System and apparatus for determining a model accuracy and a method in association thereto

Abstract

System (100), apparatus (102) and a method (300) for determining a model accuracy are disclosed. The method (300) includes an input step (302) which comprises receiving at least one input signal associated with a model quality threshold range; and a processing step (304) which comprises at least one of: obtaining a model quality performance value based on derived inference of a current model; and determining an accuracy of the current model by analyzing the model quality performance value with the model quality threshold range.

Description

SYSTEM AND APPARATUS FOR DETERMINING A MODEL ACCURACY 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 determining a model accuracy 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 of Invention

[0002] Generally, energy efficiency and power saving 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 may not address the issue of determining a model accuracy by a base station or a User Equipment (UE) in a communication network. This may lead to problems such as having inaccurate inference, unnecessary energy consumption when executing an inaccurate model (e.g., Artificial Intelligence / Machine Learning model or AI / ML model) and accumulated error in the model (e.g., Artificial Intelligence / Machine Learning model or AI / ML model) leading to a deterioration in the performance. Thus, the current techniques may not facilitate energy efficiency and power saving 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 when determining a model accuracy.Summary of the Invention

[0005] According to a first aspect of the present invention, there is provided a method for determining a model accuracy, the method comprising: an input step whichcomprises receiving at least one input signal associated with a model quality threshold range; and a processing step which comprises at least one of: obtaining a model quality performance value based on derived inference of a current model; and determining an accuracy of the current model by analyzing the model quality performance value with the model quality threshold range.

[0006] Advantageously, the method as described herein can have a User Equipment (UE) perform inference on an accurate model. Overhead may also be reduced to minimize signaling of inference of a less accurate model.

[0007] In an embodiment, the method further includes requesting at least one model parameter update if the model quality performance value is not within the model quality threshold range; and configuring the model based on the model quality performance value if the model quality performance value is within the threshold range.

[0008] In an embodiment, the method further includes determining at least one updated model parameter; communicating the at least one updated model parameter; and configuring the model based on the at least one updated model parameter.

[0009] In an embodiment, the model quality threshold range is associated with a model quality parameter range (MQRP).

[0010] In an embodiment, the model quality parameter range (MQRP) comprises at least one of: time range, inference accuracy range and / or number of datapoints in a dataset.

[0011] In an embodiment, the model is an Artificial Intelligence / Machine Learning (AIML) model.

[0012] In an embodiment, at least one base station is configured to: pre-determine the model quality threshold range; and communicate the model quality threshold range.

[0013] In an embodiment, the at least one base station corresponds to at least one Next Generation Node B (gNB).

[0014] In an embodiment, a User Equipment (UE) is configured to perform the input step and the processing step, and wherein the model quality threshold range is communicable from the gNB to the UE.

[0015] In an embodiment, the model quality threshold range is received by the UE from the gNB via at least one of: System Information Block (SIB), Master Information Block (MIB) or UE specific message.

[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 at least one of the at least one of the input step and the processing step according to the method of 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 at least one of the input step and the processing step according to the method of the first aspect.

[0018] In an embodiment, there is provided an apparatus for determining a model accuracy comprising: a first module configured to receive at least one input signal associated with a model quality threshold range; a second module configured to at least one of process and facilitate the processing step according to 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 requesting at least one model parameter update if the model quality performance value is not within the model quality threshold range and configuring the model based on the model quality performance value if the model quality performance value is within the threshold range.

[0019] In an embodiment, the apparatus (102) corresponds to a User Equipment (UE) communicable with a device (104) corresponding to a base station, and wherein the base station corresponds to a Next generation Node B (gNB) configured to communicate the at least one input signal to the UE.

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

[0021] Advantageously, the system as disclosed herein can have energy efficiency through reduction in accumulated errors and accurate model determination.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 determining a model accuracy which can include at least one apparatus, according to an embodiment of the invention.

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

[0025] Fig. 2 shows a schematic diagram illustrating the apparatus 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. 4C 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 determining a model accuracy of a UE in connection with 3GPP standard(s).

[0032] The present disclosure generally contemplates that data collection in communication networks may be performed for different purposes in a Life Cycle Management (LCM), for example model training, model inference, model monitoring, model selection, model update, etc., where each of these may be done with different requirements and different impact on potential specification. Some examples that may have specification impact include measurement configuration and reporting, contents, type and format of data including data related to model input, data related to ground-truth, quality of the data and / or other relevant information.

[0033] The present disclosure contemplates that collaboration levels can be defined by having Network-UE collaboration levels as one aspect. Examples of Network-UE collaboration include Level x where there is no collaboration and Level y where collaboration is signal-based without model transfer. Such a level of collaboration (i.e. Level y) may include cases without model delivery. A further example may be Level z where it is a signaling-based collaboration with model transfer.

[0034] The present disclosure also contemplates that performing intermediate evaluations on model performance, for example an Artificial Intelligence I Machine Learning (AI / ML or AIML) model, can be considered to derive the intermediate Key Performance Inputs or KPI(s), such as accuracy of AI / ML model output Channel Status Information CSI, for the purpose of model (e.g., AI / ML model) solution comparison. Furthermore, the present disclosure contemplates that it can be highly important to have an accurate model (e.g., AI / ML model) for deriving an inference in order to determine the accurate outputs based on the proposed use-cases.

[0035] The present disclosure further contemplates that network management can be improved by utilizing three different models in an AI / ML communication link (e.g., Air Interface). The models may include network-side (e.g., a base station or Next Generation Node B (gNB)) models, UE-side models and both UE-side and networkside models where a model (e.g., AI / ML model) may be present on either of the end terminals, i.e. UE-side and / or network-side.

[0036] The present disclosure contemplates the possibility of how a network can configure Model Quality Parameter Range (MQRP) for the UE to determine whether the model is accurate to derive an inference or whether a model update is required, before executing the model (e.g., AI / ML model). In this way, energy consumption in the network can be optimized.

[0037] The present disclosure contemplates the exact definition of model accuracy may not be described in current systems and techniques, which can result in inaccurate inference. This may also lead to accumulated error in the model (e.g., AI / ML model) leading to a deterioration in the performance of the network. In addition, there may be unnecessary energy consumption to execute an inaccurate model (e.g., AI / ML model). The present disclosure contemplates it can be important for the UE or base station (e.g., gNB) to determine the model accuracy when the model (e.g., AI / ML model) is executed.

[0038] The present disclosure contemplates that the base station (e.g., gNB) can configure AI / ML MQRP for the UE. The accuracy range may be any or all the quality parameters such as but not limited to time range, inference accuracy range and number of datapoints in the dataset. The base station (e.g., gNB) may signal the parameter range(s) to the UE using for example but not limited thereto, System Information Block (SIB), Master Information Block (MIB), UE specific message or RRC configuration messages.

[0039] The present disclosure contemplates that the UE upon receiving the accuracy threshold, may perform the following actions. In an example embodiment, the UE may compare the derived inference if it is above threshold range. If the model quality performance lies within model quality parameters range, the UE proceeds with the AI / ML model inference. If the model quality performance lies outside model quality parameters range, the UE requests the base station (e.g. gNB) for a model (e.g., AI / ML model) update. The base station (e.g. gNB) may provide the updated model parameters (e.g., AI / ML model parameters) to the UE upon receiving the model (e.g., AI / ML model) update request.

[0040] In the above manner, UE may perform inference on an accurate model and overhead can thus be reduced to minimize signaling of inference of an inaccurate model. Power saving and energy consumption efficiency can possibly be facilitated in the UE or the network, in accordance with an embodiment of the invention.

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

[0042] Referring to Fig. 1A, a schematic diagram illustrating a system 100 for determining a model accuracy is shown, according to an embodiment of the invention. The system 100 can, for example, be suitable for facilitating energy and improve power efficiency, in accordance with an embodiment of the invention.

[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 invention.

[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 invention.

[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 thecommunication 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.

[0046] The apparatus(es) 102 can, for example, be associated with or correspond to or include one or more user equipment (UE) 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 processing tasks in association with adaptive / dynamic / gradual control, in accordance with an embodiment of the invention.

[0047] In an embodiment, the apparatus(es) 102 can, for example, be configured to receive one or more input signals and perform at least one processing task based on the input signal(s) in a manner to generate one or more output signals. The input signal(s) can, for example, be communicated from the device(s) 104 and received by the apparatus(es) 102, in accordance with an embodiment of the invention. The input signal can be associated with a model quality threshold range. 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 invention. The output signal may correspond to a control signal for determining a model accuracy. The apparatus(es) 102 will be discussed later in further detail with reference to Fig. 2, according to an embodiment of the invention.

[0048] The device(s) 104 can, for example, be associated with / 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 electronic device / module having computing capabilities) which can, for example, be configuredto 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 invention. This will be discussed later in further detail in the context of an example scenario, in accordance with an embodiment of the invention.

[0049] 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.

[0050] As 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 invention. Accordingly, the device(s) 104 can pre-determine the model quality threshold range (MQRP) and also communicate the MQRP to the apparatus(es) 102. 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, hereinafter.

[0051] Fig. 1 B shows an example scenario in association with the system of Fig. 1A, according to an embodiment of the invention. Specifically, Fig. 1 B shows an example of a functional framework for Artificial Intelligence I Machine Learning (AI / ML) New Radio Air Interface. As shown in the Figure, a data collection module can output data such as training data, monitoring data and inference data to various modules, for example a model training module, a network management module and an inference module.

[0052] The network management module can receive the monitoring data from the data collection module and inference output from the inference module and delivers or communicates a performance feedback or retraining request to the model training module. The network management module can also communicate management instructions to the inference module and a model transfer or delivery request to a model storage module.

[0053] The inference module can receive the inference data from the data collection module, the management instruction from the network management module and a model transfer or delivery from the model storage module to process and generate the inference output to the network management module.

[0054] The model training module receive the training data from the data collection module and the performance feedback or retraining request from the network management module to process and generate a trained or update model to the model storage module.

[0055] The model storage module can receive the model transfer or delivery request from the network management module and the trained or update model from the model training module to process and generate the model transfer or delivery to the inference module.

[0056] 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 apparatus 102 of the present invention. Likewise, all below described aspect(s) of the apparatus 102 of the invention can also apply analogously (all) the aspect(s) of above-described system 100 of the invention.

[0057] The aforementioned apparatus(es) 102 or User Equipment (UE) will be discussed in further detail with reference to Fig. 2 hereinafter.

[0058] Referring to Fig. 2, a schematic diagram illustrating an apparatus 102 is shown in further detail in the context of an example implementation 200, according to an embodiment of the invention.

[0059] 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 invention. 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 invention. 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).

[0060] 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.

[0061] 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.

[0062] 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.

[0063] 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.

[0064] 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.

[0065] In one example, the first module 202 can correspond to a hardware-based receiver which can be configured to receive one or more input signals. The input signal(s) can, for example, be communicated from the device(s) 104 (or base station e.g., a gNB), in accordance with an embodiment of the invention.

[0066] 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.

[0067] 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 determine a model accuracy.

[0068] 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.

[0069] The UE can, for example, be further configured to process the input signal(s), 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 determine a model accuracy.

[0070] The above-described aspect(s) of the apparatus 102 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 apparatus 102 of the invention. It is to be appreciated that these remarks apply analogously to the earlier discussed system 100 of the present disclosure.

[0071] Referring to Fig. 3, a method 300 (or a communication method) for determining a model accuracy, 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.

[0072] 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.

[0073] 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.

[0074] 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).

[0075] 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 invention.

[0076] The input step 302 can include receiving at least one input signal associated with a model quality threshold range. The model quality threshold range can be associated with a model quality parameter range (MQRP) which can include at least one of: time range, inference accuracy range and / or number of datapoints in a dataset. The model quality threshold range (or MQRP) may be pre-determined by at least one base station (or device 104). The at least one base station may correspond to at least one Next Generation Node B (gNB) which may communicate the model quality threshold range to the apparatus 102 (or UE). The model quality threshold range may be received by the UE from the gNB via at least one of System Information Block (SIB), Master Information Block (MIB) or UE specific message (e.g., Radio Resource Control (RRC) reconfiguration).

[0077] With regard to the processing step 304, at least a 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 invention.

[0078] The processing step 304 may include at least one of: obtaining a model quality performance value based on derived inference of a current model; and determining an accuracy of the current model by analyzing the model quality performance value with the model quality threshold range. The processing step 304 may further include requesting at least one model parameter update if the model quality performance value is not within the model quality threshold range; and configuring the model based on the model quality performance value if the model quality performance value is within the threshold range.

[0079] The processing step 304 may further include determining at least one updated model parameter; communicating the at least one updated model parameter; and configuring the model based on the at least one updated model parameter. In an example embodiment, the processing step 304 may be performed by the apparatus 102 (UE) as well as the device 104 (base station or gNB).

[0080] 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 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 invention. In an embodiment, the apparatus 102 (or UE) may also perform the input step 302, the processing step 304 and the output step 306.

[0081] 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.

[0082] 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.

[0083] Further in view of the foregoing, it is appreciable that the present disclosure generally contemplates an apparatus 102 for determining a model accuracy which can include a first module 202, a second module 204 and / or a third module 206.

[0084] The first module 202 can be configured to receive one or more input signals.The input signal(s) can, for example, be associated a model quality threshold range.

[0085] 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.

[0086] 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 requesting at least one model parameter update if the model quality performance value is not within the model quality threshold range and configuring the model based on the model quality performance value if the model quality performance value is within the threshold range.

[0087] 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.

[0088] 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.

[0089] 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).

[0090] 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.

[0091] 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.

[0092] 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.

[0093] Fig. 4A to Fig. 4C show schematic diagrams illustrating the flow of information in association with the method of Fig. 3, according to an embodiment of the invention.

[0094] In the example context as shown in Fig. 4A, a gNB (or base station) can, for example, be configured to generate / define / (pre)configure / set AI / ML model accuracy threshold (e.g. model quality threshold range) for the UE and / or communicate one or more input signals which can correspond to or be associated with or include the generated / defined / (pre)configured / set model quality threshold range(s), in accordance with an embodiment of the invention. Moreover, the gNB (or base station) can, for example, be configured to provide or generate a model parameter update for the UE, in accordance with an embodiment of the invention.

[0095] In the example context as shown in Fig. 4B, a UE can, for example, be configured to receive a model quality parameter range (or model quality threshold range). Subsequently, the UE determines a derived inference accuracy by obtaining a model quality performance value based on derived inference of a current model and analyzing the model quality performance value with the model quality parameter range (or model quality threshold range). If the model quality performance value is not within the model quality parameter range (or model quality threshold range), theUE may request the gNB (or base station) for at least one model parameter update. On the other hand, if the model quality performance value is within the model quality parameter range (or model quality threshold range), the UE may use the derived inference by configuring the model based on the model quality performance value.

[0096] In the example context as shown in Fig. 4C, a User Equipment (UE) can be configured to receive one or more input signal(s) communicable from the gNB (or base station) at step 1. The gNB can, for example, predetermine or configure a model quality parameter range (or model quality threshold range) and the UE can be configured to process the input signal(s). The input signal can include or associated with the model quality parameter range (or model quality threshold range). At step 2, the UE can, for example, be configured to determine (e.g., detect or measure) whether a model quality is satisfied by analyzing the model quality parameter range (or model quality threshold range) with a model quality performance value that is based on derived inference of a current model. If the model quality is not satisfied or if the model quality performance value is not within the model quality parameter range (or model quality threshold range), the UE may request the gNB (or base station) for a model (or a model parameter) update at step 3. Upon receiving the request, the gNB (or base station) provides the model update response to the UE at step 4. At step 5, the UE updates the model parameters using the updated model from the gNB (or base station).

[0097] 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 determining a model accuracy, the method comprising: an input step (302) which comprises receiving at least one input signal associated with a model quality threshold range; and a processing step (304) which comprises at least one of: obtaining a model quality performance value based on derived inference of a current model; and determining an accuracy of the current model by analyzing the model quality performance value with the model quality threshold range.

2. The method (300) according to claim 1 , the processing step (304) further comprising: requesting at least one model parameter update if the model quality performance value is not within the model quality threshold range; and configuring the model based on the model quality performance value if the model quality performance value is within the threshold range.

3. The method (300) according to claim 1 , the processing step (304) further comprising: determining at least one updated model parameter; communicating the at least one updated model parameter; and configuring the model based on the at least one updated model parameter.

4. The method (300) according to claim 1 , wherein the model quality threshold range is associated with a model quality parameter range (MQRP).

5. The method (300) according to claim 4, wherein the model quality parameter range (MQRP) comprises at least one of: time range, inference accuracy range and / or number of datapoints in a dataset.

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

7. The method (300) according to claim 1 , wherein at least one base station is configured to: pre-determine the model quality threshold range; and communicate the model quality threshold range.

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

9. The method (300) according to claim 8, wherein a User Equipment (UE) is configured to perform the input step (302) and the processing step (304), and wherein the model quality threshold range is communicable from the gNB to the UE.

10. The method (300) according to claim 9, wherein the model quality threshold range is received by the UE from the gNB via at least one of: System Information Block (SIB), Master Information Block (MIB) or UE specific message.

11. A computer program comprising instructions which, when the program is executed by a computer, cause the computer to carry out at least one of the at least one of the input step (302) and the processing step (304) according to the method (300) of 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 at least one of the input step (302) and the processing step (304) according to the method (300) of claims 1-10.

13. An apparatus (102) for determining a model accuracy comprising: a first module (202) configured to receive at least one input signal associated with a model quality threshold range; a second module (204) configured to at least one of process and facilitate the processing step (304) according to the method (300) of claim 1 to claim 10 to generate at least one output signal; anda third module (206) configured to communicate at least one output signal, wherein the output signal corresponds to a control signal for requesting at least one model parameter update if the model quality performance value is not within the model quality threshold range and configuring the model based on the model quality performance value if the model quality performance value is within the threshold range.

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

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