions; a battery drain rate over a second predefined threshold; a battery drain rate owing to a particular application, service or session that is higher than a third pre-defined threshold; an energy consumption and / or efficiency; an energy consumption and / or efficiency per application or group of applications, optionally for a given time period, for an area of interest, for a radio access technology, for a public land mobile network or for a slice; and / or an energy consumption and / or efficiency for a specific type or category of application.

[0149] The battery drain rate may be per UE in different load conditions i.e., in high load, medium load, low load etc. The specific type or category of application may comprise high or highest energy consuming applications, Al applications, for instance.

[0150] The energy data may comprise at least one of the battery capacity, level and / or the battery status; a battery drain rate statistic, optionally the battery level below or above the first pre-defined threshold, the battery drain rate for the plurality of different load conditions, the battery drain rate over the second pre-defined threshold, or the battery drain rate owing to the particular application, service or session that is higher than the third predefined threshold; an energy consumption and / or efficiency statistic, optionally the energyDocket No. SMM920250103-GR-NPconsumption and / or efficiency per the application or the group, type or category of applications, optionally for the given time period, for the area of interest, for the radio access technology, for the public land mobile network or for the slice; an energy profile of the at least one UE or of an application; and / or a floating point operations per second, FLOPS, status of the at least one UE.

[0151] The energy profile may be an energy profile per data source (at a UE) or per VAL UE. The energy profile keeps statistics of or the energy rating of the UE or data source in the UE which can be used as a metric to identify whether the UE is an energy demanding source.

[0152] The analytics may comprise at least one of: a statistic or prediction relating to energy consumption and / or efficiency of the at least one UE; a sustainability of energy consumption and / or efficiency of the at least one UE, optionally for a service; or a recommendation on suitability of the at least one UE, optionally for an AI / ML task.

[0153] The service may comprise a VAL service or an AIMLE service, for instance an AI / ML service. The energy profile prediction may be of the at least one UE and / or one or more data sources in the at least one UE.

[0154] The recommendation may be based at least partly on the statistic, the prediction and / or the sustainability; or on an energy profile prediction

[0155] The at least one processor may be further configured to cause the first network entity to: determine, in response to receiving the first message, the energy data of the at least one UE.

[0156] The at least one processor may be further configured to cause the first network entity to: determine at least one data producer or repository for producing or providing the energy data; transmit, to the at least one data producer or repository, a third message indicating a requirement for the energy data; and receive, from the at least one data producer or repository, a fourth response comprising the energy data.Docket No. SMM920250103-GR-NP

[0157] The at least one processor may be further configured to cause the first network entity to determine the at least one data producer or repository by discovering the at least one data producer or repository.

[0158] The third message may comprise a data collection subscription request or data collection request. The fourth message may comprise a data collection subscription response or data collection response.

[0159] The at least one data producer or repository may comprise at least one of: an application; an application client; an application client server; an AIMLE; an ADAE client; an ADAE server; a UE modem functionality; or an ADRF.

[0160] The applicant client / client server may be a VAL application client / client server. The UE modem functionality may be a functionality for energy metering. The ADRF may comprise an A- ADRF.

[0161] The at least one processor may be further configured to cause the first network entity to: calculate, based at least partly on the energy data, an energy consumption and / or efficiency metric.

[0162] The analytics may comprise application layer analytics.

[0163] The application layer analytics may be provided by an application enablement entity.

[0164] The at least one UE may comprise at least one of: an application function; an application enabler client; an AIMLE client; a VAL UE, optionally a VAL UE client; an OS; and / or a modem.

[0165] The first network entity may comprise at least one of: an ADAE server; an application enabler server; an AIMLE server; and / or an application function. The second network entity may comprise at least one of: a VAL server; and / or an AIMLE server.

[0166] The first message may comprise at least one of: an energy capability attribute; an energy status attribute; an energy analytics reporting criteria; and / or an energy analytics event identifier.Docket No. SMM920250103-GR-NP

[0167] The energy analytics reporting criteria may be associated with the at least one UE. The energy analytics event may be a UE energy analytics event. The energy analytics event identifier may be referred to as an analytics ID.

[0168] The first message may be configured to identify the analytics as for supporting an AI / ML task.

[0169] A second network entity for wireless communication is described. The second network entity may be configured to, capable of, or operable to perform one or more operations as described herein. The second network entity may comprise at least one memory and at least one processor coupled with the at least one memory. The at least one processor may be configured to cause the second network entity to: transmit, to a first network entity, a first message indicating an analytics request associated with an energy capability of at least one UE; and receive, from the first network entity, a second message indicating an analytics associated with the energy capability of the at least one UE.

[0170] The at least one processor may be further configured to: select or remove, based at least in part on the analytics, one or more UE of the at least one UE as an AI / ML member of an AI / ML task.

[0171] A method performed or performable by a first network entity is described. The method may comprise: receiving, from a second network entity, a first message indicating an analytics request associated with an energy capability of at least one UE; determining, in response to receiving the analytics request and based on an energy data, analytics associated with the energy capability of the at least one UE; and transmitting, to the second network entity, a second message indicating the analytics associated with the energy capability of the at least one UE.

[0172] The energy capability may comprise at least one of: a battery capacity and / or battery status; a battery level below or above a first pre-defined threshold; a battery drain rate for a plurality of different load conditions; a battery drain rate over a second predefined threshold; a battery drain rate owing to a particular application, service or session that is higher than third pre-defined threshold; an energy consumption and / or efficiency; an energy consumption and / or efficiency per application or group of applications, optionallyDocket No. SMM920250103-GR-NPfor a given time period, for an area of interest, for a radio access technology, for a public land mobile network or for a slice; or an energy consumption and / or efficiency for a specific type or category of application.

[0173] The energy data may comprise at least one of: the battery capacity, level and / or the battery status; a battery drain rate statistic, optionally the battery level below or above the first pre-defined threshold, the battery drain rate for the plurality of different load conditions, the battery drain rate over the second pre-defined threshold, or the battery drain rate owing to the particular application, service or session that is higher than the third predefined threshold; an energy consumption and / or efficiency statistic, optionally the energy consumption and / or efficiency per the application or the group, type or category of applications, optionally for the given time period, for the area of interest, for the radio access technology, for the public land mobile network or for the slice; an energy profile of the at least one UE or of an application; a floating point operations per second, FLOPS, status of the at least one UE.

[0174] The analytics may comprise at least one of: a statistic or prediction relating to energy consumption and / or efficiency of the at least one UE; a sustainability of energy consumption and / or efficiency of the at least one UE, optionally for a service; and / or a recommendation on suitability of the at least one UE, optionally for an AI / ML task.

[0175] The method may comprise determining, in response to receiving the first message, the energy data of the at least one UE.

[0176] The method may comprise: determining at least one data producer or repository for producing or providing the energy data; transmitting, to the at least one data producer or repository, a third message indicating a requirement for the energy data; and receiving, from the at least one data producer or repository, a fourth response comprising the energy data.

[0177] The at least one data producer or repository may comprise at least one of: an application; an application client; an application client server; an AIMLE; an ADAE, client; an ADAE server; a UE modem functionality; or an analytics data repository function, ADRF.Docket No. SMM920250103-GR-NP

[0178] The method may comprise calculating, based at least partly on the energy data, an energy consumption and / or efficiency metric.

[0179] The analytics may comprise application layer analytics.

[0180] The at least one UE may comprise at least one of: an application function; an application enabler client; an AIMLE client; a VAL UE, optionally a VAL UE client; an OS; and / or a modem.

[0181] The first network entity may comprise at least one of: an ADAE server; an application enabler server; an AIMLE server; or an application function.

[0182] The second network entity may comprise at least one of: a VAL server; or an AIMLE server.

[0183] The first message may comprise at least one of: an energy capability attribute; an energy status attribute; an energy analytics reporting criteria; or an energy analytics event identifier.

[0184] The first message may be configured to identify the analytics as for supporting an AI / ML task.

[0185] A method performed or performable by a second network entity is described. The method may comprise: transmitting, to a first network entity, a first message indicating an analytics request associated with an energy capability of at least one UE; and receiving, from the first network entity, a second message indicating the analytics associated with the energy capability of the at least one UE.

[0186] The method may comprise selecting or removing, based at least in part on the analytics, one or more UEs of the at least one UE as an AI / ML member of an AI / ML task.

[0187] The disclosure herein tends to address the problem of how to acquire energy data from the UE side and predict the energy efficiency / consumption of a UE. In particular, the disclosure herein tends to address the problem of determining whether a UE is suitable to act as an ML / FL member in an application layer ML task (or ML model lifecycle / workflow operation).Docket No. SMM920250103-GR-NP

[0188] This disclosure herein introduces a mechanism for obtaining, at an enabler server, energy information from one or more UEs at AD AES and deriving analytics on the UE energy capability for a given application layer ML task.

[0189] Currently, AI / ML member capability analytics do not consider energy information.

[0190] An example procedure is proposed herein for enhancing AI / ML member capability analytics for supporting the energy criteria and information related to the energy consumption / efficiency metrics of the AI / ML member. The AI / ML may comprise a VAL UE or an AIMLE client of the VAL UE. These energy consumption / efficiency metrics may be used as inputs for determining whether the VAL UE can support an AI / ML operation / task (e.g., FL training / inference by the UE).

[0191] Furthermore, a new ADAE analytics service is proposed for UE energy analytics based on UE data on load / energy / performance

[0192] There is provided an apparatus for energy -related UE analytics, the apparatus comprising: at least one memory and at least one processor coupled with the at least one memory and configured to cause the apparatus to: receive from an application server a request for analytics, wherein the request for analytics comprises a requirement for predicting the energy capability of at least one UE; determine to obtain energy data for the at least one UE, based on the analytics request; obtain the determined energy data for the at least one UE; derive analytics on the energy capability partly based on the obtained energy data; and send an energy-related analytics parameter based on the derived analytics to the application server.

[0193] The at least one processor may be further configured to cause the apparatus to: determine at least one data producer to obtain the energy data, the data producer being an application or a UE modem functionality.

[0194] The at least one processor may be further configured to cause the apparatus to: obtain the energy data from at least one repository.Docket No. SMM920250103-GR-NP

[0195] The at least one processor may be further configured to cause the apparatus to: calculate an energy consumption and / or efficiency metric prior deriving the analytics.

[0196] The at least one processor may be further configured to cause the apparatus to: discover at least one data producer for obtaining the energy data.

[0197] The energy-related UE analytics may comprise an application layer analytics service, provided by an application enablement entity.

[0198] The at least one UE may comprise at least one of an application, an application enabler client, a VAL client, an OS, a UE modem or a combination thereof.

[0199] The request for analytics may comprise an energy capability attribute, energy status attribute and / or energy analytics reporting criteria associated with the at least one UE

[0200] The energy-related UE analytics may be identified as a UE capability analytics for supporting an AI / ML task

[0201] The request for analytics may comprise an UE energy analytics event

[0202] The energy capability may comprise at least one of:- a battery capacity and status of the UE; a battery drain rate per UE in different load situations (e.g. in high load, medium load); an energy consumption / efficiency per UE; an energy consumption / efficiency per app per UE; an energy consumption / efficiency for highest consuming apps or for specific types of apps (i.e., Al apps);a statistic / s on energy consumption / efficiency per UE.

[0203] The energy capability attribute or criteria may comprise: a battery level of the UE below or over a pre-defined threshold; a battery drain rate over a threshold; a battery drain rate due to an app, or a service, or a session, higher than a pre-defined threshold; a high energy consumption per app or group of apps for a given time unit; a high energy consumption per app or group of apps for a given area of interest; a high energy consumption per app or group of apps per RAT, PLMN or slice.

[0204] The energy data may comprise: a battery information for the UE (i.e., statistics and / or measurements on the battery usage and current battery capacity); a battery drain rate statistic / s (i.e., as calculated at the UE); a per app energy consumption; a per app energyDocket No. SMM920250103-GR-NPefficiency; a per UE energy consumption / efficiency; a per UE or per slice or per PLMN or per RAT energy consumption / efficiency; an energy profile per UE or app; or a FLOPS status.

[0205] The derived energy analytics and / or the energy-related analytics parameter may comprise one or more of: statistics or predictions on the energy level of the UE or the UE apps of interest; identification on whether the energy consumption / efficiency is sustainable for a given VAL or AIMLE service, where this service optionally comprises an AEML service; a recommendation on whether the VAL UE is applicable or suitable for the AI / ML task based on the energy prediction or sustainability indicator; a prediction of the energy profile of the VAL UE and / or the data sources in the VAL UE.

[0206] The derived energy analytics may further be utilized for the selection of the at least one UE as an AEML member.

[0207] 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.

[0208] 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.

[0209] The following abbreviations are relevant in the field addressed by this document: 5G, 5th Generation of Mobile Communications; 5GC, 5G Core; 5QI, 5G QoS Identifier; AF, Application Function; AI / ML or AIML, Artificial Intelligence / Machine Learning ; SEAL, Service Enabler Architecture Layer; AIMLE, AI / ML Enablement; HFL, Horizontal Federated Learning (FL); VFL, Vertical FL; VAL, Vertical Application Layer; SLA, Service Level Agreement; ASP, Application Service Provider; EIF, Energy Information Function; PLMN, Public Land Mobile Network; MNO, Mobile Network Operator; AD AES, Application Data Analytics Enablement Server; NEF, NetworkDocket No. SMM920250103-GR-NPExposure Function; EDN, Edge Data Network (DN); NPN, Non-Public Network;HPLMN, Home PLMN; SNPN, Standalone NPN; ECSP, Edge Computing Service Provider; VPLMN, Visiting PLMN; ECS, Edge Configuration Server; LBO, Local Breakout; HR, Home Routed; NSCE, Network Slice Capability Enablement; CSP, Cloud Service Provider; VAL, Vertical Application Server; and A-ADRF, Application layer - Analytics Data Repository Function.Docket No. SMM920250103-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, from a second network entity, a first message indicating an analytics request associated with an energy capability of at least one user equipment, UE; determine, in response to receiving the analytics request and based at least on an energy data, analytics associated with the energy capability of the at least one UE; and transmit, to the second network entity, a second message indicating the analytics associated with the energy capability of the at least one UE.

2. The first network entity of claim 1, wherein the energy capability comprises at least one of: a battery capacity and / or battery status; a battery level below or above a first pre-defined threshold; a battery drain rate for a plurality of different load conditions; a battery drain rate over a second pre-defined threshold; a battery drain rate owing to a particular application, service or session that is higher than a third pre-defined threshold; an energy consumption and / or efficiency; an energy consumption and / or efficiency per application or group of applications, optionally for a given time period, for an area of interest, for a radio access technology, for a public land mobile network or for a slice; and / or an energy consumption and / or efficiency for a specific type or category of application.

3. The first network entity of claim 2, wherein the energy data comprises at least one of:Docket No. SMM920250103-GR-NPthe battery capacity, level and / or the battery status; a battery drain rate statistic, optionally the battery level below or above the first pre-defined threshold, the battery drain rate for the plurality of different load conditions, the battery drain rate over the second pre-defined threshold, or the battery drain rate owing to the particular application, service or session that is higher than the third pre-defined threshold; an energy consumption and / or efficiency statistic, optionally the energy consumption and / or efficiency per the application or the group, type or category of applications, optionally for the given time period, for the area of interest, for the radio access technology, for the public land mobile network or for the slice; an energy profile of the at least one UE or of an application; and / or a floating-point operations per second, FLOPS, status of the at least one UE.

4. The first network entity of any one of the preceding claims, wherein the analytics comprise at least one of a statistic or prediction relating to energy consumption and / or efficiency of the at least one UE; a sustainability of energy consumption and / or efficiency of the at least one UE, optionally for a service; or a recommendation on suitability of the at least one UE, optionally for an AI / ML task.

5. The first network entity of any one of claims 1-4, wherein the at least one processor is further configured to cause the first network entity to: determine, in response to receiving the first message, the energy data of the at least one UE;6. The first network entity of claim 5, wherein the at least one processor is further configured to cause the first network entity to: determine at least one data producer or repository for producing or providing the energy data;Docket No. SMM920250103-GR-NPtransmit, to the at least one data producer or repository, a third message indicating a requirement for the energy data; and receive, from the at least one data producer or repository, a fourth response comprising the energy data.

7. The first network entity of claim 6, wherein the at least one data producer or repository comprises at least one of: an application; an application client; an application client server; an artificial intelligence machine learning enabler, AIMLE; an application data analytics enablement, ADAE, client; an ADAE server; a UE modem functionality; or an analytics data repository function, ADRF.

8. The first network entity of any one of the preceding claims, wherein the at least one processor is further configured to cause the first network entity to: calculate, based at least partly on the energy data, an energy consumption and / or efficiency metric.

9. The first network entity of any one of the preceding claims, wherein the analytics comprise application layer analytics.

10. The first network entity of any one of the preceding claims, wherein the at least one UE comprises at least one of: an application function; an application enabler client; an AIMLE client; a VAL UE, optionally a VAL UE client; an operating system, OS; and / orDocket No. SMM920250103-GR-NPa modem.

11. The first network entity of any one of the preceding claims, wherein: the first network entity comprises at least one of an ADAE server; an application enabler server; an AIMLE server; and / or an application function; and / or the second network entity comprises at least one of a VAL server; and / or an AIMLE server.

12. The first network entity of any one of the preceding claims, wherein the first message comprises at least one of an energy capability attribute; an energy status attribute; an energy analytics reporting criteria; or an energy analytics event identifier.

13. The first network entity of any one of the preceding claims, wherein the first message is configured to identify the analytics as for supporting an AI / ML task.

14. A second 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 second network entity to: transmit, to a first network entity, a first message indicating an analytics request associated with an energy capability of at least one UE; and receive, from the first network entity, a second message indicating an analytics associated with the energy capability of the at least one UE.Docket No. SMM920250103-GR-NP15. The second network entity of claim 14, wherein the at least one processor is further configured to: select or remove, based at least in part on the analytics, one or more UE of the at least one UE as an AI / ML member of an AI / ML task.

16. A method performed or performable by a first network entity, the method comprising: receiving, from a second network entity, a first message indicating an analytics request associated with an energy capability of at least one UE; determining, in response to receiving the analytics request and based on an energy data, analytics associated with the energy capability of the at least one UE; and transmitting, to the second network entity, a second message indicating the analytics associated with the energy capability of the at least one UE.

17. The method of claim 16, further comprising: determining at least one data producer or repository for producing or providing the energy data; transmitting, to the at least one data producer or repository, a third message indicating a requirement for the energy data; and receiving, from the at least one data producer or repository, a fourth response comprising the energy data.

18. The method of any one of claims 16-17, further comprising: calculating, based at least partly on the energy data, an energy consumption and / or efficiency metric.

19. A method performed or performable by a second network entity, comprising: transmitting, to a first network entity, a first message indicating an analytics request associated with an energy capability of at least one UE; and receiving, from the first network entity, a second message indicating the analytics associated with the energy capability of the at least one UE.Docket No. SMM920250103-GR-NP20. The method of claim 19, further comprising: selecting or removing, based at least in part on the analytics, one or more UEs of the at least one UE as an AI / ML member of an AI / ML task.Docket No. SMM920250103-GR-NP

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