System and apparatus for model training in a network and a method in association thereto

Abstract

System (100), apparatus (102) and a method (300) for model training in a network are disclosed. The method (300) includes configuring data indicative of model training; communicating the data to a user device; initiating model training based on the data; and generating a plurality of parameters related to the model training.

Description

SYSTEM AND APPARATUS FOR MODEL TRAINING IN A NETWORK AND A METHOD IN ASSOCIATION THERETOField Of Invention

[0001] The present disclosure generally relates to one or both of a system and an apparatus for model training in a network in association with, for example, a User Equipment (UE) usable for communication. The present disclosure further relates a method which can be associated with the system and / or the apparatus.Background

[0002] Generally, energy efficiency and energy savings would be helpful or desired in communication networks. An example of a communication network would be a 3rd Generation Partnership Project (3GPP) 5G (fifth generation) New Radio (NR) standard-based telecommunications network.

[0003] Typically, conventional techniques for model training, for example Artificial Intelligence Machine Learning (AI / ML) training, the entity (e.g. User Equipment UE or network) executing the model (e.g. AI / ML model) may not be aware of the dataset identification related to a particular model for correct inference.

[0004] The present disclosure contemplates that it would be helpful to address (or at least mitigate) one or more issues in relation to conventional techniques for facilitating energy efficiency and energy savings.Summary of the Invention

[0005] In accordance with a first aspect of the present invention, there is provided a method for model training in a network comprising: configuring data indicative of model training; communicating the data to a user device; initiating model training based on the data; and generating a plurality of parameters related to the model training.

[0006] Advantageously, the method as described herein may optimize networks and provide an energy efficient network by associating model dataset with the model identities and thus avoiding dataset mismatch. The method may also provide fundamental mechanisms of interworking and data information flow in radio access network collaboration for AI / ML support. AI / ML performance for wireless communication can be improved based on the gNB-UE collaboration operation for AI / ML support.

[0007] In an embodiment, the data comprises model identity and dataset identity.

[0008] In an embodiment, the plurality of parameters comprises model identity and associated dataset identity.

[0009] In an embodiment, the method further comprises communicating the plurality of parameters via physical layer (L1 ) signalling and / or data link layer (L2) signalling.

[0010] In an embodiment, the method further comprises communicating the plurality of parameters via a User Equipment (UE) specific message and / or system information block (SIB) based on a model configuration.

[0011] In an embodiment, the method further comprises validating the plurality of parameters for model training and inference.

[0012] In an embodiment, generating the plurality of parameters comprises generating periodically and / or generating based on a pre-configured event.

[0013] In an embodiment, the model training comprises an Artificial Intelligence Machine Learning (AI / ML) model training.

[0014] In an embodiment, communicating the plurality of parameters comprises communicating via at least one of: system information message and / or dedicated RRC message.

[0015] In an embodiment, there is provided a computer program (not shown) which can include instructions which, when the program is executed by a computer (not shown), cause the computer to carry out the method of the first aspect.

[0016] In an embodiment, there is provided a computer readable storage medium having data stored therein representing software executable by a computer, the software including instructions, when executed by the computer, to carry out the method of the first aspect.

[0017] In accordance with a second aspect of the disclosure, there is provided an apparatus for model training in a network comprising a first module configured to receive at least one input signal associated with data indicative of model training; a second module configured to at least one of process and facilitate the method of the first aspect to generate at least one output signal; and a third module configured to communicate at least one output signal, wherein the output signal corresponds to a control signal for model training in the network.

[0018] In an embodiment, the apparatus can correspond to a User Equipment (UE) which can communicate with a device corresponding to a base station. The base station can, for example, correspond to a Next generation Node B (gNB) which can be configured to communicate one or more signals (e.g., input signal(s)) to the UE.

[0019] In an embodiment, there is provided a system comprising one or more apparatuses and one or more devices. The apparatus(es) and the device(s) can, for example, be capable of being coupled via wired coupling and / or wireless coupling.

[0020] Advantageously, the system can allow the UE (user device) or the base station (gNB or network) executing the model (e.g. AI / ML model) to be aware of the dataset identification that is related to that particular model for correct inference. This can ensure dataset consistency during model training and inferencing for a two-sided model (e.g. AI / ML model).Brief Description of the Drawings

[0021] Embodiments of the disclosure are described hereinafter with reference to the following drawings, in which:

[0022] Fig. 1A shows a schematic diagram illustrating a system for model training in a network which can include at least one apparatus, according to an embodiment of the disclosure.

[0023] Fig. 1 B shows an example scenario in association with the system of Fig. 1A, according to an embodiment of the disclosure.

[0024] Fig. 2 shows a schematic diagram illustrating the apparatus of Fig. 1A in further detail, according to an embodiment of the disclosure.

[0025] Fig. 3 shows a method in association with the system of Fig. 1A, according to an embodiment of the disclosure.

[0026] Fig. 4A to Fig. 4D show schematic diagrams illustrating example scenarios in association with the method of Fig. 3, according to an embodiment of the disclosure.Detailed Description

[0027] 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 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. 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.

[0028] In addition, 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.

[0029] 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.

[0030] In some embodiments, the non-limiting term User Equipment (UE) or wireless device or user 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.

[0031] 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 User Equipment (directly or via another node) and / or withanother 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.

[0032] 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.

[0033] 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 similarlanguage 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.

[0034] The following explanation will provide the detailed description of the mechanism about pre-configuring and signaling the specific information about model online training by configuring a set of UE behaviors. Specifically, the present disclosure contemplates that model-based (e.g. AI / ML model) techniques may be currently applied to many different applications and may be applied to multiple use cases based on the observed potential gains. A model (e.g. 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). In applying a model (e.g. AI / ML model) for any use case or application, the present disclosure contemplates that managing the lifecycle of AI / ML model can be one of the challenging issues. This is mainly because the data / model drift occurs during model deployment / inference and can result in performance degradation of AI / ML model. Fundamentally, the dataset statistical changes may occur after the model (e.g. AI / ML model) is deployed and model inference capability may also be impacted with unseen data as input. In a similar aspect, the present disclosure contemplates that the statistical property of dataset and the relationship between input and output for the trained model can be changed with drift occurrence.

[0035] The present disclosure also contemplates that model training or re-training can be one of the key issues for model performance maintenance as model performance such as inferencing and / or training may be dependent on different model execution environment with varying configuration parameters. In order to handle such a situation, the present disclosure contemplates that collaboration between the UE and gNB may be highly important to track model performance and re-configure model corresponding to different environments. The present disclosurethus contemplates that a model (e.g. AI / ML model) may need model monitoring after deployment because model performance may not be maintained continuously due to drift. Update feedback may then be provided to re-train or update the model or select an alternative model. When a model (e.g. AI / ML model) enabled wireless communication network is deployed, it may be 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. As UE radio resource control (RRC) states can vary depending on UE mobility or network / device condition status, any supported AI / ML model at the UE side may be impacted for machine learning (ML) operation. In such a method, a finite set of multi-model combinations in association with UE RRC states (e.g., active, inactive, idle) can be pre-configured and mapping relation table can be generated to indicate different combinations of multiple models to be activated based on ML configuration with varying UE RRC states via SI or dedicated RRC signaling or Layer 1 I Layer 2 (L1 / L2) signaling.

[0036] The present disclosure further contemplates that each combination of multimodel set with different UE RRC states can be updated or re-configured (a)periodically for different model combinations depending on the status of applicable models at UE and / or network status with ML conditions. The activation or deactivation of applying multi-model combinations can be indicated via L1 / L2 or RRC signaling dynamically. In addition, a preset timer can be configured so as to determine activation or deactivation of applying multi-model combinations based on the timer. Furthermore, different combinations of multi-model sets may be configured for each RRC state. For each combination of multi-model sets for activation, different model behaviors can be configured for life cycle management (LCM) operation as two-sided model or one-sided model which can be also associated in the mapping relation information (when necessary) by using index or identity for indication. Therefore, for mapping relation table of multi-model combinations with RRC states, additional information such as model behavior and / or LCM operation description can be associated together as ML assistance information. With regards to the activation of any specific model(s) using multi-model combinations, UE autonomous decision of activating UE model(s) can be performed based on mapping relation of multi-model combinations with RRC states. In addition, network side can send indication message of activating UE model(s) based on mapping relation of multi-modelcombinations with RRC states when UE provides ML measurement or condition updates with the supported models status.

[0037] The present disclosure contemplates model identification can be categorized in the following types for AI / ML model identification of UE-side or UE-part of two- sided models. One example of a model identification type includes type A, where the model may be identified to the network (if applicable) and UE (if applicable) without over-the-air signaling. In this example embodiment, the model may be assigned with a model identity during the model identification, which may be referred or used in over-the-air signaling after model identification.

[0038] A second example of a model identification type may be type B, where the model is identified via over-the-air signaling. A third example may be type B1 where model identification may be initiated by the UE, and the network assists the remaining steps (if any) of the model identification and the model may be assigned with a model identity during the model identification. Yet another example may be type B2 where model identification can be initiated by the network, and the UE responds (if applicable) for the remaining steps (if any) of the model identification. The model may then be assigned with a model identity during the model identification which may not imply that model identification is necessary.

[0039] The present disclosure further contemplates that the following Network-UE collaboration levels may be considered as one aspect for defining collaboration levels. A first collaboration level may be level x where there is no collaboration. A second collaboration level may be level y where there is signalling-based collaboration without model transfer. Such a level can include cases without model delivery. A third collaboration level may be level z where there is signalling-based collaboration with model transfer.

[0040] The present disclosure yet further contemplates that model identification (Ml) with data collection related configuration(s) and / or indication(s) (Mi-Option 1 ) of model identification type B can be further explored in the following aspects. These aspects can include the relationship between model ID and data collection relatedconfiguration(s) and / or indication(s), information transmitted from network to UE (if any), information transmitted from UE to network (if any), any associated procedure and usage and applicable use case(s) of Mi-Option 1 .

[0041] The present disclosure generally contemplates the facilitation of, for example, network (e.g., in association with 3GPP based standard / specification etc.) and / or user equipment (UE) efficiency (e.g., energy / power efficiency), in accordance with an embodiment of the disclosure.

[0042] The present disclosure contemplates the possibility of a method to ensure dataset consistency for training and inferencing for two-sided model (e.g. AI / ML model). In particular, the entity (UE or network) executing the AI / ML model can be aware of the dataset identification related to a particular model for correct inference. In an example embodiment, model identity and dataset identity can be included with the dataset for training and inference for a two-sided model (e.g. AI / ML model).

[0043] In the above manner, power and energy consumption efficiency can be possibly facilitated, in accordance with an embodiment of the disclosure.

[0044] The foregoing will be discussed in further detail with reference to Fig. 1 to Fig. 4 hereinafter.

[0045] Referring to Fig. 1A, a system 100 for model training in a network is shown, according to an embodiment of the disclosure. The system 100 can, for example, be suitable for energy savings and facilitating energy / power efficiency in a network, in accordance with an embodiment of the disclosure.

[0046] As shown, the system 100 can include one or more apparatuses 102, at least one device 104 and, optionally, a communication network 106, in accordance with an embodiment of the disclosure.

[0047] The apparatus(es) 102 can be coupled to the device(s) 104. Specifically, the apparatus(es) 102 can, for example, be coupled to the device(s) 104 via the communication network 106, in accordance with an embodiment of the disclosure.

[0048] In one embodiment, the apparatus(es) 102 can be coupled to the communication network 106 and the device(s) 104 can be coupled to the communication network 106. Coupling can be by manner of one or both of wired coupling and wireless coupling. The apparatus(es) 102 can, in general, be configured to communicate with the device(s) 104 via the communication network 106, according to an embodiment of the disclosure.

[0049] The apparatus(es) 102 can, for example, be associated with / correspond to / include one or more user equipment (UE) which can carry one or more computers, in accordance with an embodiment of the disclosure. For example, an apparatus 102 can correspond to a UE carrying at least one computer (e.g., an electronic device / module having computing capabilities such as an electronic mobile device which can be carried into a vehicle or an electronic module which can be installed in a vehicle, in accordance with an embodiment of the disclosure) which can be configured to perform one or more processing tasks in association with adaptive / dynamic / gradual control, in accordance with an embodiment of the disclosure. In a more specific example, the apparatus(es) 102 can, in one embodiment, include one or more processors (not shown) which can be configured to perform one or more processing tasks in association with dynamic / adaptive / gradual control, in accordance with an embodiment of the disclosure. In one embodiment, the apparatus(es) 102 can, for example, be configured to receive one or more input signals and perform at least one processing task based on the input signal(s) in a manner to generate one or more output signals. The input signal(s) can, for example, be communicated from the device(s) 104 and received by the apparatus(es) 102, in accordance with an embodiment of the disclosure. As a possible option, the output signal(s) can, for example, be communicated from the apparatus(es) 102, in accordance with an embodiment of the disclosure. The apparatus(es) 102 will be discussed later in further detail with reference to Fig. 2, according to an embodiment of the disclosure.

[0050] The device(s) 104 can, for example, be associated with / correspond to at least one base station (e.g., at least one gNB). Moreover, the device(s) 104 can, for example, be configured to carry / be associated with / include one or more computers (e.g., an electronic device / module having computing capabilities) which can, for example, be configured to perform one or more processing tasks in association with the base station. The device(s) 104 can be configured to generate one or more input signals which can be communicated to the apparatus(es) 102, in accordance with an embodiment of the disclosure. This will be discussed later in further detail in the context of an example scenario, in accordance with an embodiment of the disclosure.

[0051] The communication network 106 can, for example, correspond to an Internet communication network, a cellular-based communication network, a wired-based communication network, a Global Navigation Satellite System (GNSS) based communication network, a wireless-based communication network, or any combination thereof. Communication (e.g., between the apparatuses 102 and / or between the apparatus(es) 102 and the device(s) 104) via the communication network 106 can be by manner of one or both of wired communication and wireless communication.

[0052] Earlier mentioned, the apparatus(es) 102 can, for example, be configured to receive at least one input signal and perform at least one processing task in association with dynamic / adaptive / gradual control on the input signal(s) in a manner so as to generate at least one output signal. Moreover, the device(s) 104 can, for example, be configured to generate (and communicate) the input signal(s) to the apparatus(es) 102, in accordance with an embodiment of the disclosure. This will be discussed, in accordance with an embodiment of the disclosure, in the context of an example scenario with reference to Fig. 1 B, hereinafter.

[0053] Fig. 1 B shows an example scenario in association with the system of Fig. 1A, according to an embodiment of the invention. Specifically, Fig. 1 B shows an example embodiment for training of an Artificial Intelligence Machine Learning (AI / ML) model. Referring to the Figure, the gNB (or base station) and corresponding UEs (or user devices) in a network can have the same machine learning model (e.g. AI / ML model). The gNB (or base station) can communicate the relevant model to the UEs (or userdevices) in the network and each of the UEs may then generate and communicate trained weights back to the gNB (or base station). The present disclosure contemplates that AI / ML for air-interface in the AI / ML general framework can have a collection of UE-sided model training data. The framework may further include identifying the corresponding contents of UE data collection and analyzing the UE data collection mechanisms along with the implications and limitations of each of the methods.

[0054] The present disclosure contemplates, as will be discussed further in detail in the context of an example scenario associated with the system 100 in accordance with an embodiment of the disclosure, that it may be helpful to consider some form of dynamic / adaptive / gradual configuration / determination strategy which will aid in power / energy consumption efficiency, in accordance with an embodiment of the disclosure. The dynamic / adaptive / gradual control configuration / determination strategy can, for example, be in relation to dynamic / adaptive / gradual control based on model training in a network, in accordance with an embodiment of the disclosure.

[0055] The above-described advantageous aspect(s) of the system 100 of the present disclosure can also apply analogously (all) the aspect(s) of a below described apparatus 102 of the present disclosure. Likewise, all below described advantageous aspect(s) of the apparatus 102 of the disclosure can also apply analogously (all) the aspect(s) of above described system 100 of the disclosure.

[0056] The aforementioned apparatus(es) 102 will be discussed in further detail with reference to Fig. 2 hereinafter.

[0057] Referring to Fig. 2, an apparatus 102 is shown in further detail in the context of an example implementation 200, according to an embodiment of the disclosure.

[0058] In the example implementation 200, the apparatus 102 can correspond to an electronic module 200a. The electronic module 200a can, in one example, correspond to a mobile device which can, for example, be carried into the vehicle by a user, in accordance with an embodiment of the disclosure. In another example, theelectronic module 200a can correspond to an electronic device which can be installed / mounted in the vehicle, in accordance with an embodiment of the disclosure. In this regard, the electronic module 200a can be considered to be carried by the vehicle (e.g., either carried into the vehicle by a user or installed / mounted in the vehicle).

[0059] It is contemplated that the electronic module 200a can be capable of performing one or more processing tasks in association with adaptive / dynamic / gradual control related processing, in accordance with an embodiment of the disclosure.

[0060] The electronic module 200a can, for example, include a casing 200b. Moreover, the electronic module 200a can, for example, carry any one of a first module 202, a second module 204, a third module 206, or any combination thereof.

[0061] In one embodiment, the electronic module 200a can carry a first module 202, a second module 204 and / or a third module 206. In a specific example, the electronic module 200a can carry a first module 202, a second module 204 and a third module 206, in accordance with an embodiment of the disclosure.

[0062] In this regard, it is appreciable that, in one embodiment, the casing 200b can be shaped and dimensioned to carry any one of the first module 202, the second module 204 and the third module 206, or any combination thereof.

[0063] The first module 202 can be coupled to one or both of the second module 204 and the third module 206. The second module 204 can be coupled to one or both of the first module 202 and the third module 206. The third module 206 can be coupled to one or both of the first module 202 and the second module 204. In one example, the first module 202 can be coupled to the second module 204 and the second module 204 can be coupled to the third module 206, in accordance with an embodiment of the disclosure. Coupling between the first module 202, the second module 204 and / or the third module 206 can, for example, be by manner of one or both of wired coupling and wireless coupling. Each of the first module 202, the second module 204 and the third module 206 can correspond to one or both of ahardware-based module and a software-based module, according to an embodiment of the disclosure.

[0064] In one example, the first module 202 can correspond to a hardware-based receiver which can be configured to receive one or more input signals. The input signal(s) can, for example, be communicated from the device(s) 104 (e.g., a gNB), in accordance with an embodiment of the disclosure.

[0065] The second module 204 can, for example, correspond to a hardware-based processor which can be configured to perform one or more processing tasks (e.g., in a manner so as to generate one or more output signals) as will be discussed later in further detail with reference to Fig. 3, in accordance with an embodiment of the disclosure.

[0066] The third module 206 can correspond to a hardware-based transmitter which can be configured to communicate one or more output signals from the electronic module 200a. The output signal(s) can, for example, include / correspond to one or more instructions / commands / control signals in association with the aforementioned dynamic / adaptive / gradual control configuration / determination strategy so as to facilitate efficiency (e.g., power / energy efficiency and / or communication efficiency), in accordance with an embodiment of the disclosure.

[0067] The present disclosure contemplates the possibility that the first and second modules 202 / 204 can be an integrated software-hardware based module (e.g., an electronic part which can carry a software program / algorithm in association with receiving and processing functions / an electronic module programmed to perform the functions of receiving and processing). The present disclosure further contemplates the possibility that the first and third modules 202 / 206 can be an integrated softwarehardware based module (e.g., an electronic part which can carry a software program / algorithm in association with receiving and transmitting functions / an electronic module programmed to perform the functions of receiving and transmitting). The present disclosure yet further contemplates the possibility that the first and third modules 202 / 206 can be an integrated hardware module (e.g., ahardware-based transceiver) capable of performing the functions of receiving and transmitting.

[0068] The above-described advantageous aspect(s) of the apparatus 102 of the present disclosure can also apply analogously (all) the aspect(s) of a below described processing / communication method of the present disclosure. Likewise, all below described advantageous aspect(s) of the processing / communication method of the disclosure can also apply analogously (all) the aspect(s) of above-described apparatus 102 of the disclosure. It is to be appreciated that these remarks apply analogously to the earlier discussed system 100 of the present disclosure.

[0069] Referring to Fig. 3, a method (also referable to as a processing method) in association with the system 100 is shown, according to an embodiment of the disclosure.

[0070] The method 300 can, for example, be suitable for / capable of facilitating energy efficiency, in accordance with an embodiment of the disclosure.

[0071] The processing method 300 can include any one of an input step 302, a processing step 304 and an output step 306, or any combination thereof, in accordance with an embodiment of the disclosure.

[0072] In one embodiment, the processing method 300 can include the input step 302. In another embodiment, the processing method 300 can include the input step 302 and the processing step 304. In another embodiment, the processing method 300 can include the input step 302, the processing step 304 and the output step 306. In yet another embodiment, the processing method 300 can include the processing step 304 and one or both of the input step 302 and the output step 306. In yet a further embodiment, the processing method 300 can include the input step 302, the processing step 304 and the output step 306. In yet a further additional embodiment, the processing method 300 can include the processing step 304. In yet another further additional embodiment, the processing method 300 can include any one of or any combination of the input step 302, the processing step 304 and the output step 306 (i.e. , the input step 302, the processing step 304 and / or the output step 306).

[0073] With regard to the input step 302, one or more input signal(s) can be received. For example, the input signal(s) can be communicated from the device(s) 104 and can be received by an apparatus 102, in accordance with an embodiment of the disclosure.

[0074] The input step 302 can include receiving at least one input signal associated with data indicative of model training. In an embodiment, the input signal(s) may be generated by the device 104 and transmitted from the device 104 to the apparatus 102. Alternatively, the input signal(s) may be generated and received by the apparatus 102 to advance to the processing step 304. For example, the input signal(s) may be generated by a transmitting UE (or user device) and received by a receiving UE (or user device).

[0075] With regard to the processing step 304, at least processing task can be performed in association with the received input signal(s) in a manner so as to generate one or more output signals, in accordance with an embodiment of the disclosure.

[0076] The processing step 304 may include configuring data indicative of model training; communicating the data to a user device; initiating model training based on the data; and generating a plurality of parameters related to the model training. The data may comprise model identity and dataset identity while the plurality of parameters may comprise model identity and associated dataset identity.

[0077] The processing step 304 can also include communicating the plurality of parameters via physical layer (L1 ) signalling and / or data link layer (L2) signalling; communicating the plurality of parameters via a User Equipment (UE) specific message and / or system information block (SIB) based on a model configuration and validating the plurality of parameters for model training and inference. Generating the plurality of parameters may include generating periodically and / or generating based on a pre-configured event and the model training may include an Artificial Intelligence Machine Learning (AI / ML) model training.

[0078] In an embodiment, there can be a method for dataset identification for model training and inference in a wireless communication system, whereby the gNB (or base station or network) can provide dataset along with model identity associated with the dataset. The wireless communication system may configure model identity during model identification and the dataset identity can be configured as use-case specific.

[0079] In an embodiment, the wireless communication system may configure the model identity with dataset identity, whereby model identity and dataset identity may be configured together.

[0080] In an embodiment, after a UE (or user device) has collected dataset and is performing training, the gNB (or base station) may send a use case indication with dataset identity.

[0081] In an embodiment, there can be a method for Dataset Identification for Model Training and Inference in a wireless communication system, whereby the UE (or user device) receives a message for configuring model identity with dataset identity by the wireless communication system. The UE (or user device) may then configure the model identity and dataset identity and collects dataset and performs training during model identification. The UE (or user device) may then indicate model identity with dataset identity for data collection.

[0082] In an embodiment, after receiving a message for configuring model identity by the wireless communication system, the UE (or user device) may indicate model identity with dataset identity for data collection, after the reception of the use case identification with dataset set from the gNB (or base station) after the model identification.

[0083] In an embodiment, the UE (or user device) can include Dataset identity with the model identity where dataset identity and the model identity may be configuredby the network and the UE (or user device) generating the dataset may include Dataset identity. The network (or gNB or base station) receiving the dataset can use Dataset identity for model validation and may also use the received dataset for training and inference. The UE (or user device) can generate the dataset periodically or based on a pre-configured event and the UE (or user device) can also signal the Dataset identity and Model identity via L1 or L2 signaling. This could be signaled via UE specific message and SIB based on how the AIML model identification is configured. This can result in the entities carrying out the model training (e.g. UE or gNB) associate the dataset with the model IDs to avoid dataset mismatch.

[0084] With regard to the output step 306, the output signal(s) can, for example, be communicated, as an option, in accordance with an embodiment of the disclosure. For example, the output signal(s) can optionally be communicated from the apparatus 102. In a more specific example, the output signal(s) can optionally be communicated from the apparatus 102 to one or both of at least one device 104 and another apparatus 102, in accordance with an embodiment of the disclosure.

[0085] The present disclosure further contemplates a computer program (not shown) which can include instructions which, when the program is executed by a computer (not shown), cause the computer to carry out the input step 302, the processing step 304 and / or the output step 306 as discussed with reference to the method 300. For example, the computer program can include instructions which, when the program is executed by a computer, cause the computer to carry out the input step 302 and / or the processing step 304, in accordance with an embodiment of the invention.

[0086] The present disclosure yet further contemplates a computer readable storage medium (not shown) having data stored therein representing software executable by a computer (not shown), the software including instructions, when executed by the computer, to carry out the input step 302, the processing step 304 and / or the output step 306 as discussed with reference to the method 300. For example, the computer readable storage medium can have data stored therein representing software executable by a computer, the software including instructions, when executed by thecomputer, cause the computer to carry out the input step 302 and / or the processing step 304, in accordance with an embodiment of the invention.

[0087] Further in view of the foregoing, it is appreciable that the present disclosure generally contemplates an apparatus 102 suitable for energy saving in a network which can include a first module 202, a second module 204 and / or a third module 206.

[0088] The first module 202 can be configured to receive one or more input signals. The input signal(s) can, for example, be associated with data indicative of model training.

[0089] The second module 204 can be configured to process and / or facilitate processing of the input signal(s) according to the method 300 as discussed earlier to generate one or more output signals.

[0090] The third module 206 can be configured to communicate one or more output signals. The output signal(s) can, for example, correspond to one or more control signals for model training in the network.

[0091] In one embodiment, the apparatus 102 can correspond to a User Equipment (UE) which can communicate with a device 104 corresponding to a base station. The base station can, for example, correspond to a Next generation Node B (gNB) which can be configured to communicate one or more signals (e.g., input signal(s)) to the UE.

[0092] Yet further in view of the foregoing, it is appreciable that the present disclosure generally contemplates a system 100 which can include one or more apparatuses 102 and one or more devices 104. The apparatus(es) 102 and the device(s) 104 can, for example, be capable of being coupled via wired coupling and / or wireless coupling.

[0093] It should be appreciated that the embodiments described above can be combined in any manner as appropriate (e.g., one or more embodiments as discussed in the “Detailed Description” section can be combined with one or more embodiments as described in the “Summary of the Invention” section).

[0094] It should be further appreciated by the person skilled in the art that variations and combinations of embodiments described above, not being alternatives or substitutes, may be combined to form yet further embodiments.

[0095] In one example, the possibility of the output signal(s) being communicated from the apparatus(es) 102 was discussed. It is appreciable that the output signal(s) need not necessarily be communicated from the apparatus(es) 102. Specifically, the possibility that the output signal(s) need not necessarily be communicated outside of the apparatus(es) 102 is contemplated, in accordance with an embodiment of the invention. More specifically, the output signal(s) can, for example, correspond to internal command(s) / instruction(s) (e.g., communicated only within an apparatus 102) for adaptively controlling operational configuration of an apparatus 102, in accordance with an embodiment of the invention.

[0096] Fig. 4A to Fig. 4D show schematic diagrams illustrating example scenarios in association with the method 300, in accordance with an embodiment of the disclosure.

[0097] The example context shown in Fig. 4A can have a model identity and dataset identity configured together between the UE (or user device) and gNB (or base station). The gNB (or base station) and the UE (or user device) can have a model on their sides and the model is given on the gNB (or base station) side. The gNB (or base station) may send a message to the UE (or user device) and the network configures model identity with dataset identity. After the UE (or user device) has received such a message, the UE (or user device) collects dataset and performs training. Thereafter, the UE (or user device) can indicate the model identity with dataset identity for data collection.

[0098] The example context shown in Fig. 4B can have a dataset identity configured as use-case specific between the UE (or user device) and gNB (or base station). The gNB (or base station) and the UE (or user device) can each have a model on their sides. The gNB (or base station) may send a message to the UE (or user device) and the network configures a model identity with dataset identity. After the UE (or user device) has received this message, the UE (or user device) collects the dataset and performs training and receives the use case indication with dataset identity after the model identification. Thereafter, the UE (or user device) indicates model ID with dataset ID for data collection.

[0099] In the example context as shown in Fig. 4C, the base station (or gNB or network) may configure a model identity and dataset identity for the UE (or user device).

[0100] In the example context as shown in Fig. 4D, the UE (or user device) may be configured to provide dataset along with the model identity associated with the dataset.

[0101] In the foregoing manner, various embodiments of the disclosure are described for addressing at least one of the foregoing disadvantages. Such embodiments are intended to be encompassed by the following claims, and are not to be limited to specific forms or arrangements of parts so described and it will be apparent to one skilled in the art in view of this disclosure that numerous changes and / or modification can be made, which are also intended to be encompassed by the following claims.

Claims

Claim(s)1 . A method (300) for model training in a network comprising: configuring data indicative of model training; communicating the data to a user device; initiating model training based on the data; and generating a plurality of parameters related to the model training.

2. The method (300) according to claim 1 , wherein the data comprises model identity and dataset identity.

3. The method (300) according to claim 1 , wherein the plurality of parameters comprises model identity and associated dataset identity.

4. The method (300) according to claim 1 , further comprising communicating the plurality of parameters via physical layer (L1 ) signalling and / or data link layer (L2) signalling.

5. The method (300) according to claim 1 , further comprising communicating the plurality of parameters via a User Equipment (UE) specific message and / or system information block (SIB) based on a model configuration.

6. The method (300) according to claim 1 , further comprising validating the plurality of parameters for model training and inference.

7. The method (300) according to claim 1 , wherein generating the plurality of parameters comprises generating periodically and / or generating based on a preconfigured event.

8. The method (300) according to claim 1 , wherein the model training comprises an Artificial Intelligence Machine Learning (AI / ML) model training.

9. A computer program comprising instructions which, when the program is executed by a computer, cause the computer to carry out the method (300) of any of the preceding claims.

10. A computer readable storage medium having data stored therein representing software executable by a computer, the software including instructions, when executed by the computer, to carry out the method (300) of claims 1-9.

11. An apparatus (102) for model training in a network comprising: a first module (202) configured to receive at least one input signal associated with data indicative of model training; a second module (204) configured to at least one of process and facilitate the method (300) of claim 1 to claim 8 to generate at least one output signal; and a third module (206) configured to communicate at least one output signal, wherein the output signal corresponds to a control signal for model training in the network.

12. The apparatus (102) according to claim 11 , wherein the apparatus (102) corresponds to a User Equipment (UE) communicable with a device (104) corresponding to a base station, and wherein the base station corresponds to a Next generation Node B (gNB) configured to communicate the at least one input signal to the UE.

13. A system (100) comprising: at least one apparatus (102) according to any of claims 11 and 12; and at least one device (104) according to claim 12, wherein the apparatus (102) and the device (104) are capable of being coupled via at least one of wired coupling and wireless coupling.

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