System and apparatus for preempting a model configuration and a method in association thereto

Abstract

System (100), device (104) and a method (300) for preempting a model configuration are disclosed. The method (300) includes obtaining, by a source cell, data associated with a model configuration of a target cell; processing the model configuration data to generate one or more model parameters associated with the model of the target cell; communicating the one or more model parameters to a user device; and configuring the user device based on the one or more model parameters of the target cell before the user device switches to the target cell.

Description

SYSTEM AND APPARATUS FOR PREEMPTING A MODEL CONFIGURATION AND A METHOD IN ASSOCIATION THERETOField Of Invention

[0001] The present disclosure generally relates to one or both of a system, a device and an apparatus for preempting a model configuration in association with, for example, a User Equipment (UE) or a cell (or node I access point) usable for communication. The present disclosure further relates a method which can be associated with the system, the device and / or the apparatus.Background of Invention

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

[0003] Current techniques in preempting or configuring a model for a User Equipment (UE) in a communication network may face problems such as service discontinuity, service delay and inefficient or even inaccurate model reconfiguration with frequent handover. Thus, the current techniques may not facilitate efficiency in an optimal manner.

[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 power saving.Summary of the Invention

[0005] According to a first aspect of the present invention, there is provided a method for preempting a model configuration, the method comprising: obtaining, by a source cell, data associated with a model configuration of a target cell; processing the model configuration data to generate one or more model parameters associated with themodel of the target cell; communicating the one or more model parameters to a user device; and configuring the user device based on the one or more model parameters of the target cell before the user device switches to the target cell.

[0006] Advantageously, the method as described herein can provide an efficient method for model (e.g. AI / ML model) preemption of a mobile User Equipment (UE) that undergoes frequent handover so that the mobile User Equipment (UE) is prepared with the model (e.g. AI / ML model) parameters before it is handed over to the target cell.

[0007] In an embodiment, the method may include requesting, by the source cell, data associated with the model configuration of the target cell.

[0008] In an embodiment, requesting data associated with the model configuration comprises requesting the data via a handover request.

[0009] In an embodiment, the model is an Artificial Intelligence I Machine Learning (AIML) model.

[0010] In an embodiment, communicating the one or more model parameters to a user device comprises communicating by at least one of: a handover command message or User Equipment (UE) specific message.

[0011] In an embodiment, the target cell and the source cell correspond to at least one base station.

[0012] In an embodiment, the at least one base station corresponds to at least one Next Generation Node B (gNB).

[0013] In an embodiment, the model configuration data comprises at least one of: Life Cycle Management (LCM) parameters, model Identification (ID) and / or pretrained dataset.

[0014] In an embodiment, obtaining the data associated with a model configuration comprises data associated with a specific model configuration.

[0015] In an embodiment, the method may include receiving a request for the model configuration of the target cell; processing the request to generate data associated with the model configuration of the target cell; and transmitting the data associated with the model configuration of the target cell to the source cell.

[0016] In an embodiment, there is provided a computer program comprising instructions which, when the program is executed by a computer, cause the computer to carry out the method according to the first aspect.

[0017] 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 according to the first aspect.

[0018] In an embodiment, a device for preempting a model configuration includes a first module configured to obtain data associated with a model configuration of a target cell; 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 configuring the user device based on the one or more model parameters of the target cell before the user device switches to the target cell.

[0019] In an embodiment, the device corresponds to a base station communicable with an apparatus corresponding to a User Equipment (UE), and wherein the base station corresponds to a Next generation Node B (gNB) configured to communicate the at least one output signal to the UE.

[0020] In an embodiment, there is provided a system comprising: at least one device and at least one apparatus, wherein the apparatus and the device are capable of being coupled via at least one of wired coupling and wireless coupling.

[0021] Advantageously, the system as disclosed herein can have no service disruption due to a model configuration change because the UE is prepared with the AIML configuration of the target cell before moving to the target cell. Accordingly, service delay resulting from model reconfiguration can be mitigated.Brief Description of the Drawings

[0022] Embodiments of the disclosure are described hereinafter with reference to the following drawings, in which:

[0023] Fig. 1A shows a schematic diagram illustrating a system for preempting a model configuration which can include at least one apparatus, according to an embodiment of the invention.

[0024] Fig. 1 B to Fig. 1 D show example scenarios in association with the system of Fig. 1A, according to an embodiment of the invention.

[0025] Fig. 2 shows a schematic diagram illustrating the device of Fig. 1A in further detail, according to an embodiment of the invention.

[0026] Fig. 3 shows a method in association with the system of Fig. 1A, according to an embodiment of the invention.

[0027] Fig. 4A to Fig. 4D show schematic diagrams illustrating the flow of information in association with the method of Fig. 3, according to an embodiment of the invention.Detailed Description

[0028] The present specification discloses apparatus for performing the operations of the methods. Such apparatus may be specially constructed for the required purposes, or may comprise a computer or other device selectively activated or reconfigured by a computer program stored in the computer. The algorithms and displays presented herein are not inherently related to any particular computer or other apparatus. Various machines may be used with programs in accordance with the teachings herein. Alternatively, the construction of more specialized apparatus to perform the required method steps may be appropriate. The structure of a computer will appear from the description below.

[0029] In addition, the present specification also implicitly discloses a computer program, in that it would be apparent to the person skilled in the art that the individual steps of the method described herein may be put into effect by computer code. The computer program is not intended to be limited to any particular programming language and implementation thereof. It will be appreciated that a variety of programming languages and coding thereof may be used to implement the teachings of the disclosure contained herein. Moreover, the computer program is not intended to be limited to any particular control flow. There are many other variants of the computer program, which can use different control flows without departing from the spirit or scope of the disclosure.

[0030] Furthermore, one or more of the steps of the computer program may be performed in parallel rather than sequentially. Such a computer program may be stored on any computer readable medium. The computer readable medium may include storage devices such as magnetic or optical disks, memory chips, or other storage devices suitable for interfacing with a computer. The computer readable medium may also include a hard-wired medium such as exemplified in the Internet system, or wireless medium such as exemplified in the mobile telephone system.The computer program when loaded and executed on such a computer effectively results in an apparatus that implements the steps of the preferred method.

[0031] The present disclosure generally contemplates the facilitation and optimization of a network (for example in association with 3GPP based standard / specification etc.) and / or user equipment (UE) efficiency and mobility (for example energy efficiency or power saving), in accordance with an embodiment of the invention. Specifically, the present disclosure contemplates the possibility of configuring (or preempting) an Artificial Intelligence I Machine Learning (AIML or AI / ML) model of the UE in connection with 3GPP standard(s).

[0032] In communication networks, methods or mechanisms are used to define how AIML (or AI / ML) can assist to optimize a handover (HO) for a mobile device (or UE). Executing a good handover, for example from a source cell to a target cell, is important as it may allow the UE to stay connected within the network and information can be properly transferred from one base station (e.g. source cell) to another base station (e.g. target cell). In an example embodiment, base stations (or cells), such as a Next Generation Node B (gNB), can coordinate AI / ML model implementation in a network or determine if the AI / ML model implementation is cell specific (e.g. target cell or source cell). In another example embodiment, a UE may use multiple models (e.g., multiple AI / ML models) in the serving cell (or source cell) all of which may or may not be available in the target cell.

[0033] The present disclosure generally contemplates that such mechanisms and methods may not be efficient. For example, AI / ML inference may need to be exclusive to the serving (or source) cell, which may not achieve active usage of AIML (or AI / ML) capabilities. In a further example, the target gNB (or cell) may provide the AIML (or AI / ML) model configuration when / after the UE switches to the target gNB (or cell). This may cause a delay in AIML (or AI / ML) inference when the UE switches between the serving cell (or source cell) and the target cell.

[0034] The present disclosure also contemplates it may be possible to have, for example, AI / ML aided mobility with handover optimization at the network side or UE side. This can include, for example, source (or candidate) cell(s) or target cell(s) prediction in a Layer 3(L3)-based mobility as well as candidate or target beam(s) and cell(s) prediction in lower layer triggered mobility (LTM) such as Radio layer 2 and Radio layer 3 Radio Resource Control (RAN2) and UTRAN / E-UTRAN / NG-RAN architecture and related network interfaces (RAN3).

[0035] The present disclosure contemplates that Artificial Intelligence I Machine Learning (AI / ML or AIML) can be an important technology in network optimization which can lead to energy-efficient networks. In particular, the application of AIML (or AI / ML) in communication links (e.g. Air Interface) can result in handover optimization. If the UE is not configured with the AIML (or AI / ML) model of the target cell when switching over to the target cell, there may be issues such as service disruption and delay as well as handover failure.

[0036] The present disclosure contemplates the possibility of how AIML (or AI / ML) can be pre-empted in standard network systems, signaling aspects and trigger mechanisms. More specifically, receiving the target cell AIML (or AI / ML) model configuration by the UE in a handover command message can be important in light of having energy efficiency.

[0037] In the above manner, power saving and energy consumption efficiency can possibly be facilitated in the UE or the network, in accordance with an embodiment of the invention.

[0038] The foregoing will be discussed in further detail with reference to Fig. 1 to Fig. 4 hereinafter.

[0039] Referring to Fig. 1A, a schematic diagram illustrating a system 100 for preempting a model configuration is shown, according to an embodiment of theinvention. The system 100 can, for example, be suitable for facilitating energy and power efficiency, in accordance with an embodiment of the invention.

[0040] 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 invention.

[0041] 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 invention.

[0042] 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 invention.

[0043] The apparatus(es) 102 can, for example, be associated with or correspond to or include one or more user equipment (UE) (or user device) which can carry one or more computers, in accordance with an embodiment of the invention. For example, an apparatus 102 can correspond to a UE carrying at least one computer (e.g. an electronic device or 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 invention) which can be configured to perform one or more tasks in association with adaptive / dynamic / gradual control, in accordance with an embodiment of the invention.

[0044] The device(s) 104 can, for example, be associated with or correspond to at least one base station, where the at least one base station can be a Next Generation Node B (gNB). Moreover, the device(s) 104 can, for example, be configured to carry / be associated with / include one or more computers (e.g., an electronicdevice / 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 output signals which can be communicated to the apparatus(es) 102, in accordance with an embodiment of the invention. This will be discussed later in further detail in the context of an example scenario, in accordance with an embodiment of the invention.

[0045] In an embodiment, the device(s) 104 can, for example, be configured to obtain data associated with a model (e.g. an AI / ML model) configuration of a target cell and perform at least one processing task based on the data in a manner to generate one or more output signals. The model (e.g. AI / ML model) configuration data can, for example, be communicated from a target base station (or target cell) and received by the device(s) 104, in accordance with an embodiment of the invention. As a possible option, the output signal(s) can, for example, be communicated from the device(s) 104, in accordance with an embodiment of the invention. The output signal may correspond to a control signal for configuring (or preempting) the apparatus(es) 102 (or user device or User Equipment) to the model (e.g. AI / ML model) of the target base station (or target cell). The device(s) 104 will be discussed later in further detail with reference to Fig. 2, according to an embodiment of the invention.

[0046] 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.

[0047] As mentioned, the device(s) 104 can, for example, be configured to obtain data associated with a model (e.g. AI / ML model) configuration of a target cell and perform at least one processing task in association with dynamic / adaptive / gradualcontrol on the data in a manner so as to generate at least one output signal. Moreover, the device(s) 104 can, for example, be configured to receive the data from another device 104 (e.g. a target base station or target cell) and generate (and communicate) the output signal to the apparatus(es) 102, in accordance with an embodiment of the invention. Accordingly, the device(s) 104 can request such data from another device 104 (e.g. a target base station or target cell) before generating the output signal. This will be discussed, in accordance with an embodiment of the invention, in the context of an example scenario with reference to Fig. 1 B to Fig. 1 D, hereinafter.

[0048] Fig. 1 B to Fig. 1 D show example scenarios in association with the system of Fig. 1A, according to an embodiment of the invention. Specifically, Fig. 1 B to Fig. 1 D show different examples of model (e.g. AI / ML model) training at the apparatus(es) 102 and the device(s) 104 of Fig. 1A. Fig. 1 B shows an example by training at only one side of the system, either at the device(s) 104 I base station (or network-side, e.g. gNB) or the apparatus(es) 102 (or UE-side). As shown in the figure, a training entity may be provided to each of the device(s) 104 and the apparatus(es) 102. The training entity for each of the device(s) 104 and apparatus(es) 102 can include a module (not shown) configured to implement a respective neural network for handover such that the neural network determines the handover solely by the device(s) 104 (network-side) or solely by the apparatus(es) 102 (UE-side). Therefore, a model transfer from one side to another is required, i.e. either a model transfer from the device(s) 104 (network-side) to the apparatus(es) 102 (UE-side) or a model transfer from the apparatus(es) 102 (UE-side) to the device(s) 104 (network-side).

[0049] In an alternate embodiment, Fig. 1 C shows an example by joint training to both sides of the system, at the device(s) 104 I base station (or network-side, e.g. gNB) and the apparatus(es) 102 (or UE-side). In this embodiment, training entities may be provided to each of the device(s) 104 (network-side) and the apparatus(es) 102 (UE-side). Each of the training entities can include a respective module (not shown) configured to implement a respective neural network for handover such that the respective neural networks can determine the handover by both the device(s)104 (network-side) and the apparatus(es) 102 (UE-side). Specifically, the neural network of the network-side (or network-side neural network) is trained and configured to determine handover by the device(s) 104 while the neural network of the UE-side (or UE-side neural network) is trained and configured to determine handover by the apparatus(es) 102. Furthermore, the network-side neural network may perform a backward gradient technique to the apparatus(es) 102 (UE-side) and the UE-side neural network may perform a forward activation technique to the device(s) 104 (network-side) to determine and optimize handover by the device(s) 104 and the apparatus(es) 102. In such an embodiment, training iterations to exchange the forward activation or backward gradient / propagation results may be required. Furthermore, simultaneous interactions between the network-side and UE- side may be required during the training process.

[0050] In another embodiment, Fig. 1 D shows an example by separate training to both sides of the system, at the device(s) 104 I base station (or network-side, e.g. gNB) and the apparatus(es) 102 (or UE-side). In this embodiment, training entities may be provided to each of the device(s) 104 (network-side) and the apparatus(es) 102 (UE-side). Each of the training entities can include a respective module (not shown) configured to implement a respective neural network for handover such that the respective neural networks can be used to determine the handover by both the device(s) 104 (network-side) and the apparatus(es) 102 (UE-side). Specifically, the neural network of the UE-side (or UE-side neural network) may be trained using a first training data set and configured to determine handover by the apparatus(es) 102. Subsequently, the UE-side neural network shares the training data set with the neural network of the network-side (or network-side neural network), which is then trained and configured to determine handover by the device(s) 104. It can be appreciated that the order of training can be opposite, i.e. , the network-side neural network is trained first using a training dataset followed by the UE-side neural network after the network-side neural network shares the training dataset to the UE- side neural network. In addition, Channel Status Information (CSI) generation at the UE-side and CSI reconstruction at the network-side are respectively trained by the UE-side neural network and the network-side neural network. Moreover, the trainingdataset may be shared between each side (UE-side and network-side) and the delivery of the dataset and reference model (e.g. AI / ML model) may be required.

[0051] The neural network as described in the present disclosure can be an Artificial Intelligence (Al) / Machine Learning-based neural network model such as support vector machines, decision trees, ensemble models, k-nearest neighbours models or Bayesian networks. It can be appreciated that the neural network can also be a multi-modal neural network and may contain other types of models including linear models and / or non-linear models as well as feed-forward neural networks, convolutional neural networks (CNN) or recurrent neural networks (RNN). It can also be appreciated that other types of neural network training may be available and not limited to the examples described herein.

[0052] The above-described aspect(s) of the system 100 of the present invention can also apply analogously (all) the aspect(s) of a below described device 104 of the present invention. Likewise, all below described aspect(s) of the device 104 of the invention can also apply analogously (all) the aspect(s) of above-described system 100 of the invention.

[0053] The aforementioned device(s) 104 or cell (e.g. source cell or target cell) will be discussed in further detail with reference to Fig. 2 hereinafter.

[0054] Referring to Fig. 2, a schematic diagram illustrating a device 104 is shown in further detail in the context of an example implementation 200, according to an embodiment of the invention.

[0055] In the example implementation 200, the device 104 can correspond to an electronic module 200a. The electronic module 200a can, in one example, correspond to a module positioned at a base station (or cell) in accordance with an embodiment of the invention. In another example, the electronic module 200a can correspond to an electronic device which can be installed / mounted in the base station (or cell), in accordance with an embodiment of the invention.

[0056] 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 invention.

[0057] 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.

[0058] 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 invention.

[0059] 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.

[0060] 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 invention. 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 invention.

[0061] In one example, the first module 202 can correspond to a hardware-based receiver which can be configured to receive (or obtain) one or more input signals ordata. The input signal(s) or data can, for example, be communicated from another device(s) 104 (or base station e.g., a gNB), in accordance with an embodiment of the invention.

[0062] 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 invention.

[0063] 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 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 invention. For example, the output signal(s) can be a control signal(s) to configure the apparatus(es) 102 (or user device or User Equipment) to the model (e.g. AI / ML model) of the target base station (or target cell).

[0064] The present disclosure contemplates the possibility that the first and second modules 202, 204 can be an integrated software-hardware based module, for example, an electronic part which can carry a software program or algorithm in association with receiving and processing functions or 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 software-hardware based module, for example an electronic part which can carry a software program or algorithm in association with receiving and transmitting functions or 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, for example a hardware-based transceiver, capable of performing the functions of receiving and transmitting.

[0065] The device 104 (or base station or cell) can, for example, be further configured to process the input signal(s) or data, as will be discussed later in further detail with reference to Fig. 3, in a manner so as to generate one or more output signals in a manner so as to facilitate efficiency, for example power efficiency or energy efficiency, in accordance with an embodiment of the invention. In one specific example, the output signal(s) can include one or more control signals to facilitate some form of dynamic / adaptive / gradual control configuration / determination strategy so as to facilitate efficiency, for example power efficiency or energy efficiency, in accordance with an embodiment of the invention. For example, the output signal(s) can be a control signal(s) to configure (or preempt) the apparatus(es) 102 (or user device or User Equipment) to the model (e.g. AI / ML model) of the target base station (or target cell).

[0066] The above-described aspect(s) of the device 104 of the present invention can also apply analogously (all) the aspect(s) of a below described processing / communication method of the present invention. Likewise, all below described aspect(s) of the method of the invention can also apply analogously (all) the aspect(s) of above described device 104 of the invention. It is to be appreciated that these remarks apply analogously to the earlier discussed system 100 of the present disclosure.

[0067] Referring to Fig. 3, a method 300 (or a communication method) for preempting a model configuration, for example an Artificial Intelligence / Machine Learning (AIML or AI / ML) model, in association with the system 100 is shown, according to an embodiment of the invention.

[0068] The method 300 can, for example, be suitable for facilitating energy efficiency, network optimization and power saving in accordance with an embodiment of the invention.

[0069] The 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 invention.

[0070] In an 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).

[0071] With regard to the input step 302, one or more input signal(s) or data can be obtained. For example, the one or more input signal(s) can include data associated with a model (e.g. AI / ML model) configuration of a target cell and communicated from another device(s) 104 (e.g. target cell), in accordance with an embodiment of the invention.

[0072] The input step 302 can include obtaining data associated with a model configuration of a target cell (or device 104 or a target base station) by a source cell (or device 104 or a source base station). The at least one base station (e.g. the target cell and the source cell) may correspond to at least one Next Generation Node B (gNB). The source cell (or device 104 or source base station) may request data, via a handover request, associated with the model configuration of the target cell. The model may be an Artificial Intelligence I Machine Learning (AI / ML or AIML) model while the data may be associated with a specific model configuration. Further, the model (e.g. AI / ML model) configuration data may include at least one of: LifeCycle Management model (LCM) parameters, model Identification (ID) and / or pretrained dataset or any combination thereof.

[0073] With regards to the processing step 304, at least a processing task can be performed in association with the received input signal(s) or data in a manner so as to generate one or more output signals, in accordance with an embodiment of the invention.

[0074] The processing step 304 may include at least one of: processing the model configuration data to generate one or more model parameters associated with the model of the target cell; communicating the one or more model parameters to a user device; and configuring the user device based on the one or more model parameters of the target cell before the user device switches to the target cell. Communicating the one or more model parameters to a user device may include communicating by at least one of: a handover command message or User Equipment (UE) specific message (e.g., Radio Resource Control (RRC) reconfiguration).

[0075] The processing step 304 may further include at least one of: receiving a request for the model (e.g. AI / ML model) configuration of the target cell; processing the request to generate data associated with the model (e.g. AI / ML model) configuration of the target cell; and transmitting the data associated with the model (e.g. AI / ML model) configuration of the target cell to the source cell.

[0076] With regards to the output step 306, the output signal(s) can, for example, be communicated, as an option, in accordance with an embodiment of the invention. For example, the output signal(s) can optionally be communicated from the device 104. In a more specific example, the output signal(s) can optionally be communicated from the device 104 to one or both of at least one apparatus 102 and another device 104, in accordance with an embodiment of the invention. In an embodiment, the device 104 (or base station / cell) may also perform the input step 302, the processing step 304 and the output step 306.

[0077] 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.

[0078] 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.

[0079] Further in view of the foregoing, it is appreciable that the present disclosure generally contemplates a device 104 which can include a first module 202, a second module 204 and / or a third module 206.

[0080] The first module 202 can be configured to obtain one or more input signals. The input signal(s) can, for example, include data obtain data associated with a model (e.g. AI / ML model) configuration of a target cell.

[0081] The second module 204 can be configured to process and / or facilitate processing of the input signal(s) or data according to the method 300 as discussed earlier to generate one or more output signals.

[0082] 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 configuring (or preempting) the user device (or UE) based on the one ormore model (e.g. AI / ML model) parameters of the target cell before the user device switches to the target cell.

[0083] 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 (or cell). The base station (or cell) can, for example, correspond to a Next generation Node B (gNB) which can be configured to communicate one or more signals or data to the UE. The one or more signals may include one or more model (e.g. AI / ML model) parameters associated with the model (e.g. AI / ML model) of the target cell.

[0084] 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.

[0085] 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).

[0086] 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.

[0087] In one example, the possibility of the output signal(s) being communicated from the device(s) 104 was discussed. It is appreciable that the output signal(s) need not necessarily be communicated from the device(s) 104. Specifically, the possibility that the output signal(s) need not necessarily be communicated outside of the device(s) 104 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) foradaptively controlling operational configuration of an apparatus 102, in accordance with an embodiment of the invention.

[0088] In another example, application(s) of the present disclosure in association with / in the context of low power wake up radio and / or ambient loT (Internet of Things) type device(s) can be possible, in accordance with an embodiment of the invention.

[0089] Fig. 4A to Fig. 4D show schematic diagrams illustrating the flow of information in association with the method of Fig. 3, according to an embodiment of the invention.

[0090] In the example context as shown in Fig. 4A, a User Equipment (UE or user device) can, for example, be configured to obtain / receive AIML (or AI / ML) model configuration, such as AIML (or AI / ML) model parameters, of a cell in a handover command message, in accordance with an embodiment of the invention.

[0091] In the example context as shown in Fig. 4B, a source gNB (or source base station / source cell) can, for example, be configured to request data associated with the model configuration (e.g. AIML or AI / ML model configuration) of a target gNB (or target base station / target cell), in accordance with an embodiment of the invention. The source gNB (or source base station / source cell) may also configure the UE (or user device) with the AIML (or AI / ML) model configuration of the target gNB (or target base station / target cell), in accordance with an embodiment of the invention.

[0092] In the example context as shown in Fig. 4C, a target gNB (or target base station / target cell) may provide its AIML (or AI / ML)model configuration to the source gNB (or source base station / source cell), in accordance with an embodiment of the invention.

[0093] In the example context as shown in Fig. 4D, a source gNB (or source cell) can be configured to request AIML (or AI / ML) model configuration of a target gNB (or target cell) in a handover request at step 1. At step 2, the target gNB (or target cell) can, for example, be configured to receive the request for its AIML (or AI / ML) modelconfiguration and process the request to generate data associated with the model (e.g. AIML model) configuration of the target cell. The target gNB (or target cell) may then transmit the data associated with its model (e.g. AIML model) configuration to the source gNB (or source cell) in a handover request acknowledgement at step 3. The source gNB (or source cell) obtains the data associated with the model (e.g. AIML model) configuration of the target cell and processes the model (e.g. AIML model) configuration data to generate one or more model parameters (e.g. AIML parameters) associated with the model (e.g. AIML model) of the target cell at step 4.

[0094] At step 5, the source gNB (or source cell) communicates the one or more model parameters (e.g. AIML parameters) of the target gNB (or target cell) to the UE (or user device) via an RRC reconfiguration message to configure the UE (or user device). Thereafter, the UE (or user device) switches to the new cell (e.g. target cell or target gNB) at step 6 and reconfiguration of the UE (or user device) is complete at step 7.

[0095] 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 preempting a model configuration, the method comprising: obtaining, by a source cell, data associated with a model configuration of a target cell; processing the model configuration data to generate one or more model parameters associated with the model of the target cell; communicating the one or more model parameters to a user device; and configuring the user device based on the one or more model parameters of the target cell before the user device switches to the target cell.

2. The method (300) according to claim 1 , further comprising: requesting, by the source cell, data associated with the model configuration of the target cell.

3. The method (300) according to claim 2, wherein requesting data associated with the model configuration comprises requesting the data via a handover request.

4. The method (300) according to claim 1 , wherein the model is an Artificial Intelligence I Machine Learning (AIML) model.

5. The method (300) according to claim 1 , wherein communicating the one or more model parameters to a user device comprises communicating by at least one of: a handover command message or User Equipment (UE) specific message.

6. The method (300) according to claim 1 , wherein the target cell and the source cell corresponds to at least one base station.

7. The method (300) according to claim 6, wherein the at least one base station corresponds to at least one Next Generation Node B (gNB).

8. The method (300) according to claim 1 , wherein the model configuration data comprises at least one of: Life Cycle Management (LCM) parameters, model Identification (ID) and / or pretrained dataset.

9. The method (300) according to claim 1 , wherein obtaining the data associated with a model configuration comprises data associated with a specific model configuration.

10. The method (300) according to claim 1 , further comprising: receiving a request for the model configuration of the target cell; processing the request to generate data associated with the model configuration of the target cell; and transmitting the data associated with the model configuration of the target cell to the source cell.

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

12. 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) according to any one of claims 1-10.

13. A device (104) for preempting a model configuration comprising: a first module (202) configured to obtain data associated with a model configuration of a target cell; a second module (204) configured to at least one of process and facilitate the method (300) of claim 1 to claim 10 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 configuring the user device based on the one or more model parameters of the target cell before the user device switches to the target cell.

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

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