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

Abstract

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

Description

SYSTEM AND APPARATUS FOR MODEL TRANSFER 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 transfer 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 uplink (UL) model transfer do not disclose a mechanism for two-sided model training. The present disclosure contemplates that conventional techniques may not facilitate efficiency and energy savings in an optimal manner. For example, there may be issues when using radio resource control (RRC) messages (e.g. UE Assistance Information UAI message) for UL model transfer because UAI depends on a User Equipment (UE) state and changes frequently due to various scenarios such as UE mobility etc. In such cases, using UAI for UL model transfer as a cyclic prefix (CP) mechanism may not be optimal.

[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 transfer in a network comprising: configuring a plurality of parameters indicative of uplink model transfer; communicating the plurality of parameters to a user device; and initiating an uplink model transfer by the user device based on the plurality of parameters.

[0006] Advantageously, the method as described herein can provide a dedicated framework for Artificial Intelligence Machine Learning (AIML) model transfer during uplink (UL) and may also allow the User Equipment (UE) to have an indication from the network on when to perform model transfer.

[0007] In an embodiment, the plurality of parameters indicative of uplink model transfer comprises at least one of: an uplink model transfer periodicity and / or a model drift threshold.

[0008] In an embodiment, the method further comprises determining whether a current model performance is below the model drift threshold; and initiating the uplink model transfer if the current model performance is below the model drift threshold.

[0009] In an embodiment, the method further comprises determining whether a current periodicity is within the uplink model transfer periodicity; determining whether a current model performance is below the model drift threshold; and initiating the uplink model transfer if the current model performance is below the model drift threshold and if the current periodicity is within the uplink model transfer periodicity.

[0010] In an embodiment, the uplink model transfer comprises an Artificial Intelligence Machine Learning (AIML) model transfer.

[0011] In an embodiment, initiating the uplink model transfer comprises generating a new Radio Resource Control (RRC) message and transmitting an AIML model via the new RRC message.

[0012] In an embodiment, the new RRC message comprises an information element having a plurality of parameters related to an AIML model.

[0013] In an embodiment, the plurality of parameters related to an AIML model comprises at least one of: an AIML payload, an AIML segment identification and / or an AIML model identification.

[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 transfer in a network comprising a first module configured to receive at least one input signal associated with a plurality of parameters indicative of uplink model transfer; 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 uplink model transfer by the user device.

[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 gNB (or base station) to have control over the UE to transfer the AIML model. The RRC message can provide a dedicated framework in the CP to perform AIML model delivery to the gNB (or base station).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 transfer 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. 4C 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 otherembodiments, 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 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.

[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 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.

[0034] The following explanation will provide the detailed description of the mechanism about pre-configuring and signaling the specific information about model selection using association between models and index values. Artificial Intelligence Machine Learning (AIML) 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. AIML 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 AIML model for any use case or application, one of the challengingissues is to manage the lifecycle of AIML model. It is mainly because the data / model drift occurs during model deployment / inference and it results in performance degradation of AIML model. Fundamentally, the dataset statistical changes occur after model is deployed and model inference capability is also impacted with unseen data as input. In a similar aspect, the statistical property of dataset and the relationship between input and output for the trained model can be changed with drift occurrence. In this context, model selection is one of key issues for model performance maintenance as model performance such as inferencing and / or training is dependent on different model execution environment with varying configuration parameters. To handle this issue, collaboration between UE and gNB is highly important to track model performance and re-configure model corresponding to different environments. AIML model needs model monitoring after deployment because model performance cannot be maintained continuously due to drift and update feedback is then provided to re-train / update the model or select alternative model. When AIML model enabled wireless communication network is deployed, it is then important to consider how to handle AIML model in activation with reconfiguration for wireless devices under operations such as model training, inference, updating, etc. Therefore, there is a need for specification for signaling methods and gNB-UE behaviors when a set of multiple specific AIML models are supported for RAN-based model operation and a new mechanism about gNB-UE behaviors and procedures is necessary to avoid any performance impact on model operation using multiple specific AIML models.

[0035] 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.

[0036] Specifically, the present disclosure contemplates that AIML models which are to be transferred from the UE (or user device) to the gNB (or base station) may have issues if existing Radio Resource Control (RRC) messages (e.g. User Equipment Assistance Information UAI) for uplink (UL) model transfer are used. This is because UAI depends on the UE (or user device) state and changes frequently due to variousscenarios such as UE (or user device) mobility etc. Therefore, using UAI for UL model transfer as a cyclic prefix (CP) mechanism may not be optimal in such situations.

[0037] The present disclosure also contemplates that the UE (or user device) may have a single RRC state based on the main node (MN) RRC state. The UE (or user device) may connect to the CN via single control plane connection and initial access (SRBO) and RRC configuration (SRB1 ) may be via the MN. Later reconfigurations can be from either the MN or secondary node (SN) and EN-DC may start with EUTRA Packet Data Convergence Protocol (PDCP) which can be reconfigured to use new radio (NR) PDCP.

[0038] The present disclosure further contemplates that in the scenario of an UL AIML model transfer, the CP mechanism which the UE (or user device) can make use of is UE Assistance Information message (UAI). The functionality of UAI can be highly dependent on the UE state and the mobility of the UE (or user device) and therefore, using UAI for UL AIML model transfer may not be suitable.

[0039] The present disclosure therefore contemplates the possibility of a method to address the UL model transfer issue for a UE (or user device). Specifically, the present disclosure contemplates a method in which a new UL RRC message may be used to transfer the uplink AIML model. The gNB (or base station) may configure the UE (or user device) to initiate the uplink AIML model transfer based one (or more) of the following criteria such as periodicity, UE AIML model drift and / or a combination of both periodicity and model drift.

[0040] In the above manner, power and energy consumption efficiency can be possibly facilitated, in accordance with an embodiment of the disclosure.

[0041] The foregoing will be discussed in further detail with reference to Fig. 1 to Fig.4 hereinafter.

[0042] Referring to Fig. 1A, a system 100 for model transfer 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.

[0043] 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.

[0044] 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.

[0045] 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.

[0046] 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, beconfigured 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.

[0047] 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.

[0048] 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.

[0049] 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 bediscussed, in accordance with an embodiment of the disclosure, in the context of an example scenario with reference to Fig. 1 B, hereinafter.

[0050] Fig. 1 B shows example scenarios in association with the system of Fig. 1A, according to an embodiment of the invention. Specifically, Fig. 1 B shows examples of different embodiments for training of an Artificial Intelligence Machine Learning (AIML) model. Referring to the Figure, Type 1 shows an example of joint training of a two-sided model at a single side / entity, e.g., at the UE-sided or the Network-side. In this example, model transfer is required from one side to the other. Type 2 shows an example of joint training of the two-sided model at the network side and the UE side, respectively. In this example, training iterations can exchange forward or backward propagation results needed and simultaneous interactions between network and UEs are required during the training process. Type 3 shows an example of separate training at the network side and the UE side, where the UE-side Channel state information (CSI) generation part and the network-side CSI reconstruction part are trained by the UE side and the network side, respectively. In this example, training dataset needs to be shared with other sided model and delivery of dataset and reference model are required.

[0051] The present disclosure contemplates that there may be two solutions for the transfer of models, for example an AIML model, in a network. One example solution is where a base station or node or gNB can transfer or deliver AIML model(s) to the User Equipment (UE) via Radio Resource Control (RRC) signaling. Another example solution can be the base station or node or gNB can transfer or deliver AIML model(s) to the UE via UP data. The above-mentioned solutions may imply that the model delivery may be intended for downlink only.

[0052] The present disclosure thus contemplates that in training Type 1 (see Fig. 1 B) of Channel state information (CSI) compression with two-sided model, model delivery can be from the over-the-top (OTT) server to the gNB and the UE (or user device), or from the UE (or user device) to the gNB. Therefore, the present disclosure contemplates the possibility of a model delivery solution that can support both downlink and uplink.

[0053] 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 transfer by a UE (or user device) in a network, in accordance with an embodiment of the disclosure.

[0054] 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.

[0055] The aforementioned apparatus(es) 102 will be discussed in further detail with reference to Fig. 2 hereinafter.

[0056] 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.

[0057] 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, the electronic 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).

[0058] 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.

[0059] 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.

[0060] 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.

[0061] 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.

[0062] 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 a hardware-based module and a software-based module, according to an embodiment of the disclosure.

[0063] 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 inputsignal(s) can, for example, be communicated from the device(s) 104 (e.g., a gNB), in accordance with an embodiment of the disclosure.

[0064] 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.

[0065] 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.

[0066] 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., a hardware-based transceiver) capable of performing the functions of receiving and transmitting.

[0067] 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, allbelow 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.

[0068] 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.

[0069] The method 300 can, for example, be suitable for / capable of facilitating energy efficiency, in accordance with an embodiment of the disclosure.

[0070] 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.

[0071] 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).

[0072] 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.

[0073] The input step 302 can include receiving at least one input signal associated with a plurality of parameters indicative of uplink model transfer, where the uplink model transfer may include an Artificial Intelligence Machine Learning (AIML) model transfer. 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).

[0074] 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.

[0075] The processing step 304 may include at least one of: configuring a plurality of parameters indicative of uplink model transfer; communicating the plurality of parameters to a user device; and initiating an uplink model transfer by the user device based on the plurality of parameters. Communicating the plurality of parameters comprises communicating via at least one of: system information message and / or dedicated RRC message.

[0076] The processing step 304 can also include determining whether a current model performance is below the model drift threshold and initiating the uplink model transfer if the current model performance is below the model drift threshold.

[0077] The processing step 304 may further include determining whether a current periodicity is within the uplink model transfer periodicity; determining whether a current model performance is below the model drift threshold; and initiating the uplink model transfer if the current model performance is below the model drift threshold and if the current periodicity is within the uplink model transfer periodicity.

[0078] The plurality of parameters indicative of uplink model transfer may comprise at least one of: an uplink model transfer periodicity and / or a model drift threshold.Initiating the uplink model transfer may include generating a new Radio Resource Control (RRC) message and transmitting an AIML model via the new RRC message. The new RRC message may include an information element having a plurality of parameters related to an AIML model while the plurality of parameters related to an AIML model may include at least one of: an AIML payload, an AIML segment identification and / or an AIML model identification.

[0079] In an embodiment, a new UL RRC message can be used for transferring the uplink AIML model, whereby the gNB (or base station) may configure the UE (or user device) to initiate based one (or more) of the following criteria. One example criteria for the UE (or user device) to initiate uplink AIML model transfer can be periodical (or periodicity). In this example, the UE (or user device) performs UL AIML model transfer or delivery periodically as configured by the gNB (or base station) via new RRC message. The periodicity may be configured by the gNB (or base station) in system information message and / or dedicated RRC message.

[0080] A second example criteria for the UE (or user device) to initiate uplink AIML model transfer can be based on AIML model drift. In this example, the UE (or user device) performs UL AIML model transfer / delivery based on the UE AIML model drift. In particular, the gNB (or base station) configures a threshold for the UE (or user device) to determine AIML model drift. Upon receiving the threshold, the UE (or user device) may compare the AIML model performance with the configured threshold to check for AIML model drift. If the UE (or user device) determines the AIML model performance is below the configured threshold, then the UE (or user device) may transmit UL AIML model to the gNB (or base station) via a new RRC message.

[0081] A third example criteria for the UE (or user device) to initiate uplink AIML model transfer can be based on the combination of both periodicity and AIML model drift. In this example, when the UE UL AIML model transfer periodicity is met and the model performance has not degraded from AIML model drift (e.g. the AIML model performance is equal to or above the configured threshold), then the UE (or user device) will not perform UL AI / ML model transfer. When the UE UL AI / ML model transfer periodicity is met and the model performance has degraded from AI / MLmodel drift (e.g. the AIML model performance is below the configured threshold), then the UE (or user device) will perform UL AIML model transfer. The new RRC message information element (IE) may carry the AIML model related parameters such as AIML payload, AIML segment ID, AI / ML model ID, etc. Such a method may be applicable to all RRC states.

[0082] 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.

[0083] 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.

[0084] 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 the 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.

[0085] Further in view of the foregoing, it is appreciable that the present disclosure generally contemplates an apparatus 102 suitable for energy saving in a networkwhich can include a first module 202, a second module 204 and / or a third module 206.

[0086] The first module 202 can be configured to receive one or more input signals. The input signal(s) can, for example, be associated with a plurality of parameters indicative of uplink model transfer.

[0087] 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.

[0088] 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 uplink model transfer by the user device (or UE).

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] Fig. 4A to Fig. 4C show schematic diagrams illustrating example scenarios in association with the method 300, in accordance with an embodiment of the disclosure.

[0095] In the example context as shown in Fig. 4A, the base station (or gNB or network) may trigger the UE (or user device) for an uplink AIML model transfer. The UE (or user device) may then send an uplink RRC message containing the AIML model to the base station (or gNB).

[0096] In the example context as shown in Fig. 4B, the UE (or user device) may be configured to receive a plurality of parameters indicative of uplink model transfer. The UE (or user device) may then determine if there is AIML model drift and / or periodicity. If AIML model drift and / or periodicity is present, the UE (or user device) transmits the UL AIML model to the gNB (or base station).

[0097] In the example context as shown in Fig. 4C, the gNB (or base station or network) may configure the periodicity and / or the AIML model drift for the UE (or user device) before sending them to the UE (or user device).

[0098] 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.Abbreviations:AIML Artificial Intelligence Machine Learning BWP Bandwidth part CP Cyclic Prefix CRC Cyclic redundancy checkCRI CSI-RS Resource IndicatorCSI Channel state informationCSI-RS Channel state information reference signalCSI-RSRP CSI reference signal received powerCSI-RSRQ CSI reference signal received quality CSI-SINR CSI signal-to-noise and interference ratio CW CodewordDCI Downlink control information DL DownlinkDM-RS Demodulation reference signalsDRB Data Radio Bearers DRX Discontinuous Reception L1-RSRP Layer 1 reference signal received power LI Layer IndicatorLP-WUR Low power wake up receiverLP-WUS Low power wake up signalMAC Medium Access ControlMCS Modulation and coding scheme MN Main Node MR Main receiver MR-DC Multi Radio Dual ConnectivityNR New RadioPDCP Packet Data Convergence Protocol PDCCH Physical Downlink Control ChannelPDSCH Physical downlink shared channel PHY Physical Layer PMI Precoding Matrix Indicator PRB Physical resource blockPRG Precoding resource block group PRS Positioning reference signal PSS Primary Synchronisation signal PT-RS Phase-tracking reference signal PUCCH Physical uplink control channel QCL Quasi co-location RB Resource block RBG Resource block group Rl Rank Indicator RIV Resource indicator value RLF Radio Link Failure RRC Radio Resource Control RS Reference signal RSRP Reference Signal Received Power RSRQ Reference Signal Received Quality SCI Sidelink control information SN Secondary Node SLIV Start and length indicator value SR Scheduling Request SRS Sounding reference signal SS Synchronisation signal SS-RSRP SS reference signal received power SS-RSRQ SS reference signal received quality SSS Secondary Synchronisation signal SS-SINR SS signal-to-noise and interference ratio TB Transport Block TCI Transmission Configuration Indicator TDM Time division multiplexing UAI User Equipment Assistance Information UE User equipment UL Uplink

Claims

Claim(s)1 . A method (300) for model transfer in a network comprising: configuring a plurality of parameters indicative of uplink model transfer; communicating the plurality of parameters to a user device; and initiating an uplink model transfer by the user device based on the plurality of parameters.

2. The method (300) according to claim 1 , wherein the plurality of parameters indicative of uplink model transfer comprises at least one of: an uplink model transfer periodicity and / or a model drift threshold.

3. The method (300) according to claim 2, further comprising: determining whether a current model performance is below the model drift threshold; and initiating the uplink model transfer if the current model performance is below the model drift threshold.

4. The method (300) according to claim 2, further comprising: determining whether a current periodicity is within the uplink model transfer periodicity; determining whether a current model performance is below the model drift threshold; and initiating the uplink model transfer if the current model performance is below the model drift threshold and if the current periodicity is within the uplink model transfer periodicity.

5. The method (300) according to claim 1 , wherein the uplink model transfer comprises an Artificial Intelligence Machine Learning (AIML) model transfer.

6. The method (300) according to claim 1 , wherein initiating the uplink model transfer comprises: generating a new Radio Resource Control (RRC) message; and transmitting an AIML model via the new RRC message.

7. The method (300) according to claim 6, wherein the new RRC message comprises an information element having a plurality of parameters related to an AIML model.

8. The method (300) according to claim 7, wherein the plurality of parameters related to an AIML model comprises at least one of: an AIML payload, an AIML segment identification and / or an AIML model identification.

9. The method (300) according to claim 1 , wherein communicating the plurality of parameters comprises communicating via at least one of: system information message and / or dedicated RRC message.

10. 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.

11. 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.

12. An apparatus (102) for model transfer in a network comprising: a first module (202) configured to receive at least one input signal associated with a plurality of parameters indicative of uplink model transfer; a second module (204) configured to at least one of process and facilitate the method (300) of claim 1 to claim 9 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 uplink model transfer by the user device.

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

14. A system (100) comprising: at least one apparatus (102) according to any of claims 12 and 13; and at least one device (104) according to claim 13, 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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