Method for resource-aware distributed learning in a wireless communication system

Abstract

The present disclosure describes methods of using the pre-configured AI / ML (artificial intelligence / machine learning) based model alignment in wireless mobile communication system including base station e.g., gNB, TN, NTN and mobile station e.g., UE. When AI / ML model is applied to radio access network, signaling of model information exchange can be mismatched resulting in model operation failure. Therefore, model operation, e.g., model training / inferencing / monitoring / updating, can be set up between network and UE by configuring model alignment with assignment of model identification.

Description

[0001] TITLE

[0002] Method for resource-aware distributed learning in a wireless communication system

[0003] TECHNNICAL FIELD

[0004] The present disclosure relates to AI / ML based model alignment, where techniques for pre-configuring and signaling the specific information about aligning ML models applicable to radio access network are presented.

[0005] BACKGROUND

[0006] In 3GPP (Third Generation Partnership Project), one of the selected study items as the approved Release 18 package is AI / ML (artificial intelligence / machine learning) as described in the related document (RP-213599) addressed in 3GPP TSG (Technical Specification Group) RAN (Radio Access Network) meeting #102. The official title of AI / ML study item is “Study on AI / ML for NR Air Interface”. The goal of this study item is to identify a common AI / ML framework and areas of obtaining gains using AI / ML based techniques with use cases.

[0007] According to 3GPP, the main objective of this study item is to study AI / ML framework for air-interface with target use cases by considering performance, complexity, and potential specification impact. In particular, AI / ML model, terminology and description to identify common and specific characteristics for framework are included as one of key work scopes. Regarding AI / ML framework, various aspects are under consideration for investigation and one of key items is about lifecycle management of AI / ML model where multiple stages are included as mandatory for model training, model deployment, model inference, model monitoring, model updating etc. Also, in 3GPP, two-sided (AI / ML) model is defined as a paired AI / ML model(s) over which joint inference is performed, where joint inference comprises AI / ML Inference whose inference is performed jointly across the UE and the network. Also, for one-sided (AI / ML) model, UE-side (AI / ML) model is defined as an AI / ML model whose inference is performed entirely at the UE and network-side (AI / ML) model is defined as an AI / ML model whose inference is performed entirely at the network. Currently, AI / ML specification work is at the stage of work item discussion for Release 19. Earlier, in 3GPP TR 37.817 for Release 17, titled as Study on enhancement for Data Collection for NR and EN-DC, UE (user equipment) mobility was also considered as one of AI / ML use cases and one of scenarios for model training / inference is that both functions are located within RAN node. Followingly, in Release 18 the new work item of “Artificial Intelligence (Al) / Machine Learning (ML) for NG-RAN” was initiated to specify data collection enhancements and signaling support within existing NG-RAN interfaces and architecture. For the above active standardization works, RAN-based AI / ML model is considered very significant for both network and UE to meet any desired model operations (e.g., model training, inference, selection, switching, update, monitoring, etc.). Model information can be signaled to pair both networkside and UE-side models for various lifecycle management (LCM) operations.

[0008] In RAN#102 plenary meeting, a new WID on AI / ML for air-interface was approved for Release 19, where the working objectives on AI / ML general framework are given describing the collection of UE-sided model training data. For the FS_NR_AIML_Air study use cases, identify the corresponding contents of UE data collection. Analyse the UE data collection mechanisms identified during the FS_NR_AIML_Air (TR 38.843 section 7.2.1 .3.2) study along with the implications and limitations of each of the methods.

[0009] For UE data collection, the first issue is whether the UE data collection should be transparent or not. The UE data collection takes some computing cycles and memories from UE. For example, the UE data collection for AI / ML based CSI compression and prediction could take some CPUs.

[0010] Currently in Rel-19 Wl there are only 3 use cases in consideration:

[0011] 1 . CSI Compression and prediction.

[0012] 2. Beam management.

[0013] 3. AIML Assisted Positioning.

[0014] WO 2022233511 A2 titled "EFFICIENT FEDERATED-LEARNING MODEL TRAINING IN WIRELESS COMMUNICATION SYSTEM” discloses measures for enabling / realizing efficient model training, including model collection and / or aggregation, for federated learning, including hierarchical federated learning, in a wireless communication system. Such measures exemplarily comprise that a federated-learning training host configured for local model training decides on how to perform the local model training depending on availability of a cluster head and computation and communication costs for a federated-learning training task, and either locally performs the local model training or delegates at least part of a federated-learning training task to the cluster head. Also, such measures exemplarily comprise that a federated-learning training host configured for local model training computes a similarity metric between a locally computed set of local model parameters and each the received sets of local model parameters, and decides on whether to operate as a temporary cluster head for one or more federated-learning training hosts.

[0015] IN 202341061007 A titled "DISTRIBUTED EDGE LEARNING WITH LOW POWER DEVICES IN UAVENABLED IOT FOR 5G NETWORKS” discloses an environment for edge learning in 5G Networks.

[0016] US 2022036123 A1 titled "MACHINE LEARNING MODEL SCALING SYSTEM WITH ENERGY EFFICIENT NETWORK DATA TRANSFER FOR POWER AWARE HARDWARE” discloses machine learning model swap (MLMS) framework for that selects and interchanges machine learning (ML) models in an energy and communication efficient way while adapting the ML models to real time changes in system constraints. The MLMS framework includes an ML model search strategy that can flexibly adapt ML models for a wide variety of compute system and / or environmental changes. Energy and communication efficiency is achieved by using a similarity-based ML model selection process, which selects a replacement ML model that has the most overlap in pre-trained parameters from a currently deployed ML model to minimize memory write operation overhead.

[0017] WO 2023209577 A1 titled "ML MODEL SUPPORT AND MODEL ID HANDLING BY UE AND NETWORK” discloses indicating and configuring machine learning (ML) model support in a user equipment (UE) in a network. A UE can provide model support information to a node, including a ML type and / or version information of at least one model associated with a certain functionality. A network node uses the support information, and optionally previous model ID allocation information in current or other nodes, to determine at least one model ID, and signals it to the UE. The model ID refers to the model in subsequent model handling-related signaling between the UE and the node. The node assigns the model ID such that the UE and the node are aware that a given model ID refers to a given model or model version (e.g., the mapping is unique for the UE). The model ID can also be unique within a functional area, for example CSI reporting.

[0018] US 2022004929 A1 titled "On-Device Machine Learning Platform” discloses systems and methods for on-device machine learning. In particular, the present disclosure is directed to an on-device machine learning platform and associated techniques that enable on-device prediction, training, example collection, and / or other machine learning tasks or functionality. The on-device machine learning platform can include a context provider that securely injects context features into collected training examples and / or client-provided input data used to generate predictions / inferences. Thus, the on-device machine learning platform can enable centralized training example collection, model training, and usage of machine-learned models as a service to applications or other clients.

[0019] US 2020388035 A1 titled "SYSTEMS AND METHODS FOR MEDICAL ACQUISITION PROCESSING AND MACHINE LEARNING FOR ANATOMICAL ASSESSMENT” discloses systems and methods for determining anatomy directly from raw medical acquisitions using a machine learning system. One method includes obtaining raw medical acquisition data from transmission and collection of energy and particles traveling through and originating from bodies of one or more individuals; obtaining a parameterized model associated with anatomy of each of the one or more individuals; determining one or more parameters for the parameterized model, wherein the parameters are associated with the raw medical acquisition data; training a machine learning system to predict one or more values for each of the determined parameters of the parameterized model, based on the raw medical acquisition data; acquiring a medical acquisition for a selected patient; and using the trained machine learning system to determine a parameter value for a patientspecific parameterized model of the patient. US 2020401939 A1 titled "SYSTEMS AND METHODS FOR PREPARING DATA FOR USE BY MACHINE LEARNING ALGORITHMS” discloses, that historical data used to train machine learning algorithms can have thousands of records with hundreds of fields and inevitably includes faulty data that affects the accuracy and utility of a primary model machine learning algorithm. To improve dataset integrity, it is segregated into a clean dataset having no invalid data values and a faulty dataset having invalid data values. The clean dataset is used to produce a secondary model machine learning algorithm trained to generate from plural complete data records a replacement value for a single invalid data value in a data record, and a tertiary model machine learning clustering algorithm trained to generate from plural complete data records replacement values for multiple invalid data values. Substituting the replacement data values for invalid data values in the faulty dataset creates augmented training data which is combined with clean data to train a more accurate and useful primary model.

[0020] US 2023022050 A1 titled "AIML-BASED CONTINUOUS DELIVERY FOR

[0021] NETWORKS” discloses an application in a distributed computing environment. Telemetry data is collected that corresponds with the deployment of an application. The telemetry data is received by a machine learning model that was trained with test telemetry data to determine whether the deploying is successful or failed. A successful inference results in continued deployment and a failed inference results in a rollback of the application.

[0022] Advanced, distributed learning methods, such as Split learning, Federated learning, etc., are expected to enhance network operations as well as performance. However, UEs face constraints regarding energy as well as computational resources and capabilities. For advanced and distributed AI / ML, efficient use of those limited resources and capabilities is crucial and of utmost importance. Currently, the network cannot determine whether and to what extent UE supports distributed learning or not. As a result, NW would randomly select UE for participating in distributed learning, which would result in non-convergence of AI / ML model The cited problem is solved by a new proposed UE capability, where UE indicates to NW, whether it supports distributed learning or not. If UE supports distributed learning, then it provides assistance information to the NW on whether it wants to participate in distributed learning or not.

[0023] The network is aware of UEs, which can participate in distributed learning as well as their capabilities, and can configure distributed learning accordingly, therefore the signaling overhead is reduced, which results in UE energy savings.

[0024] Prior art showed the contributions related to key areas of the selected taxonomy. The listed prior art contained various methods of AI / ML based applications for federated learning. However, the proposed idea has differentiated mechanisms accounting for UE constraints.

[0025] The present disclosure solves the cited problem by the proposed embodiments and describes as first aspect a method for resource-aware distributed learning in a wireless communication system, whereby the UE process the steps: a) The UE checks if UE capability request is received b) If the check in step a is correct, the UE Indicates distributed learning capabilities c) The UE checks if the capability reporting configuration is received d) If the check in step c is correct, the UE checks is the reporting criteria are met e) If the check in step d is correct, the UE reports updates of distributed learning capabilities.

[0026] In some embodiments of the method according to the first aspect the method is characterized by, that distributed learning is federated and / or split or any of the methods that comprise of a global model being trained ex-situ in smaller parts.

[0027] In some embodiments of the method according to the first aspect the method is characterized by, that distributed learning is federated and / or split learning and using L1 / L2 / L3 UEAssistancelnformation message.

[0028] In some embodiments of the method according to the first aspect the method is characterized by, that UE determines to change its indication based on implementation, wherein the implementation uses dataset threshold, dataset availability, energy level and / or application layer configuration, and sends updates periodically or non-periodically.

[0029] In some embodiments of the method according to the first aspect the method is characterized by, that dataset threshold is configured by gNB through UE-specific or system information message.

[0030] The present disclosure solves the cited problem by the proposed embodiments and describes as second aspect for resource-aware distributed learning in a wireless communication system, whereby the gNB process the steps: a. gNB indicates support for distributed learning b. gNB enquires UEs for support of distributed learning c. gNB provides configuration for distributed learning and UE capability reporting. d. gNB checks if report(s) of distributed learning UE capability is received e. if step d is correct, gNB adjusts distributed learning configuration and / or configuration for distributed learning UE capability reporting.

[0031] In some embodiments of the method according to the second aspect the method is characterized by, that gNB configures UE to report updates, e.g., periodically, based on UE-specific message or system information message.

[0032] In some embodiments of the method according to the second aspect the method is characterized by, that periodic reporting is triggered based on periodicity configured by gNB.

[0033] In some embodiments of the method according to the second aspect the method is characterized by, that the aperiodic reporting is triggered based on UE implementation.

[0034] In some embodiments of the method according to the second aspect the method is characterized by, that the UE implementation checks whether the energy status of the UE is low or the buffer of the UE is full. In some embodiments of the method according to the second aspect the method is characterized by, that the UE implementation checks if UE isn't willing to send any UL data and / or UE check is based on UE mobility.

[0035] According to a third aspect, the present disclosure relates to an apparatus for resource-aware distributed learning in a wireless communication system, the apparatus comprising a wireless transceiver, a processor coupled with a memory in which computer program instructions are stored, said instructions being configured to carry out the steps according to the first aspect.

[0036] According to a fourth aspect, the present disclosure relates to an apparatus for resource-aware distributed learning in a wireless communication system, the apparatus comprising a wireless transceiver, a processor coupled with a memory in which computer program instructions are stored, said instructions being configured carry out the steps according to the second aspect.

[0037] According to a fifth aspect the present disclosure solves the cited problem by the proposed embodiments and described by a user equipment comprising an apparatus according to the third aspect.

[0038] According to a sixth aspect the present disclosure solves the cited problem by the proposed embodiments and described by gNB comprising an apparatus according to the fourth aspect.

[0039] According to a seventh aspect, the present disclosure relates to wireless communication for resource-aware distributed learning, wherein the wireless communication systems comprises at least a user equipment according to the fifth aspect, at least a gNB according to the sixth aspect, whereby the user Equipment and the gNB each comprises a processor coupled with a memory in which computer program instructions are stored and carry out the steps of the first aspect for the UEs and the second aspect for gNB.

[0040] According to a eight aspect, the present disclosure relates to a computer program product comprising instructions which, when executed by at least one processor, configure said at least one processor to carry out a method according to the first aspect and / or said at least one processor to carry out a method for exchanging data according to any one of the embodiments of the present disclosure. The computer program product can use any programming language, and can be in the form of source code, object code, or in any intermediate form between source code and object code, such as in a partially compiled form, or in any other desirable form.

[0041] According to a nineth aspect, the present disclosure relates to a computer-readable storage medium comprising instructions which, when executed by at least one processor, configure said at least one processor to carry out a method according to any one of the embodiments of the present disclosure.

[0042] BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 is an exemplary flow for the UE side

[0044] Figure 2 is an exemplary flow chart for the gnB side

[0045] DETAILED DESCRIPTION

[0046] The detailed description set forth below, with reference to annexed drawings, is intended as a description of various configurations and is not intended to represent only the configurations in which the concepts described herein may be practiced. The detailed description includes specific details for the purpose of providing a thorough understanding of the various concepts. However, it will be apparent to those skilled in the art that these concepts may be practiced without these specific details. In particular, although terminology from 3GPP 5G NR may be used in this disclosure to exemplify embodiments herein, this should not be seen as limiting the scope of the invention.

[0047] Some of the embodiments contemplated herein will now be described more fully with reference to the accompanying drawings. Other embodiments, however, are contained within the scope of the subject matter disclosed herein, the disclosed subject matter should not be construed as limited to only the embodiments set forth herein; rather, these embodiments are provided by way of example to convey the scope of the subject matter to those skilled in the art.

[0048] Generally, all terms used herein are to be interpreted according to their ordinary meaning in the relevant technical field, unless a different meaning is clearly given and / or is implied from the context in which it is used. All references to a / an / the element, apparatus, component, means, step, etc. are to be interpreted openly as referring to at least one instance of the element, apparatus, component, means, step, etc., unless explicitly stated otherwise. The steps of any methods disclosed herein do not have to be performed in the exact order disclosed, unless a step is explicitly described as following or preceding another step and / or where it is implicit that a step must follow or precede another step. Any feature of any of the embodiments disclosed herein may be applied to any other embodiment, wherever appropriate. Likewise, any advantage of any of the embodiments may apply to any other embodiments, and vice versa. Other objectives, features and advantages of the enclosed embodiments will be apparent from the following description.

[0049] In some embodiments, a more general term “network node” may be used and may correspond to any type of radio network node or any network node, which communicates with a UE (directly or via another node) and / or with another network node. Examples of network nodes are NodeB, MeNB, ENB, a network node belonging to MCG or SCG, base station (BS), multi-standard radio (MSR) radio node such as MSR BS, eNodeB, gNodeB, network controller, radio network controller (RNC), base station controller (BSC), relay, donor node controlling relay, base transceiver station (BTS), access point (AP), transmission points, transmission nodes, RRU, RRH, nodes in distributed antenna system (DAS), core network node (e.g. Mobile Switching Center (MSC), Mobility Management Entity (MME), etc.), Operations & Maintenance (O&M), Operations Support System (OSS), Self Optimized Network (SON), positioning node (e.g. Evolved- Serving Mobile Location Centre (E-SMLC)), Minimization of Drive Tests (MDT), test equipment (physical node or software), etc. In some embodiments, the non-limiting term user equipment (UE) or wireless device may be used and may refer to any type of wireless device communicating with a network node and / or with another UE in a cellular or mobile communication system. Examples of UE are target device, device to device (D2D) UE, machine type UE or UE capable of machine to machine (M2M) communication, PDA, PAD, Tablet, mobile terminals, smart phone, laptop embedded equipped (LEE), laptop mounted equipment (LME), USB dongles, UE category Ml, UE category M2, ProSe UE, V2V UE, V2X UE, etc.

[0050] Additionally, terminologies such as base station / gNodeB and UE should be considered non-limiting and do in particular not imply a certain hierarchical relation between the two; in general, “gNodeB” could be considered as device 1 and “UE” could be considered as device 2 and these two devices communicate with each other over some radio channel. And in the following the transmitter or receiver could be either gNodeB (gNB), or UE.

[0051] As will be appreciated by one skilled in the art, aspects of the embodiments may be embodied as a system, apparatus, method, or program product. Accordingly, embodiments may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro-code, etc.) or an embodiment combining software and hardware aspects.

[0052] For example, the disclosed embodiments may be implemented as a hardware circuit comprising custom very-large-scale integration (“VLSI”) circuits or gate arrays, off- the-shelf semiconductors such as logic chips, transistors, or other discrete components. The disclosed embodiments may also be implemented in programmable hardware devices such as field programmable gate arrays, programmable array logic, programmable logic devices, or the like. As another example, the disclosed embodiments may include one or more physical or logical blocks of executable code which may, for instance, be organized as an object, procedure, or function. Furthermore, embodiments may take the form of a program product embodied in one or more computer readable storage devices storing machine readable code, computer readable code, and / or program code, referred hereafter as code. The storage devices may be tangible, non- transitory, and / or non-transmission. The storage devices may not embody signals. In a certain embodiment, the storage devices only employ signals for accessing code.

[0053] Any combination of one or more computer readable medium may be utilized. The computer readable medium may be a computer readable storage medium. The computer readable storage medium may be a storage device storing the code. The storage device may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, holographic, micromechanical, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing.

[0054] More specific examples (a non-exhaustive list) of the storage device would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random-access memory (“RAM”), a read-only memory (“ROM”), an erasable programmable read-only memory (“EPROM” or Flash memory), a portable compact disc read-only memory (“CD-ROM”), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device.

[0055] Code for carrying out operations for embodiments may be any number of lines and may be written in any combination of one or more programming languages including an object- oriented programming language such as Python, Ruby, Java, Smalltalk, C++, or the like, and conventional procedural programming languages, such as the “C” programming language, or the like, and / or machine languages such as assembly languages. The code may execute entirely on the user’s computer, partly on the user’s computer, as a stand-alone software package, partly on the user’s computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user’s computer through any type of network, including a local area network (“LAN”), wireless LAN (“WLAN”), or a wide area network (“WAN”), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider (“ISP”)).

[0056] Furthermore, the described features, structures, or characteristics of the embodiments may be combined in any suitable manner. In the following description, numerous specific details are provided, such as examples of programming, software modules, user selections, network transactions, database queries, database structures, hardware modules, hardware circuits, hardware chips, etc., to provide a thorough understanding of embodiments. One skilled in the relevant art will recognize, however, that embodiments may be practiced without one or more of the specific details, or with other methods, components, materials, and so forth. In other instances, well-known structures, materials, or operations are not shown or described in detail to avoid obscuring aspects of an embodiment. Reference throughout this specification to “one embodiment,” “an embodiment,” or similar language means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment. Thus, appearances of the phrases “in one embodiment,” “in an embodiment,” and similar language throughout this specification may, but do not necessarily, all refer to the same embodiment, but mean “one or more but not all embodiments” unless expressly specified otherwise. The terms “including,” “comprising,” “having,” and variations thereof mean “including but not limited to,” unless expressly specified otherwise. An enumerated listing of items does not imply that any or all of the items are mutually exclusive, unless expressly specified otherwise. The terms “a,” “an,” and “the” also refer to “one or more” unless expressly specified otherwise.

[0057] Aspects of the embodiments are described below with reference to schematic flowchart diagrams and / or schematic block diagrams of methods, apparatuses, systems, and program products according to embodiments. It will be understood that each block of the schematic flowchart diagrams and / or schematic block diagrams, and combinations of blocks in the schematic flowchart diagrams and / or schematic block diagrams, can be implemented by code. This code may be provided to a processor of a general-purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions / acts specified in the flowchart diagrams and / or block diagrams.

[0058] The code may also be stored in a storage device that can direct a computer, other programmable data processing apparatus, or other devices to function in a particular manner, such that the instructions stored in the storage device produce an article of manufacture including instructions which implement the function / act specified in the flowchart diagrams and / or block diagrams.

[0059] The code may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus, or other devices to produce a computer implemented process such that the code which execute on the computer or other programmable apparatus provide processes for implementing the functions / acts specified in the flowchart diagrams and / or block diagrams.

[0060] The flowchart diagrams and / or block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of apparatuses, systems, methods, and program products according to various embodiments. In this regard, each block in the flowchart diagrams and / or block diagrams may represent a module, segment, or portion of code, which includes one or more executable instructions of the code for implementing the specified logical function(s).

[0061] It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the Figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. Other steps and methods may be conceived that are equivalent in function, logic, or effect to one or more blocks, or portions thereof, of the illustrated Figures. Although various arrow types and line types may be employed in the flowchart and / or block diagrams, they are understood not to limit the scope of the corresponding embodiments. Indeed, some arrows or other connectors may be used to indicate only the logical flow of the depicted embodiment. For instance, an arrow may indicate a waiting or monitoring period of unspecified duration between enumerated steps of the depicted embodiment. It will also be noted that each block of the block diagrams and / or flowchart diagrams, and combinations of blocks in the block diagrams and / or flowchart diagrams, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and code.

[0062] The description of elements in each figure may refer to elements of proceeding figures. Like numbers refer to like elements in all figures, including alternate embodiments of like elements.

[0063] The detailed description set forth below, with reference to the figures, is intended as a description of various configurations and is not intended to represent the only configurations in which the concepts described herein may be practiced. The detailed description includes specific details for the purpose of providing a thorough understanding of the various concepts. However, it will be apparent to those skilled in the art that these concepts may be practiced without these specific details. For instance, although 3GPP terminology, from e.g., 5G NR, may be used in this disclosure to exemplify embodiments herein, this should not be seen as limiting the scope of the present disclosure.

[0064] The disclosure is related to wireless communication systems, which may be for example a 5G NR wireless communication system. More specifically, it represents a RAN of the wireless communication system, which is used to exchange data with UEs via radio signals. For example, the RAN may send data to the UEs (downlink, DL), for instance data received from a core network (CN). The RAN may also receive data from the UEs (uplink, UL), which data may be forwarded to the CN. In the examples illustrated, the RAN comprises one base station, BS. Of course, the RAN may comprise more than one BS to increase the coverage of the wireless communication system. Each of these BSs may be referred to as NB, eNodeB (or eNB), gNodeB (or gNB, in the case of a 5G NR wireless communication system), an access point or the like, depending on the wireless communication standard(s) implemented. The UEs is located in a coverage of the BS. The coverage of the BS corresponds for example to the area in which UEs can decode a PDCCH transmitted by the BS.

[0065] An example of a wireless device suitable for implementing any method, discussed in the present disclosure, performed at a UE corresponds to an apparatus that provides wireless connectivity with the RAN of the wireless communication system, and that can be used to exchange data with said RAN. Such a wireless device may be included in the UE. The UE may for instance be a cellular phone, a wireless modem, a wireless communication device, a handheld device, a laptop computer, or the like. The UE may also be an Internet of Things (loT) equipment, like a wireless camera, a smart sensor, a smart meter, smart glasses, a vehicle (manned or unmanned), a global positioning system device, etc., or any other equipment that may run applications that need to exchange data with remote recipients, via the wireless device.

[0066] The wireless device comprises one or more processors and one or more memory. The one or more processors may include for instance a central processing unit (CPU), a digital signal processor (DSP), a field-programmable gate array (FPGA), an application specific integrated circuit (ASIC), etc. The one or more memories may include any type of computer readable volatile and non-volatile memories (magnetic hard disk, solid- state disk, optical disk, electronic memory, etc.). The one or more memories may store a computer program product, in the form of a set of program-code instructions to be executed by the one or more processors to implement all or part of the steps of a method for exchanging data, performed at a UE’s side, according to any one of the embodiments disclosed herein.

[0067] The wireless device can comprise also a main radio, MR, unit. The MR unit corresponds to a main wireless communication unit of the wireless device, used for exchanging data with BSs of the RAN using radio signals. The MR unit may implement one or more wireless communication protocols, and may for instance be a 3G, 4G, 5G, NR, WiFi, WiMax, etc. transceiver or the like. In preferred embodiments, the MR unit corresponds to a 5G NR wireless communication unit.

[0068] The following explanation will provide a detailed description of the mechanism about pre-configuring and signaling the specific information about model online training by configuring a set of UE behaviors. AI / ML based techniques are currently applied to many different applications and 3GPP also started to work on its technical investigation to apply to multiple use cases based on the observed potential gains. AI / ML lifecycle can be split into several stages such as data collection / pre- processing, model training, model testing / validation, model deployment / update, model monitoring etc., where each stage is equally important to achieve target performance with any specific model(s).

[0069] When AI / ML model enabled wireless communication network is deployed, it is then important to consider how to handle AI / ML model in activation with re-configuration for wireless devices under operations such as model training, inference, updating, etc.. Model(s) for activation must be aligned between network side and UE side using model ID information. If not aligned, activation of model(s) can fail due to the mismatched ML conditions and / or ML operation configuration between network side and UE side.

[0070] Figure 1 is an exemplary flow for the UE side

[0071] UE sends its distributed learning capabilities to the network (e.g., UECapability message), where UE indicates capabilities based on device characteristics (e.g., computational power, supported AI / ML frameworks or models). UE indicates intention to participate in distributed learning (e.g., federated, split learning), e.g., using L1 / L2 / L3 UEAssistancelnformation message. UE determines to change its indication based on implementation (e.g., dataset threshold, energy level, application layer configuration) and sends updates periodically or non-periodically. Where dataset threshold is configured by gNB through UE-specific or system information message

[0072] When the UE is requested by the gNB / network to provide its capabilities, the UE indicates its capabilities, e.g., based on its device characteristics (e.g., computational power, supported AI / ML frameworks or models).

[0073] When the gNB / network provides a reporting configuration, the UE applies the received configuration for checking whether the configured reporting criteria are met. For example, the gNB / network can configure the UE to provide periodic or aperiodic updates of its distributed learning capabilities.

[0074] When the configured reporting criteria are met, the UE provides a distributed learning capability update to the gNB / network.

[0075] The method for resource-aware distributed learning in a wireless communication system according to Fig. 1 is characterized by, that the UE process the steps: a. The UE checks, if UE capability request is received; b. If the check in step a is correct, the UE Indicates distributed learning capabilities; c. The UE checks, if the capability reporting configuration is received; d. If the check in step c is correct, the UE checks, if the reporting criteria are met; e. If the check in step d is correct, the UE reports updates of distributed learning capabilities.

[0076] The method according to claim 1 , wherein distributed learning is federated or split or any of the methods that comprise of a global model being trained ex-situ in smaller parts. Furthermore, UE determines to change its indication based on implementation, wherein the implementation uses dataset threshold and / or energy level and / or application layer configuration and sends updates periodically or non-periodically. Furthermore, dataset threshold is configured by gNB through UE-specific or system information message.

[0077] Figure 2 is an exemplary flow chart for the gnB side. Method for resource-aware distributed learning in a wireless communication system is characterized by the gNB processing the steps: gNB indicates support for distributed learning; gNB enquires UEs for support of distributed learning; gNB provides configuration for distributed learning and UE capability reporting; gNB checks, if report(s) of distributed learning UE capability is received; if step d is correct, gNB adjusts distributed learning configuration and / or configuration for distributed learning UE capability reporting.

[0078] Furthermore, gNB configures UE to report updates, periodically, based on UE- specific message or system information message and furthermore periodic reporting is triggered based on periodicity configured by gNB.

[0079] The aperiodic reporting is triggered based on UE implementation. One possibility of the UE implementaion is that the UE implementation checks whether the energy status of the UE is low. In addition, the UE implementation checks, if UE isn't willing to send any UL data, has no data to send, and / or UE check is based on UE mobility. This means gNB configures UE to report updates, e.g., periodically, based on UE- specific or system information message. Periodic reporting is triggered based on periodicity configured by gNB. Aperiodic reporting is triggered based on UE implementation (e.g., if energy status is low, if UE does not want to send any UL data, based on UE mobility).

[0080] The gNB / network indicates support for distributed learning and the gNB / network enquires UE(s) for their distributed learning capabilities. Based on the received distributed learning UE capabilities, the gNB / network determines and provides configuration(s) for distributed learning and UE capability reporting to the UE(s). When the gNB / network receives UE report(s) of distributed learning capability update(s), the gNB / network adjusts the distributed learning configuration(s) and / or the configuration(s) for distributed learning UE capability reporting.

[0081] Dataset threshold can be defined as, e.g., “the minimum number of datapoints a device can collect to be eligible to participate in split learning”.

[0082] The UE signaling is done by for L1 , 1 bit (participation in distributed learning) in the existing AI / ML related physical UL control channel (e.g., PUCCH), for L2, use new logical channel ID that carries such information in the MAC CE. for L3, in RRC (e.g., UEAssistancelnformation) message. This means the network is aware of UE capabilities and can configure distributed learning accordingly. Signaling overhead can be significantly reduced. This translates into UE energy savings.

[0083] One preferred embodiment is an apparatus for method for resource-aware distributed learning in a wireless communication system, the apparatus comprising a wireless transceiver, a processor coupled with a memory in which computer program instructions are stored, said instructions being configured to implement steps of the method according to the steps defined in claims 1 to 4.

[0084] Another preferred embodiment is and apparatus for method for resource-aware distributed learning in a wireless communication system, the apparatus comprising a wireless transceiver, a processor coupled with a memory in which computer program instructions are stored, said instructions being configured to implement steps of the method according to steps defined in claims 5 to 10.

[0085] Another preferred embodiment is a user Equipment comprising an apparatus according to claim 11 .

[0086] Another embodiment is a gNB comprising an apparatus according to claim 12. Another embodiment is a wireless communication system for resource-aware distributed learning, wherein the wireless communication systems comprise at least a user equipment according to claim 13, at least a gNB according to claim 14, whereby the user Equipment and the gNB each comprises a processor coupled with a memory in which computer program instructions are stored.

Claims

CLAIMS1. Method for advanced model assignment signalling in a wireless communication system, whereby the UE performs the steps: a) The UE checks, if UE capability request is received; b) If the check in step a is correct, the UE indicates distributed learning capabilities; c) The UE checks, if the capability reporting configuration is received; d) If the check in step c is correct, the UE checks, if the reporting criteria are met; e) If the check in step d is correct, the UE reports updates of distributed learning capabilities.

2. The method according to claim 1 , wherein distributed learning is federated or split or any of the methods that comprise of a global model being trained ex- situ in smaller parts3. The method according to one of previous claims, wherein the UE determines to change its indication based on implementation, wherein the implementation uses dataset threshold, dataset availability , energy level and / or application layer configuration, and sends updates periodically or non-periodically.

4. The method according to one of previous claims, wherein dataset threshold is configured by gNB through UE-specific or system information message.

5. Method for advanced model assignment signaling in a wireless communication system, whereby the gNB performs the steps: a. The gNB indicates support for distributed learning; b. gNB enquires UEs for support of distributed learning; c. gNB provides configuration for distributed learning and UE capability reporting; d. gNB checks, if report(s) of distributed learning UE capability is received; e. if step d is correct, gNB adjusts distributed learning configuration and / or configuration for distributed learning UE capability reporting.

6. The method according to claim 5, wherein gNB configures UE to report updates, e.g., periodically, based on UE-specific message or system information message.

7. The method according to one of previous claims 5 or 6, wherein periodic reporting is triggered based on periodicity configured by gNB.

8. The method according to one of previous claims 5 or 7, wherein the aperiodic reporting is triggered based on UE implementation.

9. The method according to one of previous claims 5 to 8, wherein the UE implementation checks whether the energy status of the UE is low or the buffer of the UE is full.

10. The method according to one of previous claims 5 to 9, wherein the UE implementation checks, if UE isn't willing to send any UL data, has data to send, and / or UE check is based on UE mobility.11 .Apparatus for method for resource-aware distributed learning in a wireless communication system, the apparatus comprising a wireless transceiver, a processor coupled with a memory in which computer program instructions are stored, said instructions being configured to implement steps of the claims 1 to 4.

12. Apparatus for Method for resource-aware distributed learning communication system, the apparatus comprising a wireless transceiver, a processor coupled with a memory in which computer program instructions are stored, said instructions being configured to implement steps of the claims 5 to 10.

13. User Equipment comprising an apparatus according to claim 11 .

14. gNB comprising an apparatus according to claim 12.

15. Wireless communication for advanced model assignment signaling by configuring model alignment ID or index information to indicate varying model ID assignment, wherein the wireless communication system comprises at least a user equipment according to claim 13, at least a gNB according to claim 14, whereby the user Equipment and the gNB each comprises a processor coupled with a memory in which computer program instructions are stored.

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